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Davey N, Harte G, Boran A, Mc Elwaine P, Kennelly SP. GaitKeeper: An AI-Enabled Mobile Technology to Standardize and Measure Gait Speed. SENSORS (BASEL, SWITZERLAND) 2024; 24:5550. [PMID: 39275462 PMCID: PMC11398007 DOI: 10.3390/s24175550] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/13/2024] [Revised: 08/06/2024] [Accepted: 08/07/2024] [Indexed: 09/16/2024]
Abstract
Gait speed is increasingly recognized as an important health indicator. However, gait analysis in clinical settings often encounters inconsistencies due to methodological variability and resource constraints. To address these challenges, GaitKeeper uses artificial intelligence (AI) and augmented reality (AR) to standardize gait speed assessments. In laboratory conditions, GaitKeeper demonstrates close alignment with the Vicon system and, in clinical environments, it strongly correlates with the Gaitrite system. The integration of a cloud-based processing platform and robust data security positions GaitKeeper as an accurate, cost-effective, and user-friendly tool for gait assessment in diverse clinical settings.
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Affiliation(s)
- Naomi Davey
- Institute of Memory and Cognition, Tallaght University Hospital, D24 NR0A Dublin, Ireland
- Department of Medical Gerontology, School of Medicine, Trinity College Dublin, 2 Dublin, Ireland
| | - Gillian Harte
- Institute of Memory and Cognition, Tallaght University Hospital, D24 NR0A Dublin, Ireland
- Department of Physiotherapy, Tallaght University Hospital, D24 NR0A Dublin, Ireland
| | - Aidan Boran
- Insight Centre, Dublin City University, Collins Ave Ext, Whitehall, 9 Dublin, Ireland
- Digital Gait Labs, Glasnevin, 9 Dublin, Ireland
| | - Paul Mc Elwaine
- Institute of Memory and Cognition, Tallaght University Hospital, D24 NR0A Dublin, Ireland
- Department of Medical Gerontology, School of Medicine, Trinity College Dublin, 2 Dublin, Ireland
| | - Seán P Kennelly
- Institute of Memory and Cognition, Tallaght University Hospital, D24 NR0A Dublin, Ireland
- Department of Medical Gerontology, School of Medicine, Trinity College Dublin, 2 Dublin, Ireland
- Digital Gait Labs, Glasnevin, 9 Dublin, Ireland
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2
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Hesse N, Baumgartner S, Gut A, Van Hedel HJA. Concurrent Validity of Motion Parameters Measured With an RGB-D Camera-Based Markerless 3D Motion Tracking Method in Children and Young Adults. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2024; 12:580-588. [PMID: 39155921 PMCID: PMC11329219 DOI: 10.1109/jtehm.2024.3435334] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Revised: 07/12/2024] [Accepted: 07/23/2024] [Indexed: 08/20/2024]
Abstract
OBJECTIVE Low-cost, portable RGB-D cameras with integrated motion tracking functionality enable easy-to-use 3D motion analysis without requiring expensive facilities and specialized personnel. However, the accuracy of existing systems is insufficient for most clinical applications, particularly when applied to children. In previous work, we developed an RGB-D camera-based motion tracking method and showed that it accurately captures body joint positions of children and young adults in 3D. In this study, the validity and accuracy of clinically relevant motion parameters that were computed from kinematics of our motion tracking method are evaluated in children and young adults. METHODS Twenty-three typically developing children and healthy young adults (5-29 years, 110-189 cm) performed five movement tasks while being recorded simultaneously with a marker-based Vicon system and an Azure Kinect RGB-D camera. Motion parameters were computed from the extracted kinematics of both methods: time series measurements, i.e., measurements over time, peak measurements, i.e., measurements at a single time instant, and movement smoothness. The agreement of these parameter values was evaluated using Pearson's correlation coefficients r for time series data, and mean absolute error (MAE) and Bland-Altman plots with limits of agreement for peak measurements and smoothness. RESULTS Time series measurements showed strong to excellent correlations (r-values between 0.8 and 1.0), MAE for angles ranged from 1.5 to 5 degrees and for smoothness parameters (SPARC) from 0.02-0.09, while MAE for distance-related parameters ranged from 9 to 15 mm. CONCLUSION Extracted motion parameters are valid and accurate for various movement tasks in children and young adults, demonstrating the suitability of our tracking method for clinical motion analysis. CLINICAL IMPACT The low-cost portable hardware in combination with our tracking method enables motion analysis outside of specialized facilities while providing measurements that are close to those of the clinical gold-standard.
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Affiliation(s)
- Nikolas Hesse
- Swiss Children’s RehabUniversity Children’s Hospital Zurich8910Affoltern am AlbisSwitzerland
- Children’s Research CenterUniversity Children’s Hospital Zurich, University of Zurich8032ZürichSwitzerland
| | - Sandra Baumgartner
- Swiss Children’s RehabUniversity Children’s Hospital Zurich8910Affoltern am AlbisSwitzerland
- Children’s Research CenterUniversity Children’s Hospital Zurich, University of Zurich8032ZürichSwitzerland
| | - Anja Gut
- Swiss Children’s RehabUniversity Children’s Hospital Zurich8910Affoltern am AlbisSwitzerland
- Children’s Research CenterUniversity Children’s Hospital Zurich, University of Zurich8032ZürichSwitzerland
| | - Hubertus J. A. Van Hedel
- Swiss Children’s RehabUniversity Children’s Hospital Zurich8910Affoltern am AlbisSwitzerland
- Children’s Research CenterUniversity Children’s Hospital Zurich, University of Zurich8032ZürichSwitzerland
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Seifallahi M, Galvin JE, Ghoraani B. Detection of mild cognitive impairment using various types of gait tests and machine learning. Front Neurol 2024; 15:1354092. [PMID: 39055321 PMCID: PMC11269186 DOI: 10.3389/fneur.2024.1354092] [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: 12/11/2023] [Accepted: 06/27/2024] [Indexed: 07/27/2024] Open
Abstract
Introduction Alzheimer's disease and related disorders (ADRD) progressively impair cognitive function, prompting the need for early detection to mitigate its impact. Mild Cognitive Impairment (MCI) may signal an early cognitive decline due to ADRD. Thus, developing an accessible, non-invasive method for detecting MCI is vital for initiating early interventions to prevent severe cognitive deterioration. Methods This study explores the utility of analyzing gait patterns, a fundamental aspect of human motor behavior, on straight and oval paths for diagnosing MCI. Using a Kinect v.2 camera, we recorded the movements of 25 body joints from 25 individuals with MCI and 30 healthy older adults (HC). Signal processing, descriptive statistical analysis, and machine learning techniques were employed to analyze the skeletal gait data in both walking conditions. Results and discussion The study demonstrated that both straight and oval walking patterns provide valuable insights for MCI detection, with a notable increase in identifiable gait features in the more complex oval walking test. The Random Forest model excelled among various algorithms, achieving an 85.50% accuracy and an 83.9% F-score in detecting MCI during oval walking tests. This research introduces a cost-effective, Kinect-based method that integrates gait analysis-a key behavioral pattern-with machine learning, offering a practical tool for MCI screening in both clinical and home environments.
