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Jiang W, Zhou H, Wu J, Chen H, Li L, Wu Y, Meng T, Zuo G, Fan W, Shi C. Short Step Length Estimation for Parkinson's Disease Patients by Using Fusion Data From Camera-IMU in Smart Glasses. IEEE Trans Biomed Eng 2024; 71:2265-2275. [PMID: 38376981 DOI: 10.1109/tbme.2024.3367923] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/22/2024]
Abstract
Shortened step length is a prominent motor abnormality in Parkinson's disease (PD) patients. Current methods for estimating short step length have the limitation of relying on laboratory scenarios, wearing multiple sensors, and inaccurate estimation results from a single sensor. In this paper, we proposed a novel method for estimating short step length for PD patients by fusing data from camera and inertial measurement units in smart glasses. A simultaneous localization and mapping technique and acceleration thresholding-based step detection technique were combined to realize the step length estimation. Two sets of experiments were conducted to demonstrate the performance of our method. In the first set of experiments with 12 healthy subjects, the proposed method demonstrated an average error of 8.44% across all experiments including six fixed step lengths below 30 cm. The second set of straightly walking experiments were implemented with 12 PD patients, the proposed method exhibited an average error of 4.27% compared to a standard gait evaluation technique in total walking distance. Notably, among the results of step lengths below 40 cm, our method agreed with the standard technique (R 2=0.8659). This study offers a promising approach for estimating short step length for PD patients during smart glasses-based gait training.
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Brambilla C, Scano A. Kinematic synergies show good consistency when extracted with a low-cost markerless device and a marker-based motion tracking system. Heliyon 2024; 10:e32042. [PMID: 38882310 PMCID: PMC11176860 DOI: 10.1016/j.heliyon.2024.e32042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Revised: 05/23/2024] [Accepted: 05/27/2024] [Indexed: 06/18/2024] Open
Abstract
Recently, markerless tracking systems, such as RGB-Depth cameras, have spread to overcome some of the limitations of the gold standard marker-based tracking systems. Although these systems are valuable substitutes for human motion analysis, as they guarantee higher flexibility, faster setup time and lower costs, their tracking accuracy is lower with respect to marker-based systems. Many studies quantified the error made by markerless systems in terms of body segment length estimation, articular angles, and biomechanics, concluding that they are appropriate for many clinical applications related to motion analysis. We propose an innovative approach to compare a markerless tracking system (Kinect V2) with a gold standard marker-based system (Vicon), based on motor control assessment. We quantified kinematic synergies from the tracking data of fifteen participants performing multi-directional upper limb movements. Kinematic synergy analysis decomposes the kinematic data into a reduced set of motor primitives that describe how the central nervous system coordinates motion at spatial and temporal level. Synergies were extracted with the recently released mixed-matrix factorization algorithm. Four synergies were extracted from both marker-based and markerless datasets and synergies were grouped in 6 clusters for each dataset. Cosine similarity in each cluster was ⩾0.60 in both systems, showing good consistency of synergies. Good matching was found between synergies extracted from markerless and from marker-based data, with a cosine similarity between matched synergies ⩾0.60 in 5 out 6 synergies. These results showed that the markerless sensor can be feasible for kinematic synergy analysis for gross movements, as it correctly estimates the number of synergies and in most cases also their spatial and temporal organization.
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Affiliation(s)
- Cristina Brambilla
- Institute of Intelligent Industrial Systems and Technologies for Advanced Manufacturing (STIIMA), Italian Council of National Research (CNR), Milano, Italy
| | - Alessandro Scano
- Institute of Intelligent Industrial Systems and Technologies for Advanced Manufacturing (STIIMA), Italian Council of National Research (CNR), Milano, Italy
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Suzuki M, Hirano S, Otte K, Schmitz-Hübsch T, Izumi M, Tamura M, Kuroiwa R, Sugiyama A, Mori M, Röhling HM, Brandt AU, Murata A, Paul F, Kuwabara S. Digital Motor Biomarkers of Cerebellar Ataxia Using an RGB-Depth Camera-Based Motion Analysis System. CEREBELLUM (LONDON, ENGLAND) 2024; 23:1031-1041. [PMID: 37721679 DOI: 10.1007/s12311-023-01604-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 09/01/2023] [Indexed: 09/19/2023]
Abstract
This study aimed to identify quantitative biomarkers of motor function for cerebellar ataxia by evaluating gait and postural control using an RGB-depth camera-based motion analysis system. In 28 patients with degenerative cerebellar ataxia and 33 age- and sex-matched healthy controls, motor tasks (short-distance walk, closed feet stance, and stepping in place) were selected from a previously reported protocol, and scanned using Kinect V2 and customized software. The Clinical Assessment Scale for the Assessment and Rating of Ataxia (SARA) was also evaluated. Compared with the normal control group, the cerebellar ataxia group had slower gait speed and shorter step lengths, increased step width, and mediolateral trunk sway in the walk test (all P < 0.001). Lateral sway increased in the stance test in the ataxia group (P < 0.001). When stepping in place, the ataxia group showed higher arrhythmicity of stepping and increased stance time (P < 0.001). In the correlation analyses, the ataxia group showed a positive correlation between the total SARA score and arrhythmicity of stepping in place (r = 0.587, P = 0.001). SARA total score (r = 0.561, P = 0.002) and gait subscore (ρ = 0.556, P = 0.002) correlated with mediolateral truncal sway during walking. These results suggest that the RGB-depth camera-based motion analyses on mediolateral truncal sway during walking and arrhythmicity of stepping in place are useful digital motor biomarkers for the assessment of cerebellar ataxia, and could be utilized in future clinical trials.
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Affiliation(s)
- Masahide Suzuki
- Department of Neurology, Graduate School of Medicine, Chiba University, 1-8-1 Inohana, Chuo-Ku, Chiba-Shi, Chiba, 260-8670, Japan
| | - Shigeki Hirano
- Department of Neurology, Graduate School of Medicine, Chiba University, 1-8-1 Inohana, Chuo-Ku, Chiba-Shi, Chiba, 260-8670, Japan.
- Department of Functional Brain Imaging Research, Institute for Quantum Medical Science, National Institute for Quantum Science and Technology, Chiba, Japan.
| | - Karen Otte
- Experimental and Clinical Research Center, a cooperation of Max Delbrueck Center for Molecular Medicine and Charité - Universitätsmedizin Berlin, Berlin, Germany
- Motognosis GmbH, Berlin, Germany
| | - Tanja Schmitz-Hübsch
- Experimental and Clinical Research Center, a cooperation of Max Delbrueck Center for Molecular Medicine and Charité - Universitätsmedizin Berlin, Berlin, Germany
- Neuroscience Clinical Research Center, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität Zu Berlin, Berlin, Germany
| | - Michiko Izumi
- Department of Neurology, Graduate School of Medicine, Chiba University, 1-8-1 Inohana, Chuo-Ku, Chiba-Shi, Chiba, 260-8670, Japan
| | - Mitsuyoshi Tamura
- Department of Neurology, Graduate School of Medicine, Chiba University, 1-8-1 Inohana, Chuo-Ku, Chiba-Shi, Chiba, 260-8670, Japan
- Department of Functional Brain Imaging Research, Institute for Quantum Medical Science, National Institute for Quantum Science and Technology, Chiba, Japan
| | - Ryota Kuroiwa
- Division of Rehabilitation Medicine, Chiba University Hospital, Chiba, Japan
| | - Atsuhiko Sugiyama
- Department of Neurology, Graduate School of Medicine, Chiba University, 1-8-1 Inohana, Chuo-Ku, Chiba-Shi, Chiba, 260-8670, Japan
| | - Masahiro Mori
- Department of Neurology, Graduate School of Medicine, Chiba University, 1-8-1 Inohana, Chuo-Ku, Chiba-Shi, Chiba, 260-8670, Japan
| | - Hanna M Röhling
- Experimental and Clinical Research Center, a cooperation of Max Delbrueck Center for Molecular Medicine and Charité - Universitätsmedizin Berlin, Berlin, Germany
- Motognosis GmbH, Berlin, Germany
| | - Alexander U Brandt
- Experimental and Clinical Research Center, a cooperation of Max Delbrueck Center for Molecular Medicine and Charité - Universitätsmedizin Berlin, Berlin, Germany
- Neuroscience Clinical Research Center, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität Zu Berlin, Berlin, Germany
| | - Atsushi Murata
- Division of Rehabilitation Medicine, Chiba University Hospital, Chiba, Japan
| | - Friedemann Paul
- Experimental and Clinical Research Center, a cooperation of Max Delbrueck Center for Molecular Medicine and Charité - Universitätsmedizin Berlin, Berlin, Germany
- Neuroscience Clinical Research Center, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität Zu Berlin, Berlin, Germany
- Department of Neurology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität Zu Berlin, Berlin, Germany
| | - Satoshi Kuwabara
- Department of Neurology, Graduate School of Medicine, Chiba University, 1-8-1 Inohana, Chuo-Ku, Chiba-Shi, Chiba, 260-8670, Japan
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Mao Q, Zhang J, Yu L, Zhao Y, Luximon Y, Wang H. Effectiveness of sensor-based interventions in improving gait and balance performance in older adults: systematic review and meta-analysis of randomized controlled trials. J Neuroeng Rehabil 2024; 21:85. [PMID: 38807117 PMCID: PMC11131332 DOI: 10.1186/s12984-024-01375-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Accepted: 05/10/2024] [Indexed: 05/30/2024] Open
Abstract
BACKGROUND Sensor-based interventions (SI) have been suggested as an alternative rehabilitation treatment to improve older adults' functional performance. However, the effectiveness of different sensor technologies in improving gait and balance remains unclear and requires further investigation. METHODS Ten databases (Academic Search Premier; Cumulative Index to Nursing and Allied Health Literature, Complete; Cochrane Central Register of Controlled Trials; MEDLINE; PubMed; Web of Science; OpenDissertations; Open grey; ProQuest; and Grey literature report) were searched for relevant articles published up to December 20, 2022. Conventional functional assessments, including the Timed Up and Go (TUG) test, normal gait speed, Berg Balance Scale (BBS), 6-Minute Walk Test (6MWT), and Falling Efficacy Scale-International (FES-I), were used as the evaluation outcomes reflecting gait and balance performance. We first meta-analyzed the effectiveness of SI, which included optical sensors (OPTS), perception sensors (PCPS), and wearable sensors (WS), compared with control groups, which included non-treatment intervention (NTI) and traditional physical exercise intervention (TPEI). We further conducted sub-group analysis to compare the effectiveness of SI (OPTS, PCPS, and WS) with TPEI groups and compared each SI subtype with control (NTI and TPEI) and TPEI groups. RESULTS We scanned 6255 articles and performed meta-analyses of 58 selected trials (sample size = 2713). The results showed that SI groups were significantly more effective than control or TPEI groups (p < 0.000) in improving gait and balance performance. The subgroup meta-analyses between OPTS groups and TPEI groups revealed clear statistically significant differences in effectiveness for TUG test (mean difference (MD) = - 0.681 s; p < 0.000), normal gait speed (MD = 4.244 cm/s; p < 0.000), BBS (MD = 2.325; p = 0.001), 6MWT (MD = 25.166 m; p < 0.000), and FES-I scores (MD = - 2.036; p = 0.036). PCPS groups also presented statistically significant differences with TPEI groups in gait and balance assessments for normal gait speed (MD = 4.382 cm/s; p = 0.034), BBS (MD = 1.874; p < 0.000), 6MWT (MD = 21.904 m; p < 0.000), and FES-I scores (MD = - 1.161; p < 0.000), except for the TUG test (MD = - 0.226 s; p = 0.106). There were no statistically significant differences in TUG test (MD = - 1.255 s; p = 0.101) or normal gait speed (MD = 6.682 cm/s; p = 0.109) between WS groups and control groups. CONCLUSIONS SI with biofeedback has a positive effect on gait and balance improvement among a mixed population of older adults. Specifically, OPTS and PCPS groups were statistically better than TPEI groups at improving gait and balance performance, whereas only the group comparison in BBS and 6MWT can reach the minimal clinically important difference. Moreover, WS groups showed no statistically or clinically significant positive effect on gait and balance improvement compared with control groups. More studies are recommended to verify the effectiveness of specific SI. Research registration PROSPERO platform: CRD42022362817. Registered on 7/10/2022.
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Affiliation(s)
- Qian Mao
- School of Design, The Hong Kong Polytechnic University, Hong Kong, China
| | - Jiaxin Zhang
- School of System Design and Intelligent Manufacturing, Southern University of Science and Technology, Shenzhen, China
| | - Lisha Yu
- School of Data Science, Lingnan University, Hong Kong, China
| | - Yang Zhao
- School of Public Health (Shenzhen), Sun Yat-Sen University, Shenzhen, China
| | - Yan Luximon
- School of Design, The Hong Kong Polytechnic University, Hong Kong, China
| | - Hailiang Wang
- School of Design, The Hong Kong Polytechnic University, Hong Kong, China.
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Sarpourian F, Sharifian R, Poursadeghfard M, Khayami SR, Erfannia L. Comparison of the Clinical Effectiveness of Telerehabilitation with Traditional Rehabilitation Methods in Multiple Sclerosis Patients: A Systematic Review. Telemed J E Health 2024. [PMID: 38739448 DOI: 10.1089/tmj.2023.0412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/16/2024] Open
Abstract
Background: The rehabilitation process for multiple sclerosis (MS) patients is long and complex, which can lead to reduced rehabilitation outcomes and reduced quality improvement. Thus, there is a need to use new methods to boost traditional rehabilitation. Innovations such as telerehabilitation can be helpful to remove the obstacles to treatment, but evidence for their effectiveness is limited. The objective of this work was to compare the clinical effectiveness of telerehabilitation with traditional interventions in MS patients. Methods: Seven bibliographic databases (PubMed, Cochran Library, Scopus, Science Direct, Web of Science, Embase, and ProQuest) were used in this research. The initial search resulted in the extraction of 8,239 articles; after the review of the title, abstract, and full text, 11 articles were selected. In addition, backward reference list checking of the selected studies was conducted. Studies that were related to our objectives were included. Quality assessment was performed using the CONSORT checklist. Then, data extraction was done using the form set by the researcher in Word 2016 software. Results: Overall, telerehabilitation achieved more positive effects compared to traditional rehabilitation on physical (n = 6), cognitive (n = 3), cognitive, and physical outcomes (n = 2), respectively. Synchronous telerehabilitation was more effective than other modalities (n = 8). In four studies, virtual reality-based telerehabilitation was used. Also, telerehabilitation in home offered better clinical outcomes compared to rehabilitation center (n = 9). Conclusions: This review provides evidence for the potential effectiveness of telerehabilitation for the improvement of clinical outcomes in MS patients. However, more robust randomized controlled trials are needed to confirm the observed positive effects.