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Affiliation(s)
- Mahmoud Seifallahi
- Department of Computer and Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL, United States
| | - James E. Galvin
- Comprehensive Center for Brain Health, Department of Neurology, University of Miami, Boca Raton, FL, United States
| | - Behnaz Ghoraani
- Department of Computer and Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL, United States
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Scataglini S, Abts E, Van Bocxlaer C, Van den Bussche M, Meletani S, Truijen S. Accuracy, Validity, and Reliability of Markerless Camera-Based 3D Motion Capture Systems versus Marker-Based 3D Motion Capture Systems in Gait Analysis: A Systematic Review and Meta-Analysis. SENSORS (BASEL, SWITZERLAND) 2024; 24:3686. [PMID: 38894476 PMCID: PMC11175331 DOI: 10.3390/s24113686] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/21/2024] [Revised: 05/22/2024] [Accepted: 05/30/2024] [Indexed: 06/21/2024]
Abstract
(1) Background: Marker-based 3D motion capture systems (MBS) are considered the gold standard in gait analysis. However, they have limitations for which markerless camera-based 3D motion capture systems (MCBS) could provide a solution. The aim of this systematic review and meta-analysis is to compare the accuracy, validity, and reliability of MCBS and MBS. (2) Methods: A total of 2047 papers were systematically searched according to PRISMA guidelines on 7 February 2024, in two different databases: Pubmed (1339) and WoS (708). The COSMIN-tool and EBRO guidelines were used to assess risk of bias and level of evidence. (3) Results: After full text screening, 22 papers were included. Spatiotemporal parameters showed overall good to excellent accuracy, validity, and reliability. For kinematic variables, hip and knee showed moderate to excellent agreement between the systems, while for the ankle joint, poor concurrent validity and reliability were measured. The accuracy and concurrent validity of walking speed were considered excellent in all cases, with only a small bias. The meta-analysis of the inter-rater reliability and concurrent validity of walking speed, step time, and step length resulted in a good-to-excellent intraclass correlation coefficient (ICC) (0.81; 0.98). (4) Discussion and conclusions: MCBS are comparable in terms of accuracy, concurrent validity, and reliability to MBS in spatiotemporal parameters. Additionally, kinematic parameters for hip and knee in the sagittal plane are considered most valid and reliable but lack valid and accurate measurement outcomes in transverse and frontal planes. Customization and standardization of methodological procedures are necessary for future research to adequately compare protocols in clinical settings, with more attention to patient populations.
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Affiliation(s)
- Sofia Scataglini
- 4D4ALL Laboratory, Department of Rehabilitation Sciences and Physiotherapy, Center for Health and Technology (CHaT), Faculty of Medicine and Health Sciences, University of Antwerp, 2000 Antwerpen, Belgium; (E.A.); (C.V.B.); (M.V.d.B.); (S.M.); (S.T.)
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Aleixo P, Atalaia T, Bhudarally M, Miranda P, Castelinho N, Abrantes J. Deep squat test - Functional movement Screen: Convergent validity and ability to discriminate subjects with different levels of joint mobility. J Bodyw Mov Ther 2024; 38:197-204. [PMID: 38763563 DOI: 10.1016/j.jbmt.2023.12.010] [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] [Received: 03/31/2023] [Revised: 11/14/2023] [Accepted: 12/06/2023] [Indexed: 05/21/2024]
Abstract
BACKGROUND Functional Movement Screen (FMS) is an important tool in the assessment of exercise practice. Assuming FMS lacks precise validity for assessing postural deficits, further research is needed to assess whether it is a sufficiently precise tool for analysing joint mobility. Research aims were to evaluate: convergent validity of Deep Squat (DS) - one of FMS tests - regarding joint mobility, using data from a three-dimensional motion analysis as a comparable method; DS's ability to discriminate between subjects with different joint mobility levels. METHODS Sixty subjects were selected (23.6 ± 3.8 years). DS was performed according to FMS guidelines. Subjects' performance in frontal and sagittal planes was recorded by two video cameras and subsequently scored by two FMS-certified evaluators. Three-dimensional motion analyses of DS were acquired by a Vicon Motion Capture System (200 Hz). Ten trials were acquired for each subject. Ankle, knee, hip, and shoulder angular positions in sagittal plane were determined from the FullBody PlugInGait model. Spearman's coefficient examined the correlation between angular positions and DS score. Kruskal-Wallis test was used to assess the DS ability to discriminate between subjects with different joint mobility levels by comparing different scores. RESULTS Negligible to moderate correlations were found between DS score and angular positions (-0.5 < r < 0.5). Only shoulder angular positions showed differences between score "1" and "2" (p < 0.05). Shoulder and hip angular positions showed no differences between score "2" and "3" (p < 0.05). CONCLUSIONS DS yielded low convergent validity regarding joint mobility and did not show the ability to discriminate between subjects with different joint mobility levels.
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Affiliation(s)
- Pedro Aleixo
- Centro de Investigação em Desporto, Educação Física, Exercício e Saúde (CIDEFES), Universidade Lusófona, Av. do Campo Grande 376, 1749-024, Lisbon, Portugal.
| | - Tiago Atalaia
- Physiotherapy, Escola Superior de Saúde da Cruz Vermelha Portuguesa, Av. de Ceuta 1 Edifício Urbiceuta, 1300-125, Lisbon, Portugal.
| | - Maria Bhudarally
- Centro de Investigação em Desporto, Educação Física, Exercício e Saúde (CIDEFES), Universidade Lusófona, Av. do Campo Grande 376, 1749-024, Lisbon, Portugal.
| | - Paulo Miranda
- Faculdade de Educação Física e Desporto, Universidade Lusófona, Av. do Campo Grande 376, 1749-024, Lisbon, Portugal.
| | - Nuno Castelinho
- Metropolitano de Lisboa, Av. Fontes Pereira de Melo, 28 1069-095, Lisbon, Portugal.
| | - João Abrantes
- Centre for Research in Applied Communication, Culture and New Technologies (CICANT), Universidade Lusófona, Av. do Campo Grande 376, 1749-024, Lisbon, Portugal.