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Affiliation(s)
- Fatemeh Sarpourian
- Department of Health Information Management, School of Health Management and Information Sciences, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Roxana Sharifian
- Department of Health Information Management, School of Health Management and Information Sciences, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Maryam Poursadeghfard
- Department of Neurology, Clinical Neurology Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Seyed Raouf Khayami
- Department of Computer Engineering and Information Technology, Shiraz University of Technology, Shiraz, Iran
| | - Leila Erfannia
- Department of Health Information Management, School of Health Management and Information Sciences, Shiraz University of Medical Sciences, Shiraz, Iran
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Furtado S, Galna B, Godfrey A, Rochester L, Gerrand C. Feasibility of using low-cost markerless motion capture for assessing functional outcomes after lower extremity musculoskeletal cancer surgery. PLoS One 2024; 19:e0300351. [PMID: 38547229 PMCID: PMC10977781 DOI: 10.1371/journal.pone.0300351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Accepted: 02/26/2024] [Indexed: 04/02/2024] Open
Abstract
BACKGROUND Physical limitations are frequent and debilitating after sarcoma treatment. Markerless motion capture (MMC) could measure these limitations. Historically expensive cumbersome systems have posed barriers to clinical translation. RESEARCH QUESTION Can inexpensive MMC [using Microsoft KinectTM] assess functional outcomes after sarcoma surgery, discriminate between tumour sub-groups and agree with existing assessments? METHODS Walking, unilateral stance and kneeling were measured in a cross-sectional study of patients with lower extremity sarcomas using MMC and standard video. Summary measures of temporal, balance, gait and movement velocity were derived. Feasibility and early indicators of validity of MMC were explored by comparing MMC measures i) between tumour sub-groups; ii) against video and iii) with established sarcoma tools [Toronto Extremity Salvage Score (TESS)), Musculoskeletal Tumour Rating System (MSTS), Quality of life-cancer survivors (QoL-CS)]. Statistical analysis was conducted using SPSS v19. Tumour sub-groups were compared using Mann-Whitney U tests, MMC was compared to existing sarcoma measures using correlations and with video using Intraclass correlation coefficient agreement. RESULTS Thirty-four adults of mean age 43 (minimum value-maximum value 19-89) years with musculoskeletal tumours in the femur (19), pelvis/hip (3), tibia (9), or ankle/foot (3) participated; 27 had limb sparing surgery and 7 amputation. MMC was well-tolerated and feasible to deliver. MMC discriminated between surgery groups for balance (p<0.05*), agreed with video for kneeling times [ICC = 0.742; p = 0.001*] and showed moderate relationships between MSTS and gait (p = 0.022*, r = -0.416); TESS and temporal outcomes (p = 0.016* and r = -0.0557*), movement velocity (p = 0.021*, r = -0.541); QoL-CS and balance (p = 0.027*, r = 0.441) [* = statistical significance]. As MMC uncovered important relationships between outcomes, it gave an insight into how functional impairments, balance, gait, disabilities and quality of life (QoL) are associated with each other. This gives an insight into mechanisms of poor outcomes, producing clinically useful data i.e. data which can inform clinical practice and guide the delivery of targeted rehabilitation. For example, patients presenting with poor balance in various activities can be prescribed with balance rehabilitation and those with difficulty in movements or activity transitions can be managed with exercises and training to improve the quality and efficiency of the movement. SIGNIFICANCE In this first study world-wide, investigating the use of MMC after sarcoma surgery, MMC was found to be acceptable and feasible to assess functional outcomes in this cancer population. MMC demonstrated early indicators of validity and also provided new knowledge that functional impairments are related to balance during unilateral stance and kneeling, gait and movement velocity during kneeling and these outcomes in turn are related to disabilities and QoL. This highlighted important relationships between different functional outcomes and QoL, providing valuable information for delivering personalised rehabilitation. After completing future validation work in a larger study, this approach can offer promise in clinical settings. Low-cost MMC shows promise in assessing patient's impairments in the hospitals or their homes and guiding clinical management and targeted rehabilitation based on novel MMC outcomes affected, therefore providing an opportunity for delivering personalised exercise programmes and physiotherapy care delivery for this rare cancer.
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Affiliation(s)
- Sherron Furtado
- Department of Orthopaedics and Musculoskeletal Science, University College London, London, United Kingdom
- Therapies and Department of Orthopaedic Oncology, London Sarcoma Service, Royal National Orthopaedic Hospital NHS Trust, Stanmore, United Kingdom
| | - Brook Galna
- School of Allied Health (Exercise Science), Murdoch University, Perth, Australia
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Alan Godfrey
- Computer and Information Science Department, Northumbria University, Newcastle upon Tyne, United Kingdom
| | - Lynn Rochester
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, United Kingdom
- Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, United Kingdom
| | - Craig Gerrand
- Department of Orthopaedic Oncology, The London Sarcoma Service, Royal National Orthopaedic Hospital NHS Trust, Stanmore, United Kingdom
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Park KW, Mirian MS, McKeown MJ. Artificial intelligence-based video monitoring of movement disorders in the elderly: a review on current and future landscapes. Singapore Med J 2024; 65:141-149. [PMID: 38527298 PMCID: PMC11060643 DOI: 10.4103/singaporemedj.smj-2023-189] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Accepted: 12/19/2023] [Indexed: 03/27/2024]
Abstract
ABSTRACT Due to global ageing, the burden of chronic movement and neurological disorders (Parkinson's disease and essential tremor) is rapidly increasing. Current diagnosis and monitoring of these disorders rely largely on face-to-face assessments utilising clinical rating scales, which are semi-subjective and time-consuming. To address these challenges, the utilisation of artificial intelligence (AI) has emerged. This review explores the advantages and challenges associated with using AI-driven video monitoring to care for elderly patients with movement disorders. The AI-based video monitoring systems offer improved efficiency and objectivity in remote patient monitoring, enabling real-time analysis of data, more uniform outcomes and augmented support for clinical trials. However, challenges, such as video quality, privacy compliance and noisy training labels, during development need to be addressed. Ultimately, the advancement of video monitoring for movement disorders is expected to evolve towards discreet, home-based evaluations during routine daily activities. This progression must incorporate data security, ethical considerations and adherence to regulatory standards.
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Affiliation(s)
- Kye Won Park
- Pacific Parkinson Research Centre, University of British Columbia, Vancouver, British Columbia, Canada
| | - Maryam S Mirian
- Pacific Parkinson Research Centre, University of British Columbia, Vancouver, British Columbia, Canada
| | - Martin J McKeown
- Pacific Parkinson Research Centre, University of British Columbia, Vancouver, British Columbia, Canada
- Department of Medicine (Neurology), University of British Columbia, Vancouver, British Columbia, Canada
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Guo C, Liang Y, Xu S, Zheng B, Liu H. Lasso Analysis of Gait Characteristics and Correlation with Spinopelvic Parameters in Patients with Degenerative Lumbar Scoliosis. J Pers Med 2023; 13:1576. [PMID: 38003891 PMCID: PMC10671873 DOI: 10.3390/jpm13111576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Revised: 10/24/2023] [Accepted: 10/25/2023] [Indexed: 11/26/2023] Open
Abstract
PURPOSE This study quantifies the gait characteristics of patients with degenerative lumbar scoliosis (DLS) and patients with simple lumbar spinal stenosis (LSS) by means of a three-dimensional gait analysis system, aiming to determine the image of spinal deformity on gait and the correlation between spinal-pelvic parameters and gait characteristics in patients with DLS to assist clinical work. METHODS From June 2020 to December 2021, a total of 50 subjects were enrolled in this study, of whom 20 patients with DLS served as the case group and 30 middle-aged and elderly patients with LSS were selected as the control group according to the general conditions (sex, age, and BMI) of the case group. Spinal-pelvic parameters were measured by full-length frontal and lateral spine films one week before surgery, and kinematics were recorded on the same day using a gait analysis system. RESULTS Compared to the control group, DLS patients exhibited significantly reduced velocity and cadence; gait variability and symmetry of both lower limbs were notably better in the LSS group than in the DLS group; joint ROM (range of motion) across multiple dimensions was also lower in the DLS group; and correlation analysis revealed that patients with a larger Cobb angle, T1PA, and higher CSVA tended to walk more slowly, and those with a larger PI, PT, and LL usually had smaller stride lengths. The greater the PI-SS mismatch, the longer the patient stayed in the support phase. Furthermore, a larger Cobb angle correlated with worse coronal hip mobility. CONCLUSIONS DLS patients demonstrate distinctive gait abnormalities and reduced hip mobility compared to LSS patients. Significant correlations between crucial spinopelvic parameters and these gait changes underline their potential influence on gait disturbances in DLS. Our study identifies a Cobb angle cut-off of 16.1 as a key predictor for gait abnormalities. These insights can guide personalized treatment and intervention strategies, ultimately improving the quality of life for DLS patients.
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Affiliation(s)
- Chen Guo
- Department of Spinal Surgery, Peking University People's Hospital, Peking University, Beijing 100044, China
| | - Yan Liang
- Department of Spinal Surgery, Peking University People's Hospital, Peking University, Beijing 100044, China
| | - Shuai Xu
- Department of Spinal Surgery, Peking University People's Hospital, Peking University, Beijing 100044, China
| | - Bin Zheng
- Department of Spinal Surgery, Peking University People's Hospital, Peking University, Beijing 100044, China
| | - Haiying Liu
- Department of Spinal Surgery, Peking University People's Hospital, Peking University, Beijing 100044, China
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Moreau C, Rouaud T, Grabli D, Benatru I, Remy P, Marques AR, Drapier S, Mariani LL, Roze E, Devos D, Dupont G, Bereau M, Fabbri M. Overview on wearable sensors for the management of Parkinson's disease. NPJ Parkinsons Dis 2023; 9:153. [PMID: 37919332 PMCID: PMC10622581 DOI: 10.1038/s41531-023-00585-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Accepted: 10/02/2023] [Indexed: 11/04/2023] Open
Abstract
Parkinson's disease (PD) is affecting about 1.2 million patients in Europe with a prevalence that is expected to have an exponential increment, in the next decades. This epidemiological evolution will be challenged by the low number of neurologists able to deliver expert care for PD. As PD is better recognized, there is an increasing demand from patients for rigorous control of their symptoms and for therapeutic education. In addition, the highly variable nature of symtoms between patients and the fluctuations within the same patient requires innovative tools to help doctors and patients monitor the disease in their usual living environment and adapt treatment in a more relevant way. Nowadays, there are various body-worn sensors (BWS) proposed to monitor parkinsonian clinical features, such as motor fluctuations, dyskinesia, tremor, bradykinesia, freezing of gait (FoG) or gait disturbances. BWS have been used as add-on tool for patients' management or research purpose. Here, we propose a practical anthology, summarizing the characteristics of the most used BWS for PD patients in Europe, focusing on their role as tools to improve treatment management. Consideration regarding the use of technology to monitor non-motor features is also included. BWS obviously offer new opportunities for improving management strategy in PD but their precise scope of use in daily routine care should be clarified.
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Affiliation(s)
- Caroline Moreau
- Department of Neurology, Parkinson's disease expert Center, Lille University, INSERM UMRS_1172, University Hospital Center, Lille, France
- The French Ns-Park Network, Paris, France
| | - Tiphaine Rouaud
- The French Ns-Park Network, Paris, France
- CHU Nantes, Centre Expert Parkinson, Department of Neurology, Nantes, F-44093, France
| | - David Grabli
- The French Ns-Park Network, Paris, France
- Assistance Publique Hôpitaux de Paris, Department of Neurology, CIC Neurosciences, Pitié-Salpêtrière Hospital, Sorbonne University, Paris, France
- Sorbonne University, Paris Brain Institute - ICM, Inserm, CNRS, Paris, France
| | - Isabelle Benatru
- The French Ns-Park Network, Paris, France
- Department of Neurology, University Hospital of Poitiers, Poitiers, France
- INSERM, CHU de Poitiers, University of Poitiers, Centre d'Investigation Clinique CIC1402, Poitiers, France
| | - Philippe Remy
- The French Ns-Park Network, Paris, France
- Centre Expert Parkinson, NS-Park/FCRIN Network, CHU Henri Mondor, AP-HP, Equipe NPI, IMRB, INSERM et Faculté de Santé UPE-C, Créteil, FranceService de neurologie, hôpital Henri-Mondor, AP-HP, Créteil, France
| | - Ana-Raquel Marques
- The French Ns-Park Network, Paris, France
- Université Clermont Auvergne, CNRS, Clermont Auvergne INP, Institut Pascal, Clermont-Ferrand University Hospital, Neurology department, Clermont-Ferrand, France
| | - Sophie Drapier
- The French Ns-Park Network, Paris, France
- Pontchaillou University Hospital, Department of Neurology, CIC INSERM 1414, Rennes, France
| | - Louise-Laure Mariani
- The French Ns-Park Network, Paris, France
- Assistance Publique Hôpitaux de Paris, Department of Neurology, CIC Neurosciences, Pitié-Salpêtrière Hospital, Sorbonne University, Paris, France
- Sorbonne University, Paris Brain Institute - ICM, Inserm, CNRS, Paris, France
| | - Emmanuel Roze
- The French Ns-Park Network, Paris, France
- Assistance Publique Hôpitaux de Paris, Department of Neurology, CIC Neurosciences, Pitié-Salpêtrière Hospital, Sorbonne University, Paris, France
- Sorbonne University, Paris Brain Institute - ICM, Inserm, CNRS, Paris, France
| | - David Devos
- The French Ns-Park Network, Paris, France
- Parkinson's Disease Centre of Excellence, Department of Medical Pharmacology, Univ. Lille, INSERM; CHU Lille, U1172 - Degenerative & Vascular Cognitive Disorders, LICEND, NS-Park Network, F-59000, Lille, France
| | - Gwendoline Dupont
- The French Ns-Park Network, Paris, France
- Centre hospitalier universitaire François Mitterrand, Département de Neurologie, Université de Bourgogne, Dijon, France
| | - Matthieu Bereau
- The French Ns-Park Network, Paris, France
- Service de neurologie, université de Franche-Comté, CHRU de Besançon, 25030, Besançon, France
| | - Margherita Fabbri
- The French Ns-Park Network, Paris, France.
- Department of Neurosciences, Clinical Investigation Center CIC 1436, Parkinson Toulouse Expert Centre, NS-Park/FCRIN Network and NeuroToul COEN Center, Toulouse University Hospital, INSERM, University of Toulouse 3, Toulouse, France.