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Saito S, Saito M, Kondo M, Kobayashi Y. Gait pattern can alter aesthetic visual impression from a third-person perspective. Sci Rep 2024; 14:6602. [PMID: 38503793 PMCID: PMC10951343 DOI: 10.1038/s41598-024-56318-5] [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: 04/06/2023] [Accepted: 03/05/2024] [Indexed: 03/21/2024] Open
Abstract
Beauty is related to our lives in various ways and examining it from an interdisciplinary approach is essential. People are very concerned with their appearance. A widely accepted beauty ideal is that the thinner an individual is, the more beautiful they are. However, the effect of continuous motion on body form aesthetics is unclear. Additionally, an upright pelvic posture in the sagittal plane during walking seems to affect the aesthetic judgments of female appearance. We directly analyzed the influence of body form and walking pattern on aesthetic visual impressions from a third-person perspective with a two-way analysis of variance. Captured motion data for three conditions-upright pelvis, normal pelvis, and posteriorly tilted pelvic posture-were applied to each of three mannequins, representing thin, standard, and obese body forms. When participants watched stimulus videos of the mannequins walking with various postures, a significantly higher score for aesthetic visual impression was noted for an upright pelvic posture than for a posteriorly tilted pelvic posture, irrespective of body form (F(2, 119) = 79.89, p < 0.001, η2 = 0.54). These findings show that the third-person perspective of beauty can be improved even without being thin by walking with an upright pelvic posture.
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Affiliation(s)
- Sakiko Saito
- Liberal Arts and Sciences, Nippon Institute of Technology, Saitama, Japan.
| | - Momoka Saito
- Faculty of Human Life and Environmental Sciences, Ochanomizu University, Tokyo, Japan
- Department of Transdisciplinary Science and Engineering, School of Environment and Society, Tokyo Institute of Technology, Tokyo, Japan
| | - Megumi Kondo
- Faculty of Human Life and Environmental Sciences, Ochanomizu University, Tokyo, Japan
- Faculty of Core Research, Natural Sciences Division, Ochanomizu University, Tokyo, Japan
| | - Yoshiyuki Kobayashi
- Human Augmentation Research Center, National Institute of Advanced Industrial Science and Technology, Chiba, Japan
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7
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Osuka Y, Takeshima N, Kojima N, Kohama T, Fujita E, Kusunoki M, Kato Y, Brechue WF, Sasai H. Discrimination of Frailty Phenotype by Kinect TM-Based Stepping Parameters. JAR LIFE 2023; 12:100-104. [PMID: 38186668 PMCID: PMC10767484 DOI: 10.14283/jarlife.2023.17] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Accepted: 12/04/2023] [Indexed: 01/09/2024]
Abstract
Background Frailty increases the risk of falling, hospitalization, and premature death, necessitating practical early-detection tools. Objectives To examine the discriminative ability of KinectTM-based stepping parameters for identifying frailty phenotype. Design Population-based cross-sectional study. Setting Eighteen neighborhoods near Tokyo Metropolitan Institute for Geriatrics and Gerontology, Itabashi, Tokyo, Japan. Participants In total, 563 community-dwelling older adults aged ≥75 years without mobility limitations, neurological disease, or dementia were included. Measurements Step number (SN) and knee total movement distance (KMD) during a 20-s stepping test were evaluated using the KinectTM infrared depth sensor. Results The number (%) of participants with frailty were 51 (9.1). The area under the receiver operating characteristic curves (95% confidence interval) of a parameter consisting of SN and KMD for frailty was 0.72 (0.64, 0.79). Conclusions Stepping parameters evaluated using KinectTM provided acceptable ability in identifying frailty phenotype, making it a practical screening tool in primary care and home settings.
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Affiliation(s)
- Y Osuka
- Department of Frailty Research, Center for Gerontology and Social Science, Research Institute, National Center for Geriatrics and Gerontology, Aichi, Japan
- Research Team for Promoting Independence and Mental Health, Tokyo Metropolitan Institute for Geriatrics and Gerontology, Tokyo, Japan
| | - N Takeshima
- Department of Health and Sports Sciences, Asahi University, Gifu, Japan
| | - N Kojima
- Research Team for Promoting Independence and Mental Health, Tokyo Metropolitan Institute for Geriatrics and Gerontology, Tokyo, Japan
| | - T Kohama
- Faculty of Biology-Oriented Science and Technology, Kindai University, Wakayama, Japan
| | - E Fujita
- Department of Sports and Life Science, National Institute of Fitness and Sports in Kanoya, Kagoshima, Japan
| | - M Kusunoki
- Faculty of Biology-Oriented Science and Technology, Kindai University, Wakayama, Japan
| | - Y Kato
- Department of Physical Therapy, Nagoya Women's University, Aichi, Japan
| | - W F Brechue
- Department of Physiology, Kirksville College of Osteopathic Medicine, A.T. Still University of Health Sciences, Missouri, USA
| | - H Sasai
- Department of Frailty Research, Center for Gerontology and Social Science, Research Institute, National Center for Geriatrics and Gerontology, Aichi, Japan
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Chu H, Kim W, Joo S, Park E, Kim YW, Kim CH, Lee S. Validity and Reliability of POM-Checker for Measuring Shoulder Range of Motion in Healthy Participants: A Pilot Single-Center Comparative Study. Methods Protoc 2023; 6:114. [PMID: 38133134 PMCID: PMC10745328 DOI: 10.3390/mps6060114] [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: 09/13/2023] [Revised: 11/22/2023] [Accepted: 11/23/2023] [Indexed: 12/23/2023] Open
Abstract
BACKGROUND The aim of this study was to compare shoulder movement measurements between a Kinect-based markerless ROM assessment device (POM-Checker) and a 3D motion capture analysis system (BTS SMART DX-400). METHODS This was a single-visit clinical trial designed to evaluate the validity and reliability of the POM-Checker. The primary outcome was to assess the equivalence between two measurement devices within the same set of participants, aiming to evaluate the validity of the POM-Checker compared to the gold standard device (3D Motion Analysis System). As this was a pilot study, six participants were included. RESULTS The intraclass correlation coefficient (ICC) and the corresponding 95% confidence intervals (CIs) were used to assess the reproducibility of the measurements. Among the 18 movements analyzed, 16 exhibited ICC values of >0.75, indicating excellent reproducibility. CONCLUSION The results showed that the POM-checker is reliable and validated to measure the range of motion of the shoulder joint.
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Affiliation(s)
- Hongmin Chu
- Department of Internal Medicine and Neuroscience, College of Korean Medicine, Wonkwang University, Iksan 54538, Republic of Korea;
| | - Weonjin Kim
- Team Elysium Inc. R&D Center, Seoul 06682, Republic of Korea; (W.K.); (S.J.); (E.P.); (Y.W.K.)
| | - Seongsu Joo
- Team Elysium Inc. R&D Center, Seoul 06682, Republic of Korea; (W.K.); (S.J.); (E.P.); (Y.W.K.)
| | - Eunsik Park
- Team Elysium Inc. R&D Center, Seoul 06682, Republic of Korea; (W.K.); (S.J.); (E.P.); (Y.W.K.)
| | - Yeong Won Kim
- Team Elysium Inc. R&D Center, Seoul 06682, Republic of Korea; (W.K.); (S.J.); (E.P.); (Y.W.K.)
| | - Cheol-Hyun Kim
- Department of Internal Medicine and Neuroscience, College of Korean Medicine, Wonkwang University, Iksan 54538, Republic of Korea;
- Stroke Korean Medicine Research Center, Wonkwang University, Iksan 54538, Republic of Korea
| | - Sangkwan Lee
- Department of Internal Medicine and Neuroscience, College of Korean Medicine, Wonkwang University, Iksan 54538, Republic of Korea;
- Stroke Korean Medicine Research Center, Wonkwang University, Iksan 54538, Republic of Korea
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Bailey CA, Mir-Orefice A, Uchida TK, Nantel J, Graham RB. Smartwatch-Based Prediction of Single-Stride and Stride-to-Stride Gait Outcomes Using Regression-Based Machine Learning. Ann Biomed Eng 2023; 51:2504-2517. [PMID: 37400746 DOI: 10.1007/s10439-023-03290-2] [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: 01/24/2023] [Accepted: 06/17/2023] [Indexed: 07/05/2023]
Abstract
Spatiotemporal variability during gait is linked to fall risk and could be monitored using wearable sensors. Although many users prefer wrist-worn sensors, most applications position at other sites. We developed and evaluated an application using a consumer-grade smartwatch inertial measurement unit (IMU). Young adults (n = 41) completed seven-minute conditions of treadmill gait at three speeds. Single-stride outcomes (stride time, length, width, and speed) and spatiotemporal variability (coefficient of variation of each single-stride outcome) were recorded using an optoelectronic system, while 232 single- and multi-stride IMU metrics were recorded using an Apple Watch Series 5. These metrics were input to train linear, ridge, support vector machine (SVM), random forest, and extreme gradient boosting (xGB) models of each spatiotemporal outcome. We conducted Model × Condition ANOVAs to explore model sensitivity to speed-related responses. xGB models were best for single-stride outcomes [relative mean absolute error (% error): 7-11%; intraclass correlation coefficient (ICC2,1) 0.60-0.86], and SVM models were best for spatiotemporal variability (% error: 18-22%; ICC2,1 = 0.47-0.64). Spatiotemporal changes with speed were captured by these models (Condition: p < 0.00625). Results support the feasibility of monitoring single-stride and multi-stride spatiotemporal parameters using a smartwatch IMU and machine learning.
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Affiliation(s)
| | | | - Thomas K Uchida
- Department of Mechanical Engineering, University of Ottawa, Ottawa, Canada
| | - Julie Nantel
- School of Human Kinetics, University of Ottawa, Ottawa, Canada
| | - Ryan B Graham
- School of Human Kinetics, University of Ottawa, Ottawa, Canada.
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Clark N, Comerford E. An update on mobility assessment of dogs with musculoskeletal disease. J Small Anim Pract 2023; 64:599-610. [PMID: 37455329 DOI: 10.1111/jsap.13650] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 03/31/2023] [Accepted: 06/10/2023] [Indexed: 07/18/2023]
Abstract
Mobility impairments associated with musculoskeletal diseases, such as osteoarthritis and degenerative joint disease, affect approximately 200,000 dogs annually and pose a notable challenge to canine health and welfare. Osteoarthritis causes the remodelling of synovial joints, alongside inflammation and impaired mechanical function which can be extremely debilitating. Secondary osteoarthritis commonly affects dogs and can be exacerbated by previous joint abnormalities, such as patellar luxation or cranial cruciate ligament rupture. Although musculoskeletal diseases can affect dogs of any age, the early subtle signs of gait abnormalities are perhaps missed by owners, thus, dogs may be in the latter stages of osteoarthritis progression when they are presented to veterinarians. Dogs showing subtle signs of gait abnormalities must be presented to veterinary practices for acute diagnosis to prevent long-term deterioration. Musculoskeletal diseases, such as osteoarthritis and degenerative joint disease, are commonly diagnosed via visible radiographic changes. However, veterinarians can use a combination of subjective and objective clinical scoring systems, such as clinical metrology instruments and gait assessment in conjunction with radiography to aid their diagnosis and longitudinal monitoring of musculoskeletal diseases. These scoring systems may be more sensitive to earlier signs of mobility impairments in dogs, ultimately, promoting increased canine health and welfare by enabling pain reduction, improvement of muscle strength and preservation of joint function. Current canine mobility scoring systems available to veterinarians will be discussed in turn throughout this review for implementation into clinical practice.
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Affiliation(s)
- N Clark
- Institute of Life Course and Medical Sciences, Faculty of Health and Life Sciences, University of Liverpool, William Henry Duncan Building, 6 West Derby Street, Liverpool, L7 8TX, UK
| | - E Comerford
- Institute of Life Course and Medical Sciences, Faculty of Health and Life Sciences, University of Liverpool, William Henry Duncan Building, 6 West Derby Street, Liverpool, L7 8TX, UK
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Zhao Y, Yu L, Fan X, Pang MYC, Tsui KL, Wang H. Design of a Sensor-Technology-Augmented Gait and Balance Monitoring System for Community-Dwelling Older Adults in Hong Kong: A Pilot Feasibility Study. SENSORS (BASEL, SWITZERLAND) 2023; 23:8008. [PMID: 37766060 PMCID: PMC10535689 DOI: 10.3390/s23188008] [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] [Received: 08/16/2023] [Revised: 09/11/2023] [Accepted: 09/18/2023] [Indexed: 09/29/2023]
Abstract
Routine assessments of gait and balance have been recognized as an effective approach for preventing falls by issuing early warnings and implementing appropriate interventions. However, current limited public healthcare resources cannot meet the demand for continuous monitoring of deteriorations in gait and balance. The objective of this study was to develop and evaluate the feasibility of a prototype surrogate system driven by sensor technology and multi-sourced heterogeneous data analytics, for gait and balance assessment and monitoring. The system was designed to analyze users' multi-mode data streams collected via inertial sensors and a depth camera while performing a 3-m timed up and go test, a five-times-sit-to-stand test, and a Romberg test, for predicting scores on clinical measurements by physiotherapists. Generalized regression of sensor data was conducted to build prediction models for gait and balance estimations. Demographic correlations with user acceptance behaviors were analyzed using ordinal logistic regression. Forty-four older adults (38 females) were recruited in this pilot study (mean age = 78.5 years, standard deviation [SD] = 6.2 years). The participants perceived that using the system for their gait and balance monitoring was a good idea (mean = 5.45, SD = 0.76) and easy (mean = 4.95, SD = 1.09), and that the system is useful in improving their health (mean = 5.32, SD = 0.83), is trustworthy (mean = 5.04, SD = 0.88), and has a good fit between task and technology (mean = 4.97, SD = 0.84). In general, the participants showed a positive intention to use the proposed system in their gait and balance management (mean = 5.22, SD = 1.10). Demographic correlations with user acceptance are discussed. This study provides preliminary evidence supporting the feasibility of using a sensor-technology-augmented system to manage the gait and balance of community-dwelling older adults. The intervention is validated as being acceptable, viable, and valuable.