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Brambilla C, Marani R, Romeo L, Lavit Nicora M, Storm FA, Reni G, Malosio M, D'Orazio T, Scano A. Azure Kinect performance evaluation for human motion and upper limb biomechanical analysis. Heliyon 2023; 9:e21606. [PMID: 38027881 PMCID: PMC10663858 DOI: 10.1016/j.heliyon.2023.e21606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 09/21/2023] [Accepted: 10/24/2023] [Indexed: 12/01/2023] Open
Abstract
Human motion tracking is a valuable task for many medical applications. Marker-based optoelectronic systems are considered the gold standard in human motion tracking. However, their use is not always feasible in clinics and industrial environments. On the other hand, marker-less sensors became valuable tools, as they are inexpensive, noninvasive and easy to use. However, their accuracy can depend on many factors including sensor positioning, light conditions and body occlusions. In this study, following previous works on the feasibility of marker-less systems for human motion monitoring, we investigate the performance of the Microsoft Azure Kinect sensor in computing kinematic and dynamic measurements of static postures and dynamic movements. According to our knowledge, it is the first time that this sensor is compared with a Vicon marker-based system to assess the best camera positioning while observing the upper body part movements of people performing several tasks. Twenty-five healthy volunteers were monitored to evaluate the effects of the several testing conditions, including the Azure Kinect positions, the light conditions, and lower limbs occlusions, on the tracking accuracy of kinematic, dynamic, and motor control parameters. From the statistical analysis of the performed measurements, the camera in the frontal position was the most reliable, the lighting conditions had almost no effects on the tracking accuracy, while the lower limbs occlusion worsened the accuracy of the upper limbs. The assessment of human static postures and dynamic movements based on experimental data proves the feasibility of applying the Azure Kinect to the biomechanical monitoring of human motion in several fields.
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Affiliation(s)
- Cristina Brambilla
- Institute of Intelligent Industrial Systems and Technologies for Advanced Manufacturing (STIIMA), Italian Council of National Research (CNR), Italy
| | - Roberto Marani
- Institute of Intelligent Industrial Systems and Technologies for Advanced Manufacturing (STIIMA), Italian Council of National Research (CNR), Italy
| | - Laura Romeo
- Institute of Intelligent Industrial Systems and Technologies for Advanced Manufacturing (STIIMA), Italian Council of National Research (CNR), Italy
- Department of Electrical and Information Engineering (DEI), Polytechnic of Bari, Bari, Italy
| | - Matteo Lavit Nicora
- Institute of Intelligent Industrial Systems and Technologies for Advanced Manufacturing (STIIMA), Italian Council of National Research (CNR), Italy
- Industrial Engineering Department, University of Bologna, Bologna, Italy
| | - Fabio A. Storm
- Bioengineering Laboratory, Scientific Institute, IRCCS “Eugenio Medea”, 23842 Bosisio Parini, Lecco, Italy
| | - Gianluigi Reni
- Informatics Department, Autonomous Province of Bolzano, Bolzano, Italy
| | - Matteo Malosio
- Institute of Intelligent Industrial Systems and Technologies for Advanced Manufacturing (STIIMA), Italian Council of National Research (CNR), Italy
| | - Tiziana D'Orazio
- Institute of Intelligent Industrial Systems and Technologies for Advanced Manufacturing (STIIMA), Italian Council of National Research (CNR), Italy
| | - Alessandro Scano
- Institute of Intelligent Industrial Systems and Technologies for Advanced Manufacturing (STIIMA), Italian Council of National Research (CNR), Italy
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11
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Dorsch EM, Röhling HM, Zocholl D, Hafermann L, Paul F, Schmitz-Hübsch T. Progression events defined by home-based assessment of motor function in multiple sclerosis: protocol of a prospective study. Front Neurol 2023; 14:1258635. [PMID: 37881311 PMCID: PMC10597627 DOI: 10.3389/fneur.2023.1258635] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 09/26/2023] [Indexed: 10/27/2023] Open
Abstract
Background This study relates to emerging concepts of appropriate trial designs to evaluate effects of intervention on the accumulation of irreversible disability in multiple sclerosis (MS). Major starting points of our study are the known limitations of current definitions of disability progression by rater-based clinical assessment and the high relevance of gait and balance dysfunctions in MS. The study aims to explore a novel definition of disease progression using repeated instrumental assessment of relevant motor functions performed by patients in their home setting. Methods The study is a prospective single-center observational cohort study with the primary outcome acquired by participants themselves, a home-based assessment of motor functions based on an RGB-Depth (RGB-D) camera, a camera that provides both depth (D) and color (RGB) data. Participants are instructed to perform and record a set of simple motor tasks twice a day over a one-week period every 6 months. Assessments are complemented by a set of questionnaires. Annual research grade assessments are acquired at dedicated study visits and include clinical ratings as well as structural imaging (MRI and optical coherence tomography). In addition, clinical data from routine visits is provided semiannually by treating neurologists. The observation period is 24 months for the primary endpoint with an additional clinical assessment at 27 month to confirm progression defined by the Expanded Disability Status Scale (EDSS). Secondary analyses aim to explore the time course of changes in motor parameters and performance of the novel definition against different alternative definitions of progression in MS. The study was registered at Deutsches Register für Klinische Studien (DRKS00027042). Discussion The study design presented here investigates disease progression defined by marker-less home-based assessment of motor functions against 3-month confirmed disease progression (3 m-CDP) defined by the EDSS. The technical approach was chosen due to previous experience in lab-based settings. The observation time per participant of 24, respectively, 27 months is commonly conceived as the lower limit needed to study disability progression. Defining a valid digital motor outcome for disease progression in MS may help to reduce observation times in clinical trials and add confidence to the detection of progression events in MS.
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Affiliation(s)
- Eva-Maria Dorsch
- Experimental and Clinical Research Center, a Cooperation between the Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association and the Charité—Universitätsmedizin Berlin, Berlin, Germany
- Experimental and Clinical Research Center, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
- Department of Neurology, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany
- Neuroscience Clinical Research Center, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany
| | - Hanna Marie Röhling
- Experimental and Clinical Research Center, a Cooperation between the Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association and the Charité—Universitätsmedizin Berlin, Berlin, Germany
- Experimental and Clinical Research Center, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
- Motognosis GmbH, Berlin, Germany
| | - Dario Zocholl
- Institute of Biometry and Clinical Epidemiology, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Lorena Hafermann
- Institute of Biometry and Clinical Epidemiology, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Friedemann Paul
- Experimental and Clinical Research Center, a Cooperation between the Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association and the Charité—Universitätsmedizin Berlin, Berlin, Germany
- Experimental and Clinical Research Center, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
- Department of Neurology, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany
- Neuroscience Clinical Research Center, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany
| | - Tanja Schmitz-Hübsch
- Experimental and Clinical Research Center, a Cooperation between the Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association and the Charité—Universitätsmedizin Berlin, Berlin, Germany
- Experimental and Clinical Research Center, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
- Neuroscience Clinical Research Center, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany
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12
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Lohss R, Odorizzi M, Sangeux M, Hasler CC, Viehweger E. Consequences of Virtual Reality Experience on Biomechanical Gait Parameters in Children with Cerebral Palsy: A Scoping Review. Dev Neurorehabil 2023; 26:377-388. [PMID: 37537745 DOI: 10.1080/17518423.2023.2242930] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 07/26/2023] [Indexed: 08/05/2023]
Abstract
Virtual reality (VR), coupled with motion tracking, can investigate walking in a controlled setting while applying various walking challenges. The purpose of this review was to summarize the evidence on consequences of VR on biomechanical gait parameters in children with cerebral palsy. MEDLINE, Embase and Web of Science were searched. Among 7.574 studies, screened by two independent reviewers, seven studies were included, analyzing treadmill (n = 6) or overground walking (n = 1) under VR. Most frequently reported were the spatiotemporal parameters walking speed, stride length, step width, stance phase, and the kinematic parameters range of knee flexion and peak ankle dorsiflexion. However, methodological approaches and reporting of the results were inconsistent among studies. This review reveals that VR can complement information gained from clinical gait analysis. However, this is still an emerging field of research and there is limited knowledge on the effect of VR on gait parameters, notably during overground walking.
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Affiliation(s)
- Regine Lohss
- Laboratory for Movement Analysis, University Children's Hospital Basel (UKBB), Basel, Switzerland
- Department of Biomedical Engineering, University of Basel, Allschwil, Switzerland
| | - Marco Odorizzi
- Laboratory for Movement Analysis, University Children's Hospital Basel (UKBB), Basel, Switzerland
| | - Morgan Sangeux
- Laboratory for Movement Analysis, University Children's Hospital Basel (UKBB), Basel, Switzerland
- Department of Biomedical Engineering, University of Basel, Allschwil, Switzerland
| | - Carol-Claudius Hasler
- Laboratory for Movement Analysis, University Children's Hospital Basel (UKBB), Basel, Switzerland
| | - Elke Viehweger
- Laboratory for Movement Analysis, University Children's Hospital Basel (UKBB), Basel, Switzerland
- Department of Biomedical Engineering, University of Basel, Allschwil, Switzerland
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13
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Iseki C, Hayasaka T, Yanagawa H, Komoriya Y, Kondo T, Hoshi M, Fukami T, Kobayashi Y, Ueda S, Kawamae K, Ishikawa M, Yamada S, Aoyagi Y, Ohta Y. Artificial Intelligence Distinguishes Pathological Gait: The Analysis of Markerless Motion Capture Gait Data Acquired by an iOS Application (TDPT-GT). SENSORS (BASEL, SWITZERLAND) 2023; 23:6217. [PMID: 37448065 DOI: 10.3390/s23136217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 06/22/2023] [Accepted: 07/04/2023] [Indexed: 07/15/2023]
Abstract
Distinguishing pathological gait is challenging in neurology because of the difficulty of capturing total body movement and its analysis. We aimed to obtain a convenient recording with an iPhone and establish an algorithm based on deep learning. From May 2021 to November 2022 at Yamagata University Hospital, Shiga University, and Takahata Town, patients with idiopathic normal pressure hydrocephalus (n = 48), Parkinson's disease (n = 21), and other neuromuscular diseases (n = 45) comprised the pathological gait group (n = 114), and the control group consisted of 160 healthy volunteers. iPhone application TDPT-GT captured the subjects walking in a circular path of about 1 meter in diameter, a markerless motion capture system, with an iPhone camera, which generated the three-axis 30 frames per second (fps) relative coordinates of 27 body points. A light gradient boosting machine (Light GBM) with stratified k-fold cross-validation (k = 5) was applied for gait collection for about 1 min per person. The median ability model tested 200 frames of each person's data for its distinction capability, which resulted in the area under a curve of 0.719. The pathological gait captured by the iPhone could be distinguished by artificial intelligence.
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Affiliation(s)
- Chifumi Iseki
- Division of Neurology and Clinical Neuroscience, Department of Internal Medicine III, Yamagata University School of Medicine, Yamagata 990-2331, Japan
- Department of Behavioral Neurology and Cognitive Neuroscience, Tohoku University Graduate School of Medicine, Sendai 980-8575, Japan
| | - Tatsuya Hayasaka
- Department of Anesthesiology, Yamagata University School of Medicine, Yamagata 990-2331, Japan
| | - Hyota Yanagawa
- Department of Medicine, Yamagata University School of Medicine, Yamagata 990-2331, Japan
| | - Yuta Komoriya
- Department of Anesthesiology, Yamagata University School of Medicine, Yamagata 990-2331, Japan
| | - Toshiyuki Kondo
- Division of Neurology and Clinical Neuroscience, Department of Internal Medicine III, Yamagata University School of Medicine, Yamagata 990-2331, Japan
| | - Masayuki Hoshi
- Department of Physical Therapy, Fukushima Medical University School of Health Sciences, 10-6 Sakaemachi, Fukushima 960-8516, Japan
| | - Tadanori Fukami
- Department of Informatics, Faculty of Engineering, Yamagata University, Yonezawa 992-8510, Japan
| | - Yoshiyuki Kobayashi
- Human Augmentation Research Center, National Institute of Advanced Industrial Science and Technology (AIST), Kashiwa II Campus, University of Tokyo, Kashiwa 277-0882, Japan
| | - Shigeo Ueda
- Shin-Aikai Spine Center, Katano Hospital, Katano 576-0043, Japan
| | - Kaneyuki Kawamae
- Department of Anesthesia and Critical Care Medicine, Ohta-Nishinouti Hospital, Koriyama 963-8558, Japan
| | - Masatsune Ishikawa
- Rakuwa Villa Ilios, Rakuwakai Healthcare System, Kyoto 607-8062, Japan
- Normal Pressure Hydrocephalus Center, Rakuwakai Otowa Hospital, Kyoto 607-8062, Japan
| | - Shigeki Yamada
- Normal Pressure Hydrocephalus Center, Rakuwakai Otowa Hospital, Kyoto 607-8062, Japan
- Department of Neurosurgery, Nagoya City University Graduate School of Medical Science, Nagoya 467-8601, Japan
- Interfaculty Initiative in Information Studies, Institute of Industrial Science, The University of Tokyo, Tokyo 113-8654, Japan
| | | | - Yasuyuki Ohta
- Division of Neurology and Clinical Neuroscience, Department of Internal Medicine III, Yamagata University School of Medicine, Yamagata 990-2331, Japan
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Yang B, Yang S, Zhu X, Qi M, Li H, Lv Z, Cheng X, Wang F. Computer Vision Technology for Monitoring of Indoor and Outdoor Environments and HVAC Equipment: A Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:6186. [PMID: 37448035 DOI: 10.3390/s23136186] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 07/01/2023] [Accepted: 07/04/2023] [Indexed: 07/15/2023]
Abstract
Artificial intelligence technologies such as computer vision (CV), machine learning, Internet of Things (IoT), and robotics have advanced rapidly in recent years. The new technologies provide non-contact measurements in three areas: indoor environmental monitoring, outdoor environ-mental monitoring, and equipment monitoring. This paper summarizes the specific applications of non-contact measurement based on infrared images and visible images in the areas of personnel skin temperature, position posture, the urban physical environment, building construction safety, and equipment operation status. At the same time, the challenges and opportunities associated with the application of CV technology are anticipated.