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Affiliation(s)
- Yang Zhao
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen 518000, China;
| | - Lisha Yu
- School of Design, The Hong Kong Polytechnic University, Hong Kong, China;
| | - Xiaomao Fan
- College of Big Data and Internet, Shenzhen Technology University, Shenzhen 518000, China;
| | - Marco Y. C. Pang
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, China;
| | - Kwok-Leung Tsui
- Grado Department of Industrial and Systems Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA;
| | - Hailiang Wang
- School of Design, The Hong Kong Polytechnic University, Hong Kong, China;
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Liu XT, Nikkhoo M, Wang L, Chen CP, Chen HB, Chen CJ, Cheng CH. Feasibility of a kinect-based system in assessing physical function of the elderly for home-based care. BMC Geriatr 2023; 23:495. [PMID: 37587451 PMCID: PMC10429079 DOI: 10.1186/s12877-023-04179-4] [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: 12/08/2022] [Accepted: 07/18/2023] [Indexed: 08/18/2023] Open
Abstract
BACKGROUND With concerns about accurate diagnosis through telehealth, the Kinect sensor offers a reliable solution for movement analysis. However, there is a lack of practical research investigating the suitability of a Kinect-based system as a functional fitness assessment tool in homecare settings. Hence, the objective of this study was to evaluate the feasibility of using a Kinect-based system to assess physical function changes in the elderly. METHODS The study consisted of two phases. Phase one involved 35 young healthy adults, evaluating the reliability and validity of a Kinect-based fitness evaluation compared to traditional physical examination using the intraclass correlation coefficient (ICC). Phase two involved 665 elderly subjects, examining the correlation between the Kinect-based fitness evaluation and physical examination through Pearson's correlation coefficients. A Kinect sensor (Microsoft Xbox One Kinect V2) with customized software was employed to capture and compute the movement of joint centers. Both groups performed seven functional assessments simultaneously monitored by a physical therapist and the Kinect system. System usability and user satisfaction were assessed using the System Usability Scale (SUS) and Questionnaire for User Interface Satisfaction (QUIS), respectively. RESULTS Kinect-based system showed overall moderate to excellent within-day reliability (ICC = 0.633-1.0) and between-day reliability (ICC = 0.686-1.0). The overall agreement between the two devices was highly correlated (r ≧ 0.7) for all functional assessment tests in young healthy adults. The Kinect-based system also showed a high correlation with physical examination for the functional assessments (r = 0.858-0.988) except functional reach (r = 0.484) and walking speed(r = 0.493). The users' satisfaction with the system was excellent (SUS score = 84.4 ± 18.5; QUIS score = 6.5-6.7). CONCLUSIONS The reliability and validity of Kinect for assessing functional performance are generally favorable. Nonetheless, caution is advised when employing Kinect for tasks involving depth changes, such as functional reach and walking speed tests for their moderate validity. However, Kinect's fundamental motion detection capabilities demonstrate its potential for future applications in telerehabilitation in different healthcare settings.
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Affiliation(s)
- Xin-Ting Liu
- School of Physical Therapy and Graduate Institute of Rehabilitation Science, College of Medicine, Chang Gung University, No.259, Wen-Hwa 1st Rd, Kweishan, Taoyuan, Taiwan, R.O.C
| | - Mohammad Nikkhoo
- School of Physical Therapy and Graduate Institute of Rehabilitation Science, College of Medicine, Chang Gung University, No.259, Wen-Hwa 1st Rd, Kweishan, Taoyuan, Taiwan, R.O.C
- Bone and Joint Research Center, Chang Gung Memorial Hospital, Linkou, Taiwan, R.O.C
- Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Lizhen Wang
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Carl Pc Chen
- Department of Physical Medicine & Rehabilitation, Chang Gung Memorial Hospital at Linkou and College of Medicine, Chang Gung University, Taoyuan, Taiwan, R.O.C
| | - Hung-Bin Chen
- School of Physical Therapy and Graduate Institute of Rehabilitation Science, College of Medicine, Chang Gung University, No.259, Wen-Hwa 1st Rd, Kweishan, Taoyuan, Taiwan, R.O.C
| | | | - Chih-Hsiu Cheng
- School of Physical Therapy and Graduate Institute of Rehabilitation Science, College of Medicine, Chang Gung University, No.259, Wen-Hwa 1st Rd, Kweishan, Taoyuan, Taiwan, R.O.C..
- Bone and Joint Research Center, Chang Gung Memorial Hospital, Linkou, Taiwan, R.O.C..
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13
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Guffanti D, Lemus D, Vallery H, Brunete A, Hernando M, Horemans H. Performance of a Mobile 3D Camera to Evaluate Simulated Pathological Gait in Practical Scenarios. SENSORS (BASEL, SWITZERLAND) 2023; 23:6944. [PMID: 37571727 PMCID: PMC10422615 DOI: 10.3390/s23156944] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 07/24/2023] [Accepted: 08/02/2023] [Indexed: 08/13/2023]
Abstract
Three-dimensional (3D) cameras used for gait assessment obviate the need for bodily markers or sensors, making them particularly interesting for clinical applications. Due to their limited field of view, their application has predominantly focused on evaluating gait patterns within short walking distances. However, assessment of gait consistency requires testing over a longer walking distance. The aim of this study is to validate the accuracy for gait assessment of a previously developed method that determines walking spatiotemporal parameters and kinematics measured with a 3D camera mounted on a mobile robot base (ROBOGait). Walking parameters measured with this system were compared with measurements with Xsens IMUs. The experiments were performed on a non-linear corridor of approximately 50 m, resembling the environment of a conventional rehabilitation facility. Eleven individuals exhibiting normal motor function were recruited to walk and to simulate gait patterns representative of common neurological conditions: Cerebral Palsy, Multiple Sclerosis, and Cerebellar Ataxia. Generalized estimating equations were used to determine statistical differences between the measurement systems and between walking conditions. When comparing walking parameters between paired measures of the systems, significant differences were found for eight out of 18 descriptors: range of motion (ROM) of trunk and pelvis tilt, maximum knee flexion in loading response, knee position at toe-off, stride length, step time, cadence; and stance duration. When analyzing how ROBOGait can distinguish simulated pathological gait from physiological gait, a mean accuracy of 70.4%, a sensitivity of 49.3%, and a specificity of 74.4% were found when compared with the Xsens system. The most important gait abnormalities related to the clinical conditions were successfully detected by ROBOGait. The descriptors that best distinguished simulated pathological walking from normal walking in both systems were step width and stride length. This study underscores the promising potential of 3D cameras and encourages exploring their use in clinical gait analysis.