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Affiliation(s)
- Bin Yang
- School of Energy and Safety Engineering, Tianjin Chengjian University, Tianjin 300384, China
| | - Shuang Yang
- School of Energy and Safety Engineering, Tianjin Chengjian University, Tianjin 300384, China
| | - Xin Zhu
- School of Energy and Safety Engineering, Tianjin Chengjian University, Tianjin 300384, China
| | - Min Qi
- School of Energy and Safety Engineering, Tianjin Chengjian University, Tianjin 300384, China
| | - He Li
- School of Energy and Safety Engineering, Tianjin Chengjian University, Tianjin 300384, China
| | - Zhihan Lv
- Department of Game Design, Faculty of Arts, Uppsala University, SE-62167 Uppsala, Sweden
| | - Xiaogang Cheng
- College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210042, China
| | - Faming Wang
- Department of Biosystems, KU Leuven, 3001 Leuven, Belgium
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15
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Lam WWT, Tang YM, Fong KNK. A systematic review of the applications of markerless motion capture (MMC) technology for clinical measurement in rehabilitation. J Neuroeng Rehabil 2023; 20:57. [PMID: 37131238 PMCID: PMC10155325 DOI: 10.1186/s12984-023-01186-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Accepted: 04/26/2023] [Indexed: 05/04/2023] Open
Abstract
BACKGROUND Markerless motion capture (MMC) technology has been developed to avoid the need for body marker placement during motion tracking and analysis of human movement. Although researchers have long proposed the use of MMC technology in clinical measurement-identification and measurement of movement kinematics in a clinical population, its actual application is still in its preliminary stages. The benefits of MMC technology are also inconclusive with regard to its use in assessing patients' conditions. In this review we put a minor focus on the method's engineering components and sought primarily to determine the current application of MMC as a clinical measurement tool in rehabilitation. METHODS A systematic computerized literature search was conducted in PubMed, Medline, CINAHL, CENTRAL, EMBASE, and IEEE. The search keywords used in each database were "Markerless Motion Capture OR Motion Capture OR Motion Capture Technology OR Markerless Motion Capture Technology OR Computer Vision OR Video-based OR Pose Estimation AND Assessment OR Clinical Assessment OR Clinical Measurement OR Assess." Only peer-reviewed articles that applied MMC technology for clinical measurement were included. The last search took place on March 6, 2023. Details regarding the application of MMC technology for different types of patients and body parts, as well as the assessment results, were summarized. RESULTS A total of 65 studies were included. The MMC systems used for measurement were most frequently used to identify symptoms or to detect differences in movement patterns between disease populations and their healthy counterparts. Patients with Parkinson's disease (PD) who demonstrated obvious and well-defined physical signs were the largest patient group to which MMC assessment had been applied. Microsoft Kinect was the most frequently used MMC system, although there was a recent trend of motion analysis using video captured with a smartphone camera. CONCLUSIONS This review explored the current uses of MMC technology for clinical measurement. MMC technology has the potential to be used as an assessment tool as well as to assist in the detection and identification of symptoms, which might further contribute to the use of an artificial intelligence method for early screening for diseases. Further studies are warranted to develop and integrate MMC system in a platform that can be user-friendly and accurately analyzed by clinicians to extend the use of MMC technology in the disease populations.
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Affiliation(s)
- Winnie W T Lam
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
| | - Yuk Ming Tang
- Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
| | - Kenneth N K Fong
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China.
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Kincaid C, Johnson P, Charles SK. Feasibility of using the Leap Motion Controller to administer conventional motor tests: a proof-of-concept study. Biomed Phys Eng Express 2023; 9. [PMID: 36623293 DOI: 10.1088/2057-1976/acb159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Accepted: 01/09/2023] [Indexed: 01/11/2023]
Abstract
Although upper-limb movement impairments are common, the primary tools for assessing and tracking impairments in clinical settings are limited. Markerless motion capture (MMC) technology has the potential to provide a large amount of quantitative, objective movement data in routine clinical use. Many past studies have focused on whether MMC are sufficiently accurate. However, another necessary step is to create meaningful clinical tests that can be administered via MMC in a robust manner. Four conventional upper-limb motor tests common in clinical assessments (visually guided movement, finger tapping, postural tremor, and reaction time) were modified so they can be administered via a particular MMC sensor, the Leap Motion Controller (LMC). In this proof-of-concept study, we administered these modified tests to 100 healthy subjects and present here the successes and challenges we encountered. Subjects generally found the LMC and the graphical user interfaces of the tests easy to use. The LMC recorded movement with sufficiently high sampling rate (>106 samples/s), and the rate of LMC malfunctions (mainly jumps in time or space) was low, so only 1.9% of data was discarded. However, administration of the tests also revealed some significant weaknesses. The visually guided movement test was easily implemented with the LMC; the modified reaction time test worked reasonably well with the LMC but is likely more easily implemented with other existing technologies; and the modified tremor and finger tapping tests did not work well because of the limited bandwidth of the LMC. Our findings highlight the need to develop and evaluate motor tests specifically suited for MMC. The real strength of MMC may not be in replicating conventional tests but rather in administering new tests or testing conditions not possible with conventional clinical tests or other technologies.
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Affiliation(s)
- Clay Kincaid
- Mechanical Engineering, Brigham Young University, Provo, Utah 84602, United States of America
| | - Paula Johnson
- Neuroscience, Brigham Young University, Provo, Utah 84602, United States of America
| | - Steven K Charles
- Mechanical Engineering, Brigham Young University, Provo, Utah 84602, United States of America.,Neuroscience, Brigham Young University, Provo, Utah 84602, United States of America
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17
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Açış B, Güney S. Classification of human movements by using Kinect sensor. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/04/2022]
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18
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Alhammad A, Herrington L, Jones P, Althomali OW, Jones R. The reliability of lower limb 3D gait analysis variables during a change of direction to 90- and 135-degree manoeuvres in recreational soccer players. J Back Musculoskelet Rehabil 2023; 36:173-180. [PMID: 35964167 DOI: 10.3233/bmr-210351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
BACKGROUND Several biomechanical outcomes are being used to monitor the risk of injuries; therefore, their reliability and measurement errors need to be known. OBJECTIVE To measure the reliability and measurement error in lower limb 3D gait analysis outcomes during a 90∘ and 135∘ change of direction (COD) manoeuvre. METHODS A test re-test reliability study for ten healthy recreational players was conducted at seven-day intervals. Kinematics (Hip flexion, adduction, internal rotation angles and knee flexion abduction angles) and kinetics (Knee abduction moment and vertical ground reaction force) data during cutting 90∘ and 135∘ were collected using 3D gait analysis and force platform. Five trials for each task and leg were collected. Standard error of measurement (SEM) and the intraclass correlation coefficient (ICC) were calculated from the randomised leg. RESULT The ICC values of the kinematics, kinetics, and vertical ground reaction force (VGRF) outcomes (90∘ and 135∘) ranged from 0.85 to 0.95, showing good to excellent reliability. The SEM for joint angles was less than 1.69∘. The VGRV showed a higher ICC value than the other outcomes. CONCLUSION The current study results support the use of kinematics, kinetics, and VGRF outcomes for the assessment of knee ACL risk in clinic or research. However, the hip internal rotation angle should be treated with caution since the standard measurement error exceeded 10% compared to the mean value. The measurement errors provided in the current study are valuable for future studies.
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Affiliation(s)
- Ayman Alhammad
- Medical Rehabilitation Hospital, Ministry of Health, Madinah, Saudi Arabia
| | - Lee Herrington
- School of Health and Society, University of Salford, Salford, UK
| | - Paul Jones
- School of Health and Society, University of Salford, Salford, UK
| | - Omar W Althomali
- Department of Physiotherapy, College of Applied Medical Sciences, University of Ha'il, Ha'll, Saudi Arabia
| | - Richard Jones
- School of Health and Society, University of Salford, Salford, UK
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Liang F, Yu S, Pang S, Wang X, Jie J, Gao F, Song Z, Li B, Liao WH, Yin M. Non-human primate models and systems for gait and neurophysiological analysis. Front Neurosci 2023; 17:1141567. [PMID: 37188006 PMCID: PMC10175625 DOI: 10.3389/fnins.2023.1141567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Accepted: 04/11/2023] [Indexed: 05/17/2023] Open
Abstract
Brain-computer interfaces (BCIs) have garnered extensive interest and become a groundbreaking technology to restore movement, tactile sense, and communication in patients. Prior to their use in human subjects, clinical BCIs require rigorous validation and verification (V&V). Non-human primates (NHPs) are often considered the ultimate and widely used animal model for neuroscience studies, including BCIs V&V, due to their proximity to humans. This literature review summarizes 94 NHP gait analysis studies until 1 June, 2022, including seven BCI-oriented studies. Due to technological limitations, most of these studies used wired neural recordings to access electrophysiological data. However, wireless neural recording systems for NHPs enabled neuroscience research in humans, and many on NHP locomotion, while posing numerous technical challenges, such as signal quality, data throughout, working distance, size, and power constraint, that have yet to be overcome. Besides neurological data, motion capture (MoCap) systems are usually required in BCI and gait studies to capture locomotion kinematics. However, current studies have exclusively relied on image processing-based MoCap systems, which have insufficient accuracy (error: ≥4° and 9 mm). While the role of the motor cortex during locomotion is still unclear and worth further exploration, future BCI and gait studies require simultaneous, high-speed, accurate neurophysiological, and movement measures. Therefore, the infrared MoCap system which has high accuracy and speed, together with a high spatiotemporal resolution neural recording system, may expand the scope and improve the quality of the motor and neurophysiological analysis in NHPs.
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Affiliation(s)
- Fengyan Liang
- Key Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan University, Haikou, China
- Department of Rehabilitation Medicine, Affiliated Haikou Hospital of Xiangya Medical College, Central South University, Haikou, China
| | - Shanshan Yu
- Key Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan University, Haikou, China
| | - Siqi Pang
- Key Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan University, Haikou, China
| | - Xiao Wang
- Key Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan University, Haikou, China
| | - Jing Jie
- Key Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan University, Haikou, China
| | - Fei Gao
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Zhenhua Song
- Department of Rehabilitation Medicine, Affiliated Haikou Hospital of Xiangya Medical College, Central South University, Haikou, China
| | - Binbin Li
- Department of Rehabilitation Medicine, Affiliated Haikou Hospital of Xiangya Medical College, Central South University, Haikou, China
| | - Wei-Hsin Liao
- Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, China
| | - Ming Yin
- Key Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan University, Haikou, China
- *Correspondence: Ming Yin,
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Bhatia V, Vaishya RO, Jain A, Grover V, Arora S, Das G, Algarni YA, Baba SM, Khateeb SU, Saluja P, Bavabeedu SS. Static and dynamic validation of kinect for ergonomic postural analysis using electro-goniometers as a gold standard:A preliminary study. Technol Health Care 2023; 31:2107-2123. [PMID: 37125584 DOI: 10.3233/thc-220727] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
BACKGROUND Evaluation of the working postures and development of new techniques are paramount in reducing the awkward postures and occurrence of musculoskeletal disorders (MSDs). The Kinect sensor, a portable and cost-effective device, appears to be a promising alternative to study work postures. OBJECTIVE The current study aimed to evaluate the validity of Kinect against the gold-standard instrument (electro-goniometers) for body joint angle measurements. METHODS A unique software application was developed to measure the critical body joint angles for postural evaluation by using the Kinect's skeletal tracking feature. The body joint angle data of ten volunteers were measured simultaneously by both Kinect and electro-goniometers. The validation analysis was conducted in both static and dynamic domains of application. RESULTS Minimal variation was observed between the two techniques, and the Kinect correlated well for upper-arm joint angles of 45∘, 60∘ and 90∘; lower-arm joint angles of 30∘, 45∘, 60∘, and 90∘; straight neck position, neck joint angle at maximum possible flexion; straight trunk position, trunk bend angle at full flexion. In dynamic analysis, four out of five ICC values were > 0.75 except for the upper arm. Discrepancies in the results indicated the disapproval of Kinect for only wrist measurements. CONCLUSION The results of the static and dynamic studies gave a sufficient basis to consider the Kinect tool as an alternative to contemporary posture-based ergonomic evaluation methods.
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Affiliation(s)
- Vibha Bhatia
- Department of Production and Industrial Engineering, Industrial and Product Design (CoE), Punjab Engineering College (Deemed to be University), Chandigarh, India
| | - Rahul O Vaishya
- Department of Production and Industrial Engineering, Industrial and Product Design (CoE), Punjab Engineering College (Deemed to be University), Chandigarh, India
| | - Ashish Jain
- Dr. Harvansh Singh Judge Institute of Dental Sciences and Hospitals, Punjab University, Chandigarh, India
| | - Vishakha Grover
- Dr. Harvansh Singh Judge Institute of Dental Sciences and Hospitals, Punjab University, Chandigarh, India
| | - Suraj Arora
- Department of Restorative Dental Sciences, College of Dentistry, King Khalid University, Abha, Saudi Arabia
| | - Gotam Das
- Department of Prosthodontics, College of Dentistry, King Khalid University, Abha, Saudi Arabia
| | - Youssef A Algarni
- Department of Restorative Dental Sciences, College of Dentistry, King Khalid University, Abha, Saudi Arabia
| | - Suheel Manzoor Baba
- Department of Restorative Dental Sciences, College of Dentistry, King Khalid University, Abha, Saudi Arabia
| | - Shafait Ullah Khateeb
- Department of Restorative Dental Sciences, College of Dentistry, King Khalid University, Abha, Saudi Arabia
| | - Priyanka Saluja
- Department of Dentistry, University of Alberta, Edmonton, AB, Canada
| | - Shashit Shetty Bavabeedu
- Department of Restorative Dental Sciences, College of Dentistry, King Khalid University, Abha, Saudi Arabia
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Scoring People With Spinal Muscular Atrophy on the Motor Function Measure Using the Microsoft Kinect. Pediatr Phys Ther 2023; 35:36-41. [PMID: 36288197 DOI: 10.1097/pep.0000000000000968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
PURPOSE Assess the ability of the Kinect to capture movement and posture of people with spinal muscular atrophy (SMA) during completion of 14 items of the Motor Function Measure, a validated functional rating scale for people with neuromuscular diseases. METHODS Multicenter feasibility study in which Motor Function Measure items were scored as usual by the participant's therapist during the completion (Score-T) while another therapist scored items based only on the visualization of digital data collected using the Kinect (Score-D). Agreement and disagreement were investigated. RESULTS Twenty people with SMA type 2 or 3 were participants; 142 items were recorded and analyzed. There was 31.7% agreement between Score-T and Score-D for participants with SMA type 2, and 76.2% for those with SMA type 3. CONCLUSIONS The results prevent us from considering the use of Kinect capture to deduce an automated scoring, but this device may be of interest to highlight potential compensations.
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22
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A type-2 neuro-fuzzy system with a novel learning method for Parkinson’s disease diagnosis. APPL INTELL 2022. [DOI: 10.1007/s10489-022-04276-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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23
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Momin MS, Sufian A, Barman D, Dutta P, Dong M, Leo M. In-Home Older Adults' Activity Pattern Monitoring Using Depth Sensors: A Review. SENSORS (BASEL, SWITZERLAND) 2022; 22:9067. [PMID: 36501769 PMCID: PMC9735577 DOI: 10.3390/s22239067] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 11/10/2022] [Accepted: 11/15/2022] [Indexed: 06/17/2023]
Abstract
The global population is aging due to many factors, including longer life expectancy through better healthcare, changing diet, physical activity, etc. We are also witnessing various frequent epidemics as well as pandemics. The existing healthcare system has failed to deliver the care and support needed to our older adults (seniors) during these frequent outbreaks. Sophisticated sensor-based in-home care systems may offer an effective solution to this global crisis. The monitoring system is the key component of any in-home care system. The evidence indicates that they are more useful when implemented in a non-intrusive manner through different visual and audio sensors. Artificial Intelligence (AI) and Computer Vision (CV) techniques may be ideal for this purpose. Since the RGB imagery-based CV technique may compromise privacy, people often hesitate to utilize in-home care systems which use this technology. Depth, thermal, and audio-based CV techniques could be meaningful substitutes here. Due to the need to monitor larger areas, this review article presents a systematic discussion on the state-of-the-art using depth sensors as primary data-capturing techniques. We mainly focused on fall detection and other health-related physical patterns. As gait parameters may help to detect these activities, we also considered depth sensor-based gait parameters separately. The article provides discussions on the topic in relation to the terminology, reviews, a survey of popular datasets, and future scopes.