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Affiliation(s)
- Diego Guffanti
- Centro de Investigación en Mecatrónica y Sistemas Interactivos—MIST, Universidad Indoamérica, Av. Machala y Sabanilla, Quito 170103, Ecuador
- Universidad UTE, Av. Mariscal Sucre, Quito 170129, Ecuador
| | - Daniel Lemus
- Department of Rehabilitation Medicine, Erasmus MC, 3000 CA Rotterdam, The Netherlands; (D.L.); (H.V.); (H.H.)
- Faculty of Mechanical, Maritime and Materials Engineering, Delft University of Technology, Mekelweg 2, 2628 CD Delft, The Netherlands
| | - Heike Vallery
- Department of Rehabilitation Medicine, Erasmus MC, 3000 CA Rotterdam, The Netherlands; (D.L.); (H.V.); (H.H.)
- Faculty of Mechanical, Maritime and Materials Engineering, Delft University of Technology, Mekelweg 2, 2628 CD Delft, The Netherlands
| | - Alberto Brunete
- Centre for Automation and Robotics (CAR UPM-CSIC), Universidad Politécnica de Madrid, 28012 Madrid, Spain; (A.B.); (M.H.)
| | - Miguel Hernando
- Centre for Automation and Robotics (CAR UPM-CSIC), Universidad Politécnica de Madrid, 28012 Madrid, Spain; (A.B.); (M.H.)
| | - Herwin Horemans
- Department of Rehabilitation Medicine, Erasmus MC, 3000 CA Rotterdam, The Netherlands; (D.L.); (H.V.); (H.H.)
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14
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Salisu S, Ruhaiyem NIR, Eisa TAE, Nasser M, Saeed F, Younis HA. Motion Capture Technologies for Ergonomics: A Systematic Literature Review. Diagnostics (Basel) 2023; 13:2593. [PMID: 37568956 PMCID: PMC10416907 DOI: 10.3390/diagnostics13152593] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 07/25/2023] [Accepted: 08/02/2023] [Indexed: 08/13/2023] Open
Abstract
Muscular skeletal disorder is a difficult challenge faced by the working population. Motion capture (MoCap) is used for recording the movement of people for clinical, ergonomic and rehabilitation solutions. However, knowledge barriers about these MoCap systems have made them difficult to use for many people. Despite this, no state-of-the-art literature review on MoCap systems for human clinical, rehabilitation and ergonomic analysis has been conducted. A medical diagnosis using AI applies machine learning algorithms and motion capture technologies to analyze patient data, enhancing diagnostic accuracy, enabling early disease detection and facilitating personalized treatment plans. It revolutionizes healthcare by harnessing the power of data-driven insights for improved patient outcomes and efficient clinical decision-making. The current review aimed to investigate: (i) the most used MoCap systems for clinical use, ergonomics and rehabilitation, (ii) their application and (iii) the target population. We used preferred reporting items for systematic reviews and meta-analysis guidelines for the review. Google Scholar, PubMed, Scopus and Web of Science were used to search for relevant published articles. The articles obtained were scrutinized by reading the abstracts and titles to determine their inclusion eligibility. Accordingly, articles with insufficient or irrelevant information were excluded from the screening. The search included studies published between 2013 and 2023 (including additional criteria). A total of 40 articles were eligible for review. The selected articles were further categorized in terms of the types of MoCap used, their application and the domain of the experiments. This review will serve as a guide for researchers and organizational management.
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Affiliation(s)
- Sani Salisu
- School of Computer Sciences, Universiti Sains Malaysia, Gelugor 11800, Malaysia;
- Department of Information Technology, Federal University Dutse, Dutse 720101, Nigeria
| | | | | | - Maged Nasser
- Computer & Information Sciences Department, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Malaysia;
| | - Faisal Saeed
- DAAI Research Group, Department of Computing and Data Science, School of Computing and Digital Technology, Birmingham City University, Birmingham B4 7XG, UK;
| | - Hussain A. Younis
- School of Computer Sciences, Universiti Sains Malaysia, Gelugor 11800, Malaysia;
- College of Education for Women, University of Basrah, Basrah 61004, Iraq
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15
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Shivapatham G, Richards S, Bamber J, Screen H, Morrissey D. Ultrasound Measurement of Local Deformation in the Human Free Achilles Tendon Produced by Dynamic Muscle-Induced Loading: A Systematic Review. ULTRASOUND IN MEDICINE & BIOLOGY 2023; 49:1499-1509. [PMID: 37149429 DOI: 10.1016/j.ultrasmedbio.2023.03.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 02/28/2023] [Accepted: 03/18/2023] [Indexed: 05/08/2023]
Abstract
Achilles tendinopathy is the most prevalent lower limb tendinopathy, yet it remains poorly understood, with mismatches between observed structure and reported function. Recent studies have hypothesised that Achilles tendon (AT) healthy function is associated with variable deformation across the tendon width during use, focusing on quantifying sub-tendon deformation. Here, the aim of this work was to synthesise recent advances exploring human free AT tissue-level deformation during use. Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, PubMed, Embase, Scopus and Web of Science were systematically searched. Study quality and risk of bias were assessed. Thirteen articles were retained, yielding data on free AT deformation patterns. Seven were categorised as high-quality and six as medium-quality studies. Evidence consistently reports that healthy and young tendons deform non-uniformly, with the deeper layer displacing 18%-80% more than the superficial layer. Non-uniformity decreased by 12%-85% with increasing age and by 42%-91% in the presence of injury. There is limited evidence of large effect that AT deformation patterns during dynamic loading are non-uniform and may act as a biomarker of tendon health, risk of injury and rehabilitation impact. Better considered participant recruitment and improved measurement procedures would particularly improve study quality, to explore links between tendon structure, function, aging and disease in distinct populations.
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Affiliation(s)
| | - Samuel Richards
- Centre for Sports and Exercise Medicine, Queen Mary University of London, London, UK
| | - Jeffrey Bamber
- Institute of Cancer Research and Royal Marsden NHS Foundation Trust, London, UK
| | - Hazel Screen
- School of Engineering and Material Science, Queen Mary University of London, London, UK
| | - Dylan Morrissey
- Centre for Sports and Exercise Medicine, Queen Mary University of London, London, UK
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16
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Young F, Mason R, Morris RE, Stuart S, Godfrey A. IoT-Enabled Gait Assessment: The Next Step for Habitual Monitoring. SENSORS (BASEL, SWITZERLAND) 2023; 23:4100. [PMID: 37112441 PMCID: PMC10144082 DOI: 10.3390/s23084100] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 04/13/2023] [Accepted: 04/17/2023] [Indexed: 06/19/2023]
Abstract
Walking/gait quality is a useful clinical tool to assess general health and is now broadly described as the sixth vital sign. This has been mediated by advances in sensing technology, including instrumented walkways and three-dimensional motion capture. However, it is wearable technology innovation that has spawned the highest growth in instrumented gait assessment due to the capabilities for monitoring within and beyond the laboratory. Specifically, instrumented gait assessment with wearable inertial measurement units (IMUs) has provided more readily deployable devices for use in any environment. Contemporary IMU-based gait assessment research has shown evidence of the robust quantifying of important clinical gait outcomes in, e.g., neurological disorders to gather more insightful habitual data in the home and community, given the relatively low cost and portability of IMUs. The aim of this narrative review is to describe the ongoing research regarding the need to move gait assessment out of bespoke settings into habitual environments and to consider the shortcomings and inefficiencies that are common within the field. Accordingly, we broadly explore how the Internet of Things (IoT) could better enable routine gait assessment beyond bespoke settings. As IMU-based wearables and algorithms mature in their corroboration with alternate technologies, such as computer vision, edge computing, and pose estimation, the role of IoT communication will enable new opportunities for remote gait assessment.