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Affiliation(s)
- Md Sarfaraz Momin
- Department of Computer Science, Kaliachak College, University of Gour Banga, Malda 732101, India
- Department of Computer & System Sciences, Visva-Bharati University, Bolpur 731235, India
| | - Abu Sufian
- Department of Computer Science, University of Gour Banga, Malda 732101, India
| | - Debaditya Barman
- Department of Computer & System Sciences, Visva-Bharati University, Bolpur 731235, India
| | - Paramartha Dutta
- Department of Computer & System Sciences, Visva-Bharati University, Bolpur 731235, India
| | - Mianxiong Dong
- Department of Science and Informatics, Muroran Institute of Technology, Muroran 050-8585, Hokkaido, Japan
| | - Marco Leo
- National Research Council of Italy, Institute of Applied Sciences and Intelligent Systems, 73100 Lecce, Italy
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Hannink E, Dawes H, Shannon TML, Barker KL. Validity of sagittal thoracolumbar curvature measurement using a non-radiographic surface topography method. Spine Deform 2022; 10:1299-1306. [PMID: 35809201 PMCID: PMC9579080 DOI: 10.1007/s43390-022-00538-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 05/28/2022] [Indexed: 12/03/2022]
Abstract
PURPOSE To estimate the criterion validity of sagittal thoracolumbar spine measurement using a surface topography method in a clinical population against the gold standard and to estimate concurrent validity against two non-radiographic clinical tools. METHODS In this cross-sectional validity study, thoracolumbar curvature was measured in adults with spinal conditions recruited from a specialist orthopaedic hospital. A surface topography method using a Kinect sensor was compared to three other measurement methods: spinal radiograph (gold standard), flexicurve and digital inclinometer. Correlation coefficients and agreement between the measurement tools were analysed. RESULTS Twenty-nine participants (79% female) were included in criterion validity analyses and 38 (76% female) in concurrent validity analyses. The surface topography method was moderately correlated with the radiograph (r = .70, p < .001) in the thoracic spine, yet there was no significant correlation with the radiograph in the lumbar spine (r = .32, p = .89). The surface topography method was highly correlated with the flexicurve (rs = .91, p < .001) and digital inclinometer (r = .82, p < .001) in the thoracic spine, and highly correlated with the flexicurve (r = .74, p < .001) and digital inclinometer (r = .74, p < .001) in the lumbar spine. CONCLUSIONS The surface topography method showed moderate correlation and agreement in thoracic spine with the radiograph (criterion validity) and high correlation with the flexicurve and digital inclinometer (concurrent validity). Compared with other non-radiographic tools, this surface topography method displayed similar criterion validity for kyphosis curvature measurement.
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Affiliation(s)
- Erin Hannink
- Physiotherapy Research Unit, Nuffield Orthopaedic Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
- Centre for Movement, Occupational and Rehabilitation Sciences, Oxford Brookes University, Oxford, UK.
- Nuffield Department of Orthopaedic, Rheumatoid and Musculoskeletal Sciences, University of Oxford, Oxford, UK.
| | - Helen Dawes
- Centre for Movement, Occupational and Rehabilitation Sciences, Oxford Brookes University, Oxford, UK
- College of Medicine and Health, University of Exeter, Exeter, UK
- Oxford Health, Biomedical Research Centre, University of Oxford, Oxford, UK
| | - Thomas M L Shannon
- Centre for Biomechanics and Rehabilitation Technologies, Staffordshire University, Stoke-on-Trent, UK
| | - Karen L Barker
- Physiotherapy Research Unit, Nuffield Orthopaedic Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
- Nuffield Department of Orthopaedic, Rheumatoid and Musculoskeletal Sciences, University of Oxford, Oxford, UK
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25
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Liu PL, Chang CC, Li L, Xu X. A Simple Method to Optimally Select Upper-Limb Joint Angle Trajectories from Two Kinect Sensors during the Twisting Task for Posture Analysis. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22197662. [PMID: 36236761 PMCID: PMC9572104 DOI: 10.3390/s22197662] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 09/26/2022] [Accepted: 10/06/2022] [Indexed: 05/17/2023]
Abstract
A trunk-twisting posture is strongly associated with physical discomfort. Measurement of joint kinematics to assess physical exposure to injuries is important. However, using a single Kinect sensor to track the upper-limb joint angle trajectories during twisting tasks in the workplace is challenging due to sensor view occlusions. This study provides and validates a simple method to optimally select the upper-limb joint angle data from two Kinect sensors at different viewing angles during the twisting task, so the errors of trajectory estimation can be improved. Twelve healthy participants performed a rightward twisting task. The tracking errors of the upper-limb joint angle trajectories of two Kinect sensors during the twisting task were estimated based on concurrent data collected using a conventional motion tracking system. The error values were applied to generate the error trendlines of two Kinect sensors using third-order polynomial regressions. The intersections between two error trendlines were used to define the optimal data selection points for data integration. The finding indicates that integrating the outputs from two Kinect sensor datasets using the proposed method can be more robust than using a single sensor for upper-limb joint angle trajectory estimations during the twisting task.
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Affiliation(s)
- Pin-Ling Liu
- Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Hsinchu 300, Taiwan
| | - Chien-Chi Chang
- Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Hsinchu 300, Taiwan
- Correspondence: ; Tel.: +886-3-5742942
| | - Li Li
- Edward P. Fitts Department of Industrial and Systems Engineering, North Carolina State University, Raleigh, NC 27695, USA
| | - Xu Xu
- Edward P. Fitts Department of Industrial and Systems Engineering, North Carolina State University, Raleigh, NC 27695, USA
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26
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Da Silva KG, Nuvolini RA, Bacha JMR, De Freitas TB, Doná F, Torriani-Pasin C, Pompeu JE. Comparison of the Effects of an Exergame-Based Program with Conventional Physiotherapy Protocol Based on Core Areas of the European Guideline on Postural Control, Functional Mobility, and Quality of Life in Patients with Parkinson's Disease: Randomized Clinical Trial. Games Health J 2022; 12:228-241. [PMID: 36206023 DOI: 10.1089/g4h.2022.0039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Introduction: Impairment of postural control and functional mobility are debilitating symptoms of Parkinson's disease (PD). In addition to limiting performance in activities of daily living, it is associated with a higher prevalence of falls in this population. Particularly, dysfunction in postural control does not respond to dopaminergic replacement therapy, but physiotherapy can improve this outcome in patients with PD. Objective: The aim of this study was to analyze the effects of training based on Kinect Adventures games compared with a conventional physiotherapy protocol based on the core areas of the European physiotherapy guideline in patients with PD on postural control, functional mobility, self-perception of confidence in the balance, quality of life (QoL), lower limb muscle strength, transfer skill and motor function, as well as to observe adherence and safety interventions. Methods: Thirty-eight patients diagnosed with idiopathic PD were randomized into two groups, and performed 14 training sessions, twice a week for 60 minutes. The primary outcome assessed postural control using the Mini-Balance Evaluation Systems Test (Mini-BESTest). The following were evaluated as secondary outcomes: limit of stability; balance functional reserve and center of pressure area by computerized posturography; functional mobility by the Timed Up and Go test; self-confidence in balance through the Activities-specific Balance Confidence scale; QoL through the Parkinson's Disease Questionnaire; lower limb muscle strength by the Five Times Sit-To-Stand test; and motor function by the Unified Parkinson's Disease Rating Scale. Results: Patients completed training sessions with high rates of safety and adherence. After training, there was a significant improvement in postural control, motor function, and QoL. Conclusion: Both interventions proved to be safe, applicable, and effective to improve postural control, QoL, and motor function in patients with PD. However, there was no difference between the effects of Kinect Adventures games and conventional physiotherapeutic protocol in patients with PD. Brazilian Registry of Clinical Trials (RBR-27kqv5).
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Affiliation(s)
- Keyte Guedes Da Silva
- Department of Physiotherapy, Speech and Occupational Therapy, School of Medicine, University of Sao Paulo, Sao Paulo, Brazil
| | - Rosemeyre Alcarde Nuvolini
- Department of Physiotherapy, Speech and Occupational Therapy, School of Medicine, University of Sao Paulo, Sao Paulo, Brazil
| | - Jéssica Maria Ribeiro Bacha
- Department of Physiotherapy, Speech and Occupational Therapy, School of Medicine, University of Sao Paulo, Sao Paulo, Brazil
| | - Tatiana Beline De Freitas
- Motor Behavior Laboratory, Department of Biodynamics of Human Body Movement, School of Physical Education and Sport, University of São Paulo, São Paulo, Brazil
| | - Flávia Doná
- Department of Sciences of the Human Movement and Rehabilitation, Federal University of São Paulo, São Paulo, Brazil
| | - Camila Torriani-Pasin
- Motor Behavior Laboratory, Department of Biodynamics of Human Body Movement, School of Physical Education and Sport, University of São Paulo, São Paulo, Brazil
| | - José Eduardo Pompeu
- Department of Physiotherapy, Speech and Occupational Therapy, School of Medicine, University of Sao Paulo, Sao Paulo, Brazil
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Hu H, Xiao D, Rhodin H, Murphy TH. Towards a Visualizable, De-identified Synthetic Biomarker of Human Movement Disorders. JOURNAL OF PARKINSON'S DISEASE 2022; 1:2085-2096. [PMID: 36057831 PMCID: PMC10473142 DOI: 10.3233/jpd-223351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 08/10/2022] [Indexed: 12/15/2022]
Abstract
Human motion analysis has been a common thread across modern and early medicine. While medicine evolves, analysis of movement disorders is mostly based on clinical presentation and trained observers making subjective assessments using clinical rating scales. Currently, the field of computer vision has seen exponential growth and successful medical applications. While this has been the case, neurology, for the most part, has not embraced digital movement analysis. There are many reasons for this including: the limited size of labeled datasets, accuracy and nontransparent nature of neural networks, and potential legal and ethical concerns. We hypothesize that a number of opportunities are made available by advancements in computer vision that will enable digitization of human form, movements, and will represent them synthetically in 3D. Representing human movements within synthetic body models will potentially pave the way towards objective standardized digital movement disorder diagnosis and building sharable open-source datasets from such processed videos. We provide a perspective of this emerging field and describe how clinicians and computer scientists can navigate this new space. Such digital movement capturing methods will be important for both machine learning-based diagnosis and computer vision-aided clinical assessment. It would also supplement face-to-face clinical visits and be used for longitudinal monitoring and remote diagnosis.
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Affiliation(s)
- Hao Hu
- University of British Columbia, Department of Psychiatry, Kinsmen Laboratory of Neurological Research, Detwiller Pavilion, Vancouver, BC, Canada
- Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | - Dongsheng Xiao
- University of British Columbia, Department of Psychiatry, Kinsmen Laboratory of Neurological Research, Detwiller Pavilion, Vancouver, BC, Canada
- Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | - Helge Rhodin
- Department of Computer Science, University of British Columbia, Vancouver, BC, Canada
| | - Timothy H. Murphy
- University of British Columbia, Department of Psychiatry, Kinsmen Laboratory of Neurological Research, Detwiller Pavilion, Vancouver, BC, Canada
- Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
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Agami S, Riemer R, Berman S. Enhancing motion tracking accuracy of a low-cost 3D video sensor using a biomechanical model, sensor fusion, and deep learning. FRONTIERS IN REHABILITATION SCIENCES 2022; 3:956381. [PMID: 36188943 PMCID: PMC9397931 DOI: 10.3389/fresc.2022.956381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Accepted: 07/27/2022] [Indexed: 11/13/2022]
Abstract
Low-cost 3D video sensors equipped with routines for extracting skeleton data facilitate the widespread use of virtual reality (VR) for rehabilitation. However, the accuracy of the extracted skeleton data is often limited. Accuracy can be improved using a motion tracker, e.g., using a recurrent neural network (RNN). Yet, training an RNN requires a considerable amount of relevant and accurate training data. Training databases can be obtained using gold-standard motion tracking sensors. This limits the use of the RNN trackers in environments and tasks that lack accessibility to gold-standard sensors. Digital goniometers are typically cheaper, more portable, and simpler to use than gold-standard motion tracking sensors. The current work suggests a method for generating accurate skeleton data suitable for training an RNN motion tracker based on the offline fusion of a Kinect 3D video sensor and an electronic goniometer. The fusion applies nonlinear constraint optimization, where the constraints are based on an advanced shoulder-centered kinematic model of the arm. The model builds on the representation of the arm as a triangle (the arm triangle). The shoulder-centered representation of the arm triangle motion simplifies constraint representation and consequently the optimization problem. To test the performance of the offline fusion and the RNN trained using the optimized data, arm motion of eight participants was recorded using a Kinect sensor, an electronic goniometer, and, for comparison, a passive-marker-based motion tracker. The data generated by fusing the Kinect and goniometer recordings were used for training two long short-term memory (LSTM) RNNs. The input to one RNN included both the Kinect and the goniometer data, and the input to the second RNN included only Kinect data. The performance of the networks was compared to the performance of a tracker based on a Kalman filter and to the raw Kinect measurements. The accuracy of the fused data was high, and it considerably improved data accuracy. The accuracy for both trackers was high, and both were more accurate than the Kalman filter tracker and the raw Kinect measurements. The developed methods are suitable for integration with immersive VR rehabilitation systems in the clinic and the home environments.
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Affiliation(s)
| | | | - Sigal Berman
- Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, Beer-Sheva, Israel
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29
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Kinect-Based Assessment of Lower Limbs during Gait in Post-Stroke Hemiplegic Patients: A Narrative Review. SENSORS 2022; 22:s22134910. [PMID: 35808426 PMCID: PMC9269781 DOI: 10.3390/s22134910] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 06/23/2022] [Accepted: 06/27/2022] [Indexed: 12/25/2022]
Abstract
The aim of this review was to present an overview of the state of the art in the use of the Microsoft Kinect camera to assess gait in post-stroke individuals through an analysis of the available literature. In recent years, several studies have explored the potentiality, accuracy, and effectiveness of this 3D optical sensor as an easy-to-use and non-invasive clinical measurement tool for the assessment of gait parameters in several pathologies. Focusing on stroke individuals, some of the available studies aimed to directly assess and characterize their gait patterns. In contrast, other studies focused on the validation of Kinect-based measurements with respect to a gold-standard reference (i.e., optoelectronic systems). However, the nonhomogeneous characteristics of the participants, of the measures, of the methodologies, and of the purposes of the studies make it difficult to adequately compare the results. This leads to uncertainties about the strengths and weaknesses of this technology in this pathological state. The final purpose of this narrative review was to describe and summarize the main features of the available works on gait in the post-stroke population, highlighting similarities and differences in the methodological approach and primary findings, thus facilitating comparisons of the studies as much as possible.