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Affiliation(s)
- Fraser Young
- Department of Computer and Information Sciences, Northumbria University, Newcastle-upon-Tyne NE1 8ST, UK
| | - Rachel Mason
- Department of Health and Life Sciences, Northumbria University, Newcastle-upon-Tyne NE1 8ST, UK
| | - Rosie E. Morris
- Department of Health and Life Sciences, Northumbria University, Newcastle-upon-Tyne NE1 8ST, UK
| | - Samuel Stuart
- Department of Health and Life Sciences, Northumbria University, Newcastle-upon-Tyne NE1 8ST, UK
| | - Alan Godfrey
- Department of Computer and Information Sciences, Northumbria University, Newcastle-upon-Tyne NE1 8ST, UK
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17
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Uhlár Á, Ambrus M, Lacza Z. Dynamic valgus knee revealed with single leg jump tests in soccer players. J Sports Med Phys Fitness 2023; 63:461-470. [PMID: 36861880 DOI: 10.23736/s0022-4707.22.14442-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/03/2023]
Abstract
BACKGROUND Dynamic valgus knee occurs in sports that involve jumps and landing such as soccer and pose an increased risk for anterior cruciate ligament injury. Visual estimation is biased by the athlete's body type, the experience of the evaluator and the movement phase at which the valgus is assessed - thus the result is highly variable. The aim of our study was to accurately assess dynamic knee positions during single and double leg tests through a video-based movement analysis system. METHODS Young soccer players (U15, N.=22) performed single leg squat, single leg jump, and double leg jump tests while the knee medio-lateral movement was monitored with a Kinect Azure camera. Jumping and landing phases of the movement were determined within the continuous recording of the knee medio-lateral position over the ankle and the hip vertical position. Kinect measurements were validated by Optojump (Microgate, Bolzano, Italy). RESULTS Soccer players retained their predominantly varus knee positions in all phases of double-leg jumps, which was far less prominent in single leg tests. Interestingly, a marked dynamic valgus was observed in athletes who participated in traditional strengthening exercises, while this valgus shift was mostly prevented in those who participated in antivalgus training regimes. All these differences were only revealed during single leg tests, while the double leg jump tests masked all valgus tendencies. CONCLUSIONS We propose to use single-leg tests and movement analysis systems for evaluating dynamic valgus knee in athletes. These methods can reveal valgus tendencies even in soccer players who have a characteristic varus knee while standing.
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Affiliation(s)
- Ádám Uhlár
- Research Center for Sports Physiology, Hungarian University of Sports Science, Budapest, Hungary -
| | - Mira Ambrus
- Research Center for Sports Physiology, Hungarian University of Sports Science, Budapest, Hungary
| | - Zsombor Lacza
- Research Center for Sports Physiology, Hungarian University of Sports Science, Budapest, Hungary
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18
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Fast tool to evaluate 3D movements of the foot-ankle complex using multi-view depth sensors. MEDICINE IN NOVEL TECHNOLOGY AND DEVICES 2023. [DOI: 10.1016/j.medntd.2023.100212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023] Open
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19
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Yogev-Seligmann G, Josman N, Bitterman N, Rosenblum S, Naaman S, Gilboa Y. The development of a home-based technology to improve gait in people with Parkinson's disease: a feasibility study. Biomed Eng Online 2023; 22:2. [PMID: 36658571 PMCID: PMC9851591 DOI: 10.1186/s12938-023-01066-2] [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: 09/08/2022] [Accepted: 01/09/2023] [Indexed: 01/20/2023] Open
Abstract
BACKGROUND People with Parkinson's disease (PwP) may experience gait impairment and freezing of gait (FOG), a major cause of falls. External cueing, including visual (e.g., spaced lines on the floor) and auditory (e.g., rhythmic metronome beats) stimuli, are considered effective in alleviating mobility deficits and FOG. Currently, there is a need for a technology that delivers automatic, individually adjusted cues in the homes of PwP. The aims of this feasibility study were to describe the first step toward the development of a home-based technology that delivers external cues, test its effect on gait, and assess user experience. METHODS Iterative system development was performed by our multidisciplinary team. The system was designed to deliver visual and auditory cues: light stripes projected on the floor and metronome beats, separately. Initial testing was performed using the feedback of five healthy elderly individuals on the cues' clarity (clear visibility of the light stripes and the sound of metronome beats) and discomfort experienced. A pilot study was subsequently conducted in the homes of 15 PwP with daily FOG. We measured participants' walking under three conditions: baseline (with no cues), walking with light stripes, and walking to metronome beats. Outcome measures included step length and step time. User experience was also captured in semi-structured interviews. RESULTS Repeated-measures ANOVA of gait assessment in PwP revealed that light stripes significantly improved step length (p = 0.009) and step time (p = 0.019) of PwP. No significant changes were measured in the metronome condition. PwP reported that both cueing modalities improved their gait, confidence, and stability. Most PwP did not report any discomfort in either modality and expressed a desire to have such a technology in their homes. The metronome was preferred by the majority of participants. CONCLUSIONS This feasibility study demonstrated the usability and potential effect of a novel cueing technology on gait, and represents an important first step toward the development of a technology aimed to prevent FOG by delivering individually adjusted cues automatically. A further full-scale study is needed. Trial registration This study was registered in ClinicalTrials.gov at 1/2/2022 NCT05211687.