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Scott B, Seyres M, Philp F, Chadwick EK, Blana D. Healthcare applications of single camera markerless motion capture: a scoping review. PeerJ 2022; 10:e13517. [PMID: 35642200 PMCID: PMC9148557 DOI: 10.7717/peerj.13517] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 05/09/2022] [Indexed: 01/17/2023] Open
Abstract
Background Single camera markerless motion capture has the potential to facilitate at home movement assessment due to the ease of setup, portability, and affordable cost of the technology. However, it is not clear what the current healthcare applications of single camera markerless motion capture are and what information is being collected that may be used to inform clinical decision making. This review aims to map the available literature to highlight potential use cases and identify the limitations of the technology for clinicians and researchers interested in the collection of movement data. Survey Methodology Studies were collected up to 14 January 2022 using Pubmed, CINAHL and SPORTDiscus using a systematic search. Data recorded included the description of the markerless system, clinical outcome measures, and biomechanical data mapped to the International Classification of Functioning, Disability and Health Framework (ICF). Studies were grouped by patient population. Results A total of 50 studies were included for data collection. Use cases for single camera markerless motion capture technology were identified for Neurological Injury in Children and Adults; Hereditary/Genetic Neuromuscular Disorders; Frailty; and Orthopaedic or Musculoskeletal groups. Single camera markerless systems were found to perform well in studies involving single plane measurements, such as in the analysis of infant general movements or spatiotemporal parameters of gait, when evaluated against 3D marker-based systems and a variety of clinical outcome measures. However, they were less capable than marker-based systems in studies requiring the tracking of detailed 3D kinematics or fine movements such as finger tracking. Conclusions Single camera markerless motion capture offers great potential for extending the scope of movement analysis outside of laboratory settings in a practical way, but currently suffers from a lack of accuracy where detailed 3D kinematics are required for clinical decision making. Future work should therefore focus on improving tracking accuracy of movements that are out of plane relative to the camera orientation or affected by occlusion, such as supination and pronation of the forearm.
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Affiliation(s)
- Bradley Scott
- School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Aberdeen, United Kingdom
| | - Martin Seyres
- School of Engineering, University of Aberdeen, Aberdeen, United Kingdom
| | - Fraser Philp
- School of Health Sciences, University of Liverpool, Liverpool, United Kingdom
| | | | - Dimitra Blana
- School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Aberdeen, United Kingdom
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Automatic Personality Assessment through Movement Analysis. SENSORS 2022; 22:s22103949. [PMID: 35632357 PMCID: PMC9147512 DOI: 10.3390/s22103949] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Revised: 05/09/2022] [Accepted: 05/18/2022] [Indexed: 11/16/2022]
Abstract
Obtaining accurate and objective assessments of an individual's personality is vital in many areas including education, medicine, sports and management. Currently, most personality assessments are conducted using scales and questionnaires. Unfortunately, it has been observed that both scales and questionnaires present various drawbacks. Their limitations include the lack of veracity in the answers, limitations in the number of times they can be administered, or cultural biases. To solve these problems, several articles have been published in recent years proposing the use of movements that participants make during their evaluation as personality predictors. In this work, a multiple linear regression model was developed to assess the examinee's personality based on their movements. Movements were captured with the low-cost Microsoft Kinect camera, which facilitates its acceptance and implementation. To evaluate the performance of the proposed system, a pilot study was conducted aimed at assessing the personality traits defined by the Big-Five Personality Model. It was observed that the traits that best fit the model are Extroversion and Conscientiousness. In addition, several patterns that characterize the five personality traits were identified. These results show that it is feasible to assess an individual's personality through his or her movements and open up pathways for several research.
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Thomas J, Hall JB, Bliss R, Guess TM. Comparison of Azure Kinect and optical retroreflective motion capture for kinematic and spatiotemporal evaluation of the sit-to-stand test. Gait Posture 2022; 94:153-159. [PMID: 35334335 DOI: 10.1016/j.gaitpost.2022.03.011] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 03/15/2022] [Accepted: 03/17/2022] [Indexed: 02/02/2023]
Abstract
BACKGROUND The sit-to-stand test (STS) is commonly used to evaluate functional capabilities within a variety of clinical populations. Traditionally STS is a timed test, limiting the depth of information which can be gained from its evaluation. The Azure Kinect has the potential to add in-depth analysis to STS. Despite these potential benefits, the recently released (2019) Azure Kinect has yet to be evaluated for its ability to accurately assess STS. RESEARCH QUESTIONS Purposes of this work were to compare data captured during STS using both a 12 camera Vicon motion capture system and the Azure Kinect; and to calculate kinematic and spatiotemporal variables related to the four phases of the STS cycle. METHODS Spatiotemporal and kinematic measures for STS were simultaneously collected by both devices for 15 participants. Cycle waveforms were compared for right and left hip and knee flexion/extension angular displacement, right and left hip and knee flexion/extension angular velocity, and knee-to-ankle separation ratio. Evaluated discrete outcome variables included: phase time points, maximum knee extension velocity from phases 3 to 4, medial-lateral pelvic sway range, and total time to completion. Waveform summary data were compared using R, R2, and RMSE. Discrete variables were analyzed using Spearman's Rank correlation coefficient. RESULTS R and R2 values between the two systems indicated high levels of correlation (all R values > 0.711, all R2 values > 0.660). Although there was an overall high level of agreement between waveform shapes, high RMSE values indicated some minor tracking errors for Kinect within the STS cycle. Spearman's Rank correlation coefficient indicated high levels of correlation between the systems for discrete variables (all R values > 0.89), with the exception of medial-lateral pelvic sway range. SIGNIFICANCE The Azure Kinect provides valuable insight into STS movement strategies allowing for improved precision in clinical decision making across multiple clinical populations.
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Affiliation(s)
- Jacob Thomas
- School of Health Professions, University of Missouri, Columbia, MO, USA.
| | - Jamie B Hall
- Department of Physical Therapy, University of Missouri, Columbia, MO, USA
| | - Rebecca Bliss
- Department of Physical Therapy, University of Missouri, Columbia, MO, USA
| | - Trent M Guess
- Department of Physical Therapy, University of Missouri, Columbia, MO, USA; Department of Orthopaedic Surgery, University of Missouri, Columbia, MO, USA
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Homan K, Yamamoto K, Kadoya K, Ishida N, Iwasaki N. Comprehensive validation of a wearable foot sensor system for estimating spatiotemporal gait parameters by simultaneous three-dimensional optical motion analysis. BMC Sports Sci Med Rehabil 2022; 14:71. [PMID: 35430808 PMCID: PMC9013462 DOI: 10.1186/s13102-022-00461-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Accepted: 04/11/2022] [Indexed: 11/10/2022]
Abstract
BACKGROUND Use of a wearable gait analysis system (WGAS) is becoming common when conducting gait analysis studies due to its versatility. At the same time, its versatility raises a concern about its accuracy, because its calculations rely on assumptions embedded in its algorithms. The purpose of the present study was to validate twenty spatiotemporal gait parameters calculated by the WGAS by comparison with simultaneous measurements taken with an optical motion capture system (OMCS). METHODS Ten young healthy volunteers wore two inertial sensors of the commercially available WGAS, Physilog®, on their feet and 23 markers for the OMCS on the lower part of the body. The participants performed at least three sets of 10-m walk tests at their self-paced speed in the laboratory equipped with 12 high-speed digital cameras with embedded force plates. To measure repeatability, all participants returned for a second day of testing within two weeks. RESULTS Twenty gait parameters calculated by the WGAS had a significant correlation with the ones determined by the OMCS. Bland and Altman analysis showed that the between-device agreement for twenty gait parameters was within clinically acceptable limits. The validity of the gait parameters generated by the WGAS was found to be excellent except for two parameters, swing width and maximal heel clearance. The repeatability of the WGAS was excellent when measured between sessions. CONCLUSION The present study showed that spatiotemporal gait parameters estimated by the WGAS were reasonably accurate and repeatable in healthy young adults, providing a scientific basis for applying this system to clinical studies.
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Affiliation(s)
- Kentaro Homan
- Department of Orthopaedic Surgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Kita 15, Nishi 7, Kita-ku, Sapporo, Hokkaido, 060-8638, Japan
| | - Keizo Yamamoto
- School of Lifelong Sport, Hokusho University, 23 Bunkyodai, Ebetsu, 069-8511, Japan
| | - Ken Kadoya
- Department of Orthopaedic Surgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Kita 15, Nishi 7, Kita-ku, Sapporo, Hokkaido, 060-8638, Japan.
| | - Naoki Ishida
- Department of Orthopedic Surgery, Hokuto Medical Corporation Hokuto Hospital, Kisen 7-5 Inada-cho, Obihiro, Hokkaido, Japan
| | - Norimasa Iwasaki
- Department of Orthopaedic Surgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Kita 15, Nishi 7, Kita-ku, Sapporo, Hokkaido, 060-8638, Japan
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Comparison of Motion Analysis Systems in Tracking Upper Body Movement of Myoelectric Bypass Prosthesis Users. SENSORS 2022; 22:s22082953. [PMID: 35458943 PMCID: PMC9029489 DOI: 10.3390/s22082953] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Revised: 04/07/2022] [Accepted: 04/11/2022] [Indexed: 02/01/2023]
Abstract
Current literature lacks a comparative analysis of different motion capture systems for tracking upper limb (UL) movement as individuals perform standard tasks. To better understand the performance of various motion capture systems in quantifying UL movement in the prosthesis user population, this study compares joint angles derived from three systems that vary in cost and motion capture mechanisms: a marker-based system (Vicon), an inertial measurement unit system (Xsens), and a markerless system (Kinect). Ten healthy participants (5F/5M; 29.6 ± 7.1 years) were trained with a TouchBionic i-Limb Ultra myoelectric terminal device mounted on a bypass prosthetic device. Participants were simultaneously recorded with all systems as they performed standardized tasks. Root mean square error and bias values for degrees of freedom in the right elbow, shoulder, neck, and torso were calculated. The IMU system yielded more accurate kinematics for shoulder, neck, and torso angles while the markerless system performed better for the elbow angles. By evaluating the ability of each system to capture kinematic changes of simulated upper limb prosthesis users during a variety of standardized tasks, this study provides insight into the advantages and limitations of using different motion capture technologies for upper limb functional assessment.
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Cabaraux P, Agrawal SK, Cai H, Calabro RS, Casali C, Damm L, Doss S, Habas C, Horn AKE, Ilg W, Louis ED, Mitoma H, Monaco V, Petracca M, Ranavolo A, Rao AK, Ruggieri S, Schirinzi T, Serrao M, Summa S, Strupp M, Surgent O, Synofzik M, Tao S, Terasi H, Torres-Russotto D, Travers B, Roper JA, Manto M. Consensus Paper: Ataxic Gait. CEREBELLUM (LONDON, ENGLAND) 2022; 22:394-430. [PMID: 35414041 DOI: 10.1007/s12311-022-01373-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 01/20/2022] [Indexed: 12/19/2022]
Abstract
The aim of this consensus paper is to discuss the roles of the cerebellum in human gait, as well as its assessment and therapy. Cerebellar vermis is critical for postural control. The cerebellum ensures the mapping of sensory information into temporally relevant motor commands. Mental imagery of gait involves intrinsically connected fronto-parietal networks comprising the cerebellum. Muscular activities in cerebellar patients show impaired timing of discharges, affecting the patterning of the synergies subserving locomotion. Ataxia of stance/gait is amongst the first cerebellar deficits in cerebellar disorders such as degenerative ataxias and is a disabling symptom with a high risk of falls. Prolonged discharges and increased muscle coactivation may be related to compensatory mechanisms and enhanced body sway, respectively. Essential tremor is frequently associated with mild gait ataxia. There is growing evidence for an important role of the cerebellar cortex in the pathogenesis of essential tremor. In multiple sclerosis, balance and gait are affected due to cerebellar and spinal cord involvement, as a result of disseminated demyelination and neurodegeneration impairing proprioception. In orthostatic tremor, patients often show mild-to-moderate limb and gait ataxia. The tremor generator is likely located in the posterior fossa. Tandem gait is impaired in the early stages of cerebellar disorders and may be particularly useful in the evaluation of pre-ataxic stages of progressive ataxias. Impaired inter-joint coordination and enhanced variability of gait temporal and kinetic parameters can be grasped by wearable devices such as accelerometers. Kinect is a promising low cost technology to obtain reliable measurements and remote assessments of gait. Deep learning methods are being developed in order to help clinicians in the diagnosis and decision-making process. Locomotor adaptation is impaired in cerebellar patients. Coordinative training aims to improve the coordinative strategy and foot placements across strides, cerebellar patients benefiting from intense rehabilitation therapies. Robotic training is a promising approach to complement conventional rehabilitation and neuromodulation of the cerebellum. Wearable dynamic orthoses represent a potential aid to assist gait. The panel of experts agree that the understanding of the cerebellar contribution to gait control will lead to a better management of cerebellar ataxias in general and will likely contribute to use gait parameters as robust biomarkers of future clinical trials.
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Affiliation(s)
- Pierre Cabaraux
- Unité Des Ataxies Cérébelleuses, Department of Neurology, CHU de Charleroi, Charleroi, Belgium.
| | | | - Huaying Cai
- Department of Neurology, Neuroscience Center, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, 310016, China
| | | | - Carlo Casali
- Department of Medico-Surgical Sciences and Biotechnologies, University of Rome Sapienza, Latina, Italy
| | - Loic Damm
- EuroMov Digital Health in Motion, Univ Montpellier, IMT Mines Ales, Montpellier, France
| | - Sarah Doss
- Department of Neurological Sciences, University of Nebraska Medical Center, Omaha, USA
| | - Christophe Habas
- Université Versailles Saint-Quentin, Versailles, France.,Service de NeuroImagerie, Centre Hospitalier National des 15-20, Paris, France
| | - Anja K E Horn
- Institute of Anatomy and Cell Biology I, Ludwig Maximilians-University Munich, Munich, Germany
| | - Winfried Ilg
- Section Computational Sensomotorics, Hertie Institute for Clinical Brain Research, University Tübingen, Tübingen, Germany
| | - Elan D Louis
- Department of Neurology, University of Texas Southwestern, Dallas, TX, USA
| | - Hiroshi Mitoma
- Department of Medical Education, Tokyo Medical University, Tokyo, Japan
| | - Vito Monaco
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Maria Petracca
- Department of Human Neurosciences, University of Rome Sapienza, Rome, Italy
| | - Alberto Ranavolo
- Department of Occupational and Environmental Medicine, Epidemiology and Hygiene, INAIL, Monte Porzio Catone, Rome, Italy
| | - Ashwini K Rao
- Department of Rehabilitation & Regenerative Medicine (Programs in Physical Therapy), Gertrude H. Sergievsky Center, College of Physicians and Surgeons, Columbia University, New York, NY, USA
| | - Serena Ruggieri
- Department of Human Neurosciences, University of Rome Sapienza, Rome, Italy.,Neuroimmunology Unit, IRCSS Fondazione Santa Lucia, Rome, Italy
| | - Tommaso Schirinzi
- Department of Systems Medicine, University of Roma Tor Vergata, Rome, Italy
| | - Mariano Serrao
- Department of Medico-Surgical Sciences and Biotechnologies, University of Rome Sapienza, Latina, Italy.,Movement Analysis LAB, Policlinico Italia, Rome, Italy
| | - Susanna Summa
- MARlab, Neuroscience and Neurorehabilitation Department, Bambino Gesù Children's Hospital - IRCCS, Rome, Italy
| | - Michael Strupp
- Department of Neurology and German Center for Vertigo and Balance Disorders, Hospital of the Ludwig Maximilians-University Munich, Munich, Germany
| | - Olivia Surgent
- Neuroscience Training Program and Waisman Center, University of Wisconsin-Madison, Madison, WI, USA
| | - Matthis Synofzik
- Department of Neurodegeneration, Hertie Institute for Clinical Brain Research and Centre of Neurology, Tübingen, Germany
| | - Shuai Tao
- Dalian Key Laboratory of Smart Medical and Health, Dalian University, Dalian, 116622, China
| | - Hiroo Terasi
- Department of Neurology, Tokyo Medical University, Tokyo, Japan
| | - Diego Torres-Russotto
- Department of Neurological Sciences, University of Nebraska Medical Center, Omaha, USA
| | - Brittany Travers
- Department of Kinesiology and Waisman Center, University of Wisconsin-Madison, Madison, WI, USA
| | - Jaimie A Roper
- School of Kinesiology, Auburn University, Auburn, AL, USA
| | - Mario Manto
- Unité Des Ataxies Cérébelleuses, Department of Neurology, CHU de Charleroi, Charleroi, Belgium.,Service Des Neurosciences, University of Mons, UMons, Mons, Belgium
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Kinect-Based Rehabilitation Systems for Stroke Patients: A Scoping Review. BIOMED RESEARCH INTERNATIONAL 2022; 2022:4339054. [PMID: 35386303 PMCID: PMC8977286 DOI: 10.1155/2022/4339054] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Accepted: 02/04/2022] [Indexed: 01/01/2023]
Abstract
Background and Objective. Kinect-based rehabilitation is an effective solution for creating motivation and promoting adherence to rehabilitation programs in stroke patients. The current study was aimed at examining the effects of Kinect-based rehabilitation systems on performance improvement, domains of use, and its limitations for stroke patients. Method. This study was conducted according to Arksey and O’Malley’s framework. To investigate the evidence on the effects of Kinect-based rehabilitation, a search was executed in five databases (Web of Science, PubMed, Cochrane Library, Scopus, and IEEE) from 2010 to 2020. Results. Thirty-three articles were finally selected by the inclusion criteria. Most of the studies had been conducted in the US (22%). In terms of the application of Kinect-based rehabilitation for stroke patients, most studies had focused on the rehabilitation of upper extremities (55%), followed by balance (27%). The majority of the studies had developed customized rehabilitation programs (36%) for the rehabilitation of stroke patients. Most of these studies had noted that the simultaneous use of Kinect-based rehabilitation and other physiotherapy methods has a more noticeable effect on performance improvement in patients. Conclusion. The simultaneous application of Kinect-based rehabilitation and other physiotherapy methods has a stronger effect on the performance improvement of stroke patients. Better effects can be achieved by designing Kinect-based rehabilitation programs tailored to the characteristics and abilities of stroke patients.
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Mahboobeh DJ, Dias SB, Khandoker AH, Hadjileontiadis LJ. Machine Learning-Based Analysis of Digital Movement Assessment and ExerGame Scores for Parkinson's Disease Severity Estimation. Front Psychol 2022; 13:857249. [PMID: 35369199 PMCID: PMC8974120 DOI: 10.3389/fpsyg.2022.857249] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Accepted: 02/03/2022] [Indexed: 01/06/2023] Open
Abstract
Neurodegenerative Parkinson's Disease (PD) is one of the common incurable diseases among the elderly. Clinical assessments are characterized as standardized means for PD diagnosis. However, relying on medical evaluation of a patient's status can be subjective to physicians' experience, making the assessment process susceptible to human errors. The use of ICT-based tools for capturing the status of patients with PD can provide more objective and quantitative metrics. In this vein, the Personalized Serious Game Suite (PGS) and intelligent Motor Assessment Tests (iMAT), produced within the i-PROGNOSIS European project (www.i-prognosis.eu), are explored in the current study. More specifically, data from 27 patients with PD at Stage 1 (9) and Stage 3 (18) produced from their interaction with PGS/iMAT are analyzed. Five feature vector (FV) scenarios are set, including features from PGS or iMAT scores or their combination, after also taking into consideration the age of patients with PD. These FVs are fed into three machine learning classifiers, i.e., K-Nearest Neighbor (KNN), Support Vector Machines (SVM), and Random Forest (RF), to infer the stage of each patient with PD. A Leave-One-Out Cross-Validation (LOOCV) method is adopted for testing the classification performance. The experimental results show that a high (>90%) classification accuracy is achieved from both data sources (PGS/iMAT), justifying the effectiveness of PGS/iMAT to efficiently reflect the motor skill status of patients with PD and further potentiating PGS/iMAT enhancement with a machine learning a part to infer for the stage of patients with PD. Clearly, this integrated approach provides new opportunities for remote monitoring of the stage of patients with PD, contributing to a more efficient organization and set up of personalized interventions.
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Affiliation(s)
- Dunia J. Mahboobeh
- Department of Electrical Engineering and Computer Science, Khalifa University, Abu Dhabi, United Arab Emirates
| | - Sofia B. Dias
- CIPER, Faculdade de Motricidade Humana, Universidade de Lisboa, Lisbon, Portugal
| | - Ahsan H. Khandoker
- Department of Biomedical Engineering, Khalifa University, Abu Dhabi, United Arab Emirates
- Healthcare Engineering Innovation Center (HEIC), Khalifa University, Abu Dhabi, United Arab Emirates
| | - Leontios J. Hadjileontiadis
- Department of Biomedical Engineering, Khalifa University, Abu Dhabi, United Arab Emirates
- Healthcare Engineering Innovation Center (HEIC), Khalifa University, Abu Dhabi, United Arab Emirates
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece
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Chien HF, Zonta MB, Chen J, Diaferia G, Viana CF, Teive HAG, Pedroso JL, Barsottini OGP. Rehabilitation in patients with cerebellar ataxias. ARQUIVOS DE NEURO-PSIQUIATRIA 2022; 80:306-315. [DOI: 10.1590/0004-282x-anp-2021-0065] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Accepted: 05/13/2021] [Indexed: 11/22/2022]
Abstract
ABSTRACT Cerebellar ataxias comprise a heterogeneous group of diseases characterized by motor and non-motor symptoms, which can be acquired, degenerative, or have a genetic cause, such as spinocerebellar ataxias (SCA). Usually, the genetic and neurodegenerative forms of cerebellar ataxias present a progressive and inevitable worsening of the clinical picture so that rehabilitation treatment is fundamental. Rehabilitation treatment includes physical therapy, respiratory therapy, speech, voice and swallowing therapy, occupational therapy, and new technologies, such as the use of exergames. The current treatment of patients with cerebellar ataxias, especially neurodegenerative forms, genetic or not, should include these different forms of rehabilitation, with the main objective of improving the quality of life of patients.
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McLaren S, Evans W, Galna B, Portas M, Weston M, Spears I. Fast reconstruction of centre of mass and foot kinematics during a single-legged horizontal jump: A point-cloud processing approach. J Biomech 2022; 135:111015. [DOI: 10.1016/j.jbiomech.2022.111015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Revised: 12/23/2021] [Accepted: 02/17/2022] [Indexed: 10/19/2022]
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Tian H, Ma X, Wu H, Li Y. Skeleton-based abnormal gait recognition with spatio-temporal attention enhanced gait-structural graph convolutional networks. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2021.12.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Park C, An Y, Yoon H, Park I, Kim K, Cha Y. Comparative accuracy of a shoulder range motion measurement sensor and Vicon 3D motion capture for shoulder abduction in frozen shoulder. Technol Health Care 2022; 30:251-257. [PMID: 35124602 PMCID: PMC9028631 DOI: 10.3233/thc-228024] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND: Although patients with frozen shoulders have the range of motion (ROM) of their shoulder’s abduction movements measured at hospital and the physical therapy visits, multiple visits to check for progress is often difficult. Thus, we developed an artificial intelligence-based image recognition detectable sensor (AIRDS) intended for easy use at home. OBJECTIVE: The purpose of this study was to determine the accuracy of a sensor (AIRDS) measuring shoulder abduction angle, thus offering a valid and feasible system for monitoring patients with frozen shoulder. METHODS: Ten patients with frozen shoulder (5 males, 5 females) performed shoulder joint movements while being measured with the AIRDS system and the 3-dimensional Vicon system. The measure of the outcome included the linear regression of the shoulder abduction joint kinematics. RESULTS: Linear regression analysis of the AIRDS system and the Vicon system demonstrated a significant correlation coefficient of R2= 0.9979 (P< 0.05). CONCLUSIONS: Our results provide novel, promising evidence that AIRDS can accurately measure the timing and total spatial characteristics of clinical movements. AIRDS is designed to provide real-time ROM measurements for joint mobility using artificial intelligence instead of the judgement of the physical therapist.
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Affiliation(s)
- Chanhee Park
- Department of Physical Therapy, Yonsei University, Wonju, Korea
- Funrehab Co., Ltd, Daejoeon, Korea
| | | | - Hyunsik Yoon
- Department of Physical Therapy, Chungnam National University Hospital, Daejeon, Korea
| | - Ilbong Park
- Department of Sports Rehabilitation, Busan University of Foreign Studies, Busan, Korea
| | - Kyoungtae Kim
- Department of Physical Therapy, Cheju Halla University, Jeju, Korea
| | - Youngjoo Cha
- Department of Physical Therapy, Cheju Halla University, Jeju, Korea
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Cimolin V, Vismara L, Ferraris C, Amprimo G, Pettiti G, Lopez R, Galli M, Cremascoli R, Sinagra S, Mauro A, Priano L. Computation of Gait Parameters in Post Stroke and Parkinson's Disease: A Comparative Study Using RGB-D Sensors and Optoelectronic Systems. SENSORS 2022; 22:s22030824. [PMID: 35161570 PMCID: PMC8839392 DOI: 10.3390/s22030824] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 01/07/2022] [Accepted: 01/20/2022] [Indexed: 02/04/2023]
Abstract
The accurate and reliable assessment of gait parameters is assuming an important role, especially in the perspective of designing new therapeutic and rehabilitation strategies for the remote follow-up of people affected by disabling neurological diseases, including Parkinson’s disease and post-stroke injuries, in particular considering how gait represents a fundamental motor activity for the autonomy, domestic or otherwise, and the health of neurological patients. To this end, the study presents an easy-to-use and non-invasive solution, based on a single RGB-D sensor, to estimate specific features of gait patterns on a reduced walking path compatible with the available spaces in domestic settings. Traditional spatio-temporal parameters and features linked to dynamic instability during walking are estimated on a cohort of ten parkinsonian and eleven post-stroke subjects using a custom-written software that works on the result of a body-tracking algorithm. Then, they are compared with the “gold standard” 3D instrumented gait analysis system. The statistical analysis confirms no statistical difference between the two systems. Data also indicate that the RGB-D system is able to estimate features of gait patterns in pathological individuals and differences between them in line with other studies. Although they are preliminary, the results suggest that this solution could be clinically helpful in evolutionary disease monitoring, especially in domestic and unsupervised environments where traditional gait analysis is not usable.
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Affiliation(s)
- Veronica Cimolin
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy; (V.C.); (R.L.); (M.G.)
| | - Luca Vismara
- Istituto Auxologico Italiano, IRCCS, Department of Neurology and Neurorehabilitation, S. Giuseppe Hospital, 28824 Piancavallo, Italy; (L.V.); (R.C.); (S.S.); (A.M.)
- Department of Neurosciences, University of Turin, Via Cherasco 15, 10100 Torino, Italy
| | - Claudia Ferraris
- Institute of Electronics, Computer and Telecommunication Engineering, National Research Council, Corso Duca degli Abruzzi 24, 10129 Torino, Italy; (C.F.); (G.A.); (G.P.)
| | - Gianluca Amprimo
- Institute of Electronics, Computer and Telecommunication Engineering, National Research Council, Corso Duca degli Abruzzi 24, 10129 Torino, Italy; (C.F.); (G.A.); (G.P.)
- Department of Control and Computer Engineering, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy
| | - Giuseppe Pettiti
- Institute of Electronics, Computer and Telecommunication Engineering, National Research Council, Corso Duca degli Abruzzi 24, 10129 Torino, Italy; (C.F.); (G.A.); (G.P.)
| | - Roberto Lopez
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy; (V.C.); (R.L.); (M.G.)
- Department of Electrical Engineering, Universidad de Concepción, Víctor Lamas 1290, Concepción 4030000, Chile
| | - Manuela Galli
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy; (V.C.); (R.L.); (M.G.)
| | - Riccardo Cremascoli
- Istituto Auxologico Italiano, IRCCS, Department of Neurology and Neurorehabilitation, S. Giuseppe Hospital, 28824 Piancavallo, Italy; (L.V.); (R.C.); (S.S.); (A.M.)
- Department of Neurosciences, University of Turin, Via Cherasco 15, 10100 Torino, Italy
| | - Serena Sinagra
- Istituto Auxologico Italiano, IRCCS, Department of Neurology and Neurorehabilitation, S. Giuseppe Hospital, 28824 Piancavallo, Italy; (L.V.); (R.C.); (S.S.); (A.M.)
| | - Alessandro Mauro
- Istituto Auxologico Italiano, IRCCS, Department of Neurology and Neurorehabilitation, S. Giuseppe Hospital, 28824 Piancavallo, Italy; (L.V.); (R.C.); (S.S.); (A.M.)
- Department of Neurosciences, University of Turin, Via Cherasco 15, 10100 Torino, Italy
| | - Lorenzo Priano
- Istituto Auxologico Italiano, IRCCS, Department of Neurology and Neurorehabilitation, S. Giuseppe Hospital, 28824 Piancavallo, Italy; (L.V.); (R.C.); (S.S.); (A.M.)
- Department of Neurosciences, University of Turin, Via Cherasco 15, 10100 Torino, Italy
- Correspondence: ; Tel.: +39-0323-514-392
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Hakkala A, Koskinen J. Personal data protection in the age of mass surveillance. JOURNAL OF COMPUTER SECURITY 2022. [DOI: 10.3233/jcs-200033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
We present a solution to data ownership in the surveillance age in the form of an ethically sustainable framework for managing personal and person-derived data. This framework is based on the concept of Datenherrschaft – mastery over data that all natural persons should have on data they themselves produce or is derived thereof. We give numerous examples and tie cases to robust ethical analysis, and also discuss technological dimensions.
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Affiliation(s)
- Antti Hakkala
- Department of Computing, University of Turku, Finland. E-mail:
| | - Jani Koskinen
- Turku School of Economics, University of Turku, Finland. E-mail:
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Ono H, Hori Y, Tsunemi M, Matsuzaki I, Hayashi K, Kamijima M, Ebara T. Promoting endoscopists' health through cutting-edge motion analysis technology: Accuracy and precision of ergonomic motion tracking system for endoscopy suite (EMTES). J Occup Health 2022; 64:e12355. [PMID: 36069285 PMCID: PMC9449985 DOI: 10.1002/1348-9585.12355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Revised: 07/29/2022] [Accepted: 08/22/2022] [Indexed: 11/14/2022] Open
Abstract
OBJECTIVES Endoscopists often suffer from musculoskeletal disorders due to posture-specific workloads imposed by precise maneuvering or long procedural duration. An ergonomic motion tracking system for endoscopy suite (EMTES) was developed using Azure Kinect sensors to evaluate the occlusion, accuracy, and precision, focusing mainly on upper and lower limb movements. METHODS Three healthy male participants pointed the prescribed points for 5 s on the designated work envelopes and their coordinates were measured. The mean occlusion rate (%) of the 32 motion tracking landmarks, standard deviation (SD) of distance and orientation, and partial regression coefficient (β) and R2 model fit for accuracy were calculated using the time series of coordinates data of the upper/lower limb movements. RESULTS The mean occlusion rate was 5.2 ± 10.6% and 1.6 ± 1.4% for upper and lower limb movements, respectively. Of the 32 landmarks, 28 (87.5%) had occlusion rates of 10% or less. The mean SDs of 4.2 mm for distance and 1.2° for orientation were found. Most of the R2 values were over 0.9. In the case of right upper/lower limb measurement for orientation, β coefficients ranged from 0.82 to 1.36. CONCLUSION EMTES is reliable in calculating occlusion, precision, and accuracy for practical motion-tracking measurements in endoscopists.
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Affiliation(s)
- Hiroaki Ono
- Department of Occupational and Environmental HealthNagoya City University Graduate School of Medical Sciences/Medical SchoolNagoyaJapan
| | - Yasuki Hori
- Department of Gastroenterology and MetabolismNagoya City University Graduate School of Medical SciencesNagoyaJapan
| | - Mafu Tsunemi
- Department of Occupational and Environmental HealthNagoya City University Graduate School of Medical Sciences/Medical SchoolNagoyaJapan
- Department of NursingYamashita HospitalIchinomiyaJapan
| | - Ippei Matsuzaki
- Department of GastroenterologyYamashita HospitalIchinomiyaJapan
| | - Kazuki Hayashi
- Department of Gastroenterology and MetabolismNagoya City University Graduate School of Medical SciencesNagoyaJapan
| | - Michihiro Kamijima
- Department of Occupational and Environmental HealthNagoya City University Graduate School of Medical Sciences/Medical SchoolNagoyaJapan
| | - Takeshi Ebara
- Department of Occupational and Environmental HealthNagoya City University Graduate School of Medical Sciences/Medical SchoolNagoyaJapan
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Portable, open-source solutions for estimating wrist position during reaching in people with stroke. Sci Rep 2021; 11:22491. [PMID: 34795346 PMCID: PMC8602299 DOI: 10.1038/s41598-021-01805-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2021] [Accepted: 10/26/2021] [Indexed: 12/29/2022] Open
Abstract
Arm movement kinematics may provide a more sensitive way to assess neurorehabilitation outcomes than existing metrics. However, measuring arm kinematics in people with stroke can be challenging for traditional optical tracking systems due to non-ideal environments, expense, and difficulty performing required calibration. Here, we present two open-source methods, one using inertial measurement units (IMUs) and another using virtual reality (Vive) sensors, for accurate measurements of wrist position with respect to the shoulder during reaching movements in people with stroke. We assessed the accuracy of each method during a 3D reaching task. We also demonstrated each method's ability to track two metrics derived from kinematics-sweep area and smoothness-in people with chronic stroke. We computed correlation coefficients between the kinematics estimated by each method when appropriate. Compared to a traditional optical tracking system, both methods accurately tracked the wrist during reaching, with mean signed errors of 0.09 ± 1.81 cm and 0.48 ± 1.58 cm for the IMUs and Vive, respectively. Furthermore, both methods' estimated kinematics were highly correlated with each other (p < 0.01). By using relatively inexpensive wearable sensors, these methods may be useful for developing kinematic metrics to evaluate stroke rehabilitation outcomes in both laboratory and clinical environments.
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Hernandez-Gomez JC, Restrepo-Martínez A, Valencia-Aguirre J. Descripción del movimiento humano basado en el marco de Frenet Serret y datos tipo MOCAP. REVISTA POLITÉCNICA 2021. [DOI: 10.33571/rpolitec.v17n34a11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022] Open
Abstract
Clasificar el movimiento humano se ha convertido en una necesidad tecnológica, en donde para definir la posición de un sujeto requiere identificar el recorrido de las extremidades y el tronco del cuerpo, y tener la capacidad de diferenciar esta posición respecto a otros sujetos o movimientos, generándose la necesidad tener datos y algoritmos que faciliten su clasificación. Es así, como en este trabajo, se evalúa la capacidad discriminante de datos de captura de movimiento en rehabilitación física, donde la posición de los sujetos es adquirida con el Kinect de Microsoft y marcadores ópticos, y atributos del movimiento generados con el marco de Frenet Serret, evaluando su capacidad discriminante con los algoritmos máquinas de soporte vectorial, redes neuronales y k vecinos más cercanos. Los resultados presentan porcentajes de acierto del 93.5% en la clasificación con datos obtenidos del Kinect, y un éxito del 100% para los movimientos con marcadores ópticos.
Classify human movement has become a technological necessity, where defining the position of a subject requires identifying the trajectory of the limbs and trunk of the body, having the ability to differentiate this position from other subjects or movements, which generates the need to have data and algorithms that help their classification. Therefore, the discriminant capacity of motion capture data in physical rehabilitation is evaluated, where the position of the subjects is acquired with the Microsoft Kinect and optical markers. Attributes of the movement generated with the Frenet Serret framework. Evaluating their discriminant capacity by means of support vector machines, neural networks, and k nearest neighbors algorithms. The obtained results present an accuracy of 93.5% in the classification with data obtained from the Kinect, and success of 100% for movements where the position is defined with optical markers.
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Virtual Interface with Kinect 3D Sensor for Interaction with Bedridden People. INTERNATIONAL JOURNAL OF HEALTHCARE INFORMATION SYSTEMS AND INFORMATICS 2021. [DOI: 10.4018/ijhisi.294114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The human-machine interaction has evolved significantly in the last years, allowing a new range of opportunities for developing solutions for people with physical limitations. Natural user interfaces (NUI) allow bedridden and/or physically disabled people to perform a set of actions trough gestures thus increasing their quality of life and autonomy. This paper presents a solution based on image processing and computer vision using the Kinect 3D sensor for development of applications that recognize gestures made by the human hand. The gestures are then identified by a software application that triggers a set of actions of upmost importance for the bedridden person, for example, trigger the emergency, switch on/off the TV or control the bed slope. It was used a shape matching technique for six gestures recognition, being the final actions activated by the Arduino platform. The results show a success rate of 96%. This system can improve the quality of life and autonomy of bedridden people, being able to be adapted for the specific necessities of an individual subject.
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Yu RWL, Chan AHS. Meta-analysis of the effects of game types and devices on older adults-video game interaction: Implications for video game training on cognition. APPLIED ERGONOMICS 2021; 96:103477. [PMID: 34107433 DOI: 10.1016/j.apergo.2021.103477] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Revised: 05/24/2021] [Accepted: 05/25/2021] [Indexed: 06/12/2023]
Abstract
Video game training can effectively improve the cognition of older adults. However, whether video game types and game devices influence the training effects of video games remains controversial. This meta-analysis aimed to access and evaluate the effects of video game types and game devices in video game training on the cognition of older adults. Interestingly, results indicated that mouse/keyboard was superior over other video game devices on perceptual-motor function. The effect size (Hedge's g) for perceptual-motor function decreased by 1.777 and 1.722 when the video game training device changed from mouse/keyboard to driving simulator and motion controller. The effects of cognitive training game and conventional video game were moderated by session length. More well-designed studies are required to clarify the unique efficacy of video game types and devices for older adults with video game training.
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Affiliation(s)
- Rita Wing Lam Yu
- Department of Advanced Design and Systems Engineering, City University of Hong Kong, Kowloon, Hong Kong.
| | - Alan Hoi Shou Chan
- Department of Advanced Design and Systems Engineering, City University of Hong Kong, Kowloon, Hong Kong.
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Trinidad-Fernández M, Cuesta-Vargas A, Vaes P, Beckwée D, Moreno FÁ, González-Jiménez J, Fernández-Nebro A, Manrique-Arija S, Ureña-Garnica I, González-Sánchez M. Human motion capture for movement limitation analysis using an RGB-D camera in spondyloarthritis: a validation study. Med Biol Eng Comput 2021; 59:2127-2137. [PMID: 34467447 PMCID: PMC8440303 DOI: 10.1007/s11517-021-02406-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Accepted: 07/01/2021] [Indexed: 11/30/2022]
Abstract
A human motion capture system using an RGB-D camera could be a good option to understand the trunk limitations in spondyloarthritis. The aim of this study is to validate a human motion capture system using an RGB-D camera to analyse trunk movement limitations in spondyloarthritis patients. Cross-sectional study was performed where spondyloarthritis patients were diagnosed with a rheumatologist. The RGB-D camera analysed the kinematics of each participant during seven functional tasks based on rheumatologic assessment. The OpenNI2 library collected the depth data, the NiTE2 middleware detected a virtual skeleton and the MRPT library recorded the trunk positions. The gold standard was registered using an inertial measurement unit. The outcome variables were angular displacement, angular velocity and lineal acceleration of the trunk. Criterion validity and the reliability were calculated. Seventeen subjects (54.35 (11.75) years) were measured. The Bending task obtained moderate results in validity (r = 0.55-0.62) and successful results in reliability (ICC = 0.80-0.88) and validity and reliability of angular kinematic results in Chair task were moderate and (r = 0.60-0.74, ICC = 0.61-0.72). The kinematic results in Timed Up and Go test were less consistent. The RGB-D camera was documented to be a reliable tool to assess the movement limitations in spondyloarthritis depending on the functional tasks: Bending task. Chair task needs further research and the TUG analysis was not validated. Comparation of both systems, required software for camera analysis, outcomes and final results of validity and reliability of each test.
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Affiliation(s)
- Manuel Trinidad-Fernández
- Departamento de Fisioterapia, Instituto de Biomedicina de Málaga (IBIMA), Universidad de Málaga, Grupo de Clinimetría (F-14), Málaga, Spain
- Rehabilitation Sciences Research Department, Vrije Universiteit Brussel, Brussels, Belgium
| | - Antonio Cuesta-Vargas
- Departamento de Fisioterapia, Instituto de Biomedicina de Málaga (IBIMA), Universidad de Málaga, Grupo de Clinimetría (F-14), Málaga, Spain.
- School of Clinical Sciences, Faculty of Health, Queensland University of Technology, Brisbane, QLD, Australia.
| | - Peter Vaes
- Rehabilitation Sciences Research Department, Vrije Universiteit Brussel, Brussels, Belgium
| | - David Beckwée
- Rehabilitation Sciences Research Department, Vrije Universiteit Brussel, Brussels, Belgium
- Department of Rehabilitation Sciences and Physiotherapy, University of Antwerp, Antwerp, Belgium
| | - Francisco-Ángel Moreno
- MAPIR-UMA Group, Department Ingeniería de Sistemas Y Automática, Instituto de Investigación Biomédico de Málaga (IBIMA), Universidad de Málaga, Málaga, Spain
| | - Javier González-Jiménez
- MAPIR-UMA Group, Department Ingeniería de Sistemas Y Automática, Instituto de Investigación Biomédico de Málaga (IBIMA), Universidad de Málaga, Málaga, Spain
| | - Antonio Fernández-Nebro
- UGC de Reumatología, Instituto de Investigación Biomédica de Málaga (IBIMA) Hospital Regional Universitario de Málaga, Universidad de Málaga, Málaga, Spain
| | - Sara Manrique-Arija
- UGC de Reumatología, Instituto de Investigación Biomédica de Málaga (IBIMA) Hospital Regional Universitario de Málaga, Universidad de Málaga, Málaga, Spain
| | - Inmaculada Ureña-Garnica
- UGC de Reumatología, Instituto de Investigación Biomédica de Málaga (IBIMA) Hospital Regional Universitario de Málaga, Universidad de Málaga, Málaga, Spain
| | - Manuel González-Sánchez
- Departamento de Fisioterapia, Instituto de Biomedicina de Málaga (IBIMA), Universidad de Málaga, Grupo de Clinimetría (F-14), Málaga, Spain
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Wei L, Chung CS, Koontz AM. Automating the Clinical Assessment of Independent Wheelchair Sitting Pivot Transfer Techniques. Top Spinal Cord Inj Rehabil 2021; 27:1-11. [PMID: 34456542 DOI: 10.46292/sci20-00050] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
Background Using proper transfer technique can help to reduce forces and prevent secondary injuries. However, current assessment tools rely on the ability to subjectively identify harmful movement patterns. Objectives The purpose of the study was to determine the accuracy of using a low-cost markerless motion capture camera and machine learning methods to evaluate the quality of independent wheelchair sitting pivot transfers. We hypothesized that the algorithms would be able to discern proper (low risk) and improper (high risk) wheelchair transfer techniques in accordance with component items on the Transfer Assessment Instrument (TAI). Methods Transfer motions of 91 full-time wheelchair users were recorded and used to develop machine learning classifiers that could be used to discern proper from improper technique. The data were labeled using the TAI item scores. Eleven out of 18 TAI items were evaluated by the classifiers. Motion variables from the Kinect were inputted as the features. Random forests and k-nearest neighbors algorithms were chosen as the classifiers. Eighty percent of the data were used for model training and hyperparameter turning. The validation process was performed using 20% of the data as the test set. Results The area under the receiver operating characteristic curve of the test set for each item was over 0.79. After adjusting the decision threshold, the precisions of the models were over 0.87, and the model accuracies were over 71%. Conclusion The results show promise for the objective assessment of the transfer technique using a low cost camera and machine learning classifiers.
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Affiliation(s)
- Lin Wei
- Human Engineering Research Laboratories, Rehabilitation Research and Development Service, Department of Veterans Affairs, Pittsburgh, PA.,Department of Rehabilitation Science and Technology, University of Pittsburgh, PA
| | - Cheng-Shiu Chung
- Human Engineering Research Laboratories, Rehabilitation Research and Development Service, Department of Veterans Affairs, Pittsburgh, PA
| | - Alicia M Koontz
- Human Engineering Research Laboratories, Rehabilitation Research and Development Service, Department of Veterans Affairs, Pittsburgh, PA.,Department of Rehabilitation Science and Technology, University of Pittsburgh, PA.,Department of Bioengineering, University of Pittsburgh, PA
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