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Affiliation(s)
- Galit Yogev-Seligmann
- grid.18098.380000 0004 1937 0562Department of Occupational Therapy, Faculty of Social Welfare & Health Sciences, University of Haifa, 3498838 Haifa, Israel
| | - Naomi Josman
- grid.18098.380000 0004 1937 0562Department of Occupational Therapy, Faculty of Social Welfare & Health Sciences, University of Haifa, 3498838 Haifa, Israel
| | - Noemi Bitterman
- grid.6451.60000000121102151Technion, Israel Institute of Technology, Haifa, Israel
| | - Sara Rosenblum
- grid.18098.380000 0004 1937 0562Department of Occupational Therapy, Faculty of Social Welfare & Health Sciences, University of Haifa, 3498838 Haifa, Israel
| | - Sitar Naaman
- grid.18098.380000 0004 1937 0562Department of Physical Therapy, Faculty of Social Welfare & Health Sciences, University of Haifa, Haifa, Israel
| | - Yafit Gilboa
- grid.9619.70000 0004 1937 0538School of Occupational Therapy, Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
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20
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Young F, Mason R, Morris R, Stuart S, Godfrey A. Internet-of-Things-Enabled Markerless Running Gait Assessment from a Single Smartphone Camera. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23020696. [PMID: 36679494 PMCID: PMC9866353 DOI: 10.3390/s23020696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 01/04/2023] [Accepted: 01/05/2023] [Indexed: 05/14/2023]
Abstract
Running gait assessment is essential for the development of technical optimization strategies as well as to inform injury prevention and rehabilitation. Currently, running gait assessment relies on (i) visual assessment, exhibiting subjectivity and limited reliability, or (ii) use of instrumented approaches, which often carry high costs and can be intrusive due to the attachment of equipment to the body. Here, the use of an IoT-enabled markerless computer vision smartphone application based upon Google’s pose estimation model BlazePose was evaluated for running gait assessment for use in low-resource settings. That human pose estimation architecture was used to extract contact time, swing time, step time, knee flexion angle, and foot strike location from a large cohort of runners. The gold-standard Vicon 3D motion capture system was used as a reference. The proposed approach performs robustly, demonstrating good (ICC(2,1) > 0.75) to excellent (ICC(2,1) > 0.90) agreement in all running gait outcomes. Additionally, temporal outcomes exhibit low mean error (0.01−0.014 s) in left foot outcomes. However, there are some discrepancies in right foot outcomes, due to occlusion. This study demonstrates that the proposed low-cost and markerless system provides accurate running gait assessment outcomes. The approach may help routine running gait assessment in low-resource environments.
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Affiliation(s)
- Fraser Young
- Department of Computer and Information Sciences, Northumbria University, Newcastle-upon-Tyne NE1 8ST, UK
| | - Rachel Mason
- Department of Health and Life Sciences, Northumbria University, Newcastle-upon-Tyne NE1 8ST, UK
| | - Rosie Morris
- Department of Health and Life Sciences, Northumbria University, Newcastle-upon-Tyne NE1 8ST, UK
| | - Samuel Stuart
- Department of Health and Life Sciences, Northumbria University, Newcastle-upon-Tyne NE1 8ST, UK
| | - Alan Godfrey
- Department of Computer and Information Sciences, Northumbria University, Newcastle-upon-Tyne NE1 8ST, UK
- Correspondence:
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21
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Wang Y, Tang R, Wang H, Yu X, Li Y, Wang C, Wang L, Qie S. The Validity and Reliability of a New Intelligent Three-Dimensional Gait Analysis System in Healthy Subjects and Patients with Post-Stroke. SENSORS (BASEL, SWITZERLAND) 2022; 22:9425. [PMID: 36502143 PMCID: PMC9740023 DOI: 10.3390/s22239425] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 11/28/2022] [Accepted: 11/29/2022] [Indexed: 06/17/2023]
Abstract
Odonate is a new, intelligent three-dimensional gait analysis system based on binocular depth cameras and neural networks, but its accuracy has not been validated. Twenty-six healthy subjects and sixteen patients with post-stroke were recruited to investigate the validity and reliability of Odonate for gait analysis and examine its ability to discriminate abnormal gait patterns. The repeatability tests of different raters and different days showed great consistency. Compared with the results measured by Vicon, gait velocity, cadence, step length, cycle time, and sagittal hip and knee joint angles measured by Odonate showed high consistency, while the consistency of the gait phase division and the sagittal ankle joint angle was slightly lower. In addition, the stages with statistical differences between healthy subjects and patients during a gait cycle measured by the two systems were consistent. In conclusion, Odonate has excellent inter/intra-rater reliability, and has strong validity in measuring some spatiotemporal parameters and the sagittal joint angles, except the gait phase division and the ankle joint angle. Odonate is comparable to Vicon in its ability to identify abnormal gait patterns in patients with post-stroke. Therefore, Odonate has the potential to provide accessible and objective measurements for clinical gait assessment.
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Affiliation(s)
- Yingpeng Wang
- Department of Rehabilitation, Beijing Rehabilitation Hospital, Capital Medical University, Beijing 100144, China
| | - Ran Tang
- Department of Rehabilitation, Beijing Rehabilitation Hospital, Capital Medical University, Beijing 100144, China
| | - Hujun Wang
- Department of Rehabilitation, Beijing Rehabilitation Hospital, Capital Medical University, Beijing 100144, China
| | - Xin Yu
- Beijing Rehabilitation Medical College, Capital Medical University, Beijing 100144, China
| | - Yingqi Li
- Department of Rehabilitation, Beijing Rehabilitation Hospital, Capital Medical University, Beijing 100144, China
| | - Congxiao Wang
- Department of Rehabilitation, Beijing Rehabilitation Hospital, Capital Medical University, Beijing 100144, China
| | - Luyi Wang
- Department of Rehabilitation, Beijing Rehabilitation Hospital, Capital Medical University, Beijing 100144, China
| | - Shuyan Qie
- Department of Rehabilitation, Beijing Rehabilitation Hospital, Capital Medical University, Beijing 100144, China
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22
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Hatamzadeh M, Busé L, Chorin F, Alliez P, Favreau JD, Zory R. A kinematic-geometric model based on ankles' depth trajectory in frontal plane for gait analysis using a single RGB-D camera. J Biomech 2022; 145:111358. [PMID: 36334322 DOI: 10.1016/j.jbiomech.2022.111358] [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] [Received: 12/14/2021] [Revised: 09/19/2022] [Accepted: 10/20/2022] [Indexed: 11/06/2022]
Abstract
The emergence of RGB-D cameras and the development of pose estimation algorithms offer opportunities in biomechanics. However, some challenges still remain when using them for gait analysis, including noise which leads to misidentification of gait events and inaccuracy. Therefore, we present a novel kinematic-geometric model for spatio-temporal gait analysis, based on ankles' trajectory in the frontal plane and distance-to-camera data (depth). Our approach consists of three main steps: identification of the gait pattern and modeling via parameterized curves, development of a fitting algorithm, and computation of locomotive indices. The proposed fitting algorithm applies on both ankles' depth data simultaneously, by minimizing through numerical optimization some geometric and biomechanical error functions. For validation, 15 subjects were asked to walk inside the walkway of the OptoGait, while the OptoGait and an RGB-D camera (Microsoft Azure Kinect) were both recording. Then, the spatio-temporal parameters of both feet were computed using the OptoGait and the proposed model. Validation results show that the proposed model yields good to excellent absolute statistical agreement (0.86 ≤ Rc ≤ 0.99). Our kinematic-geometric model offers several benefits: (1) It relies only on the ankles' depth trajectory both for gait events extraction and spatio-temporal parameters' calculation; (2) it is usable with any kind of RGB-D camera or even with 3D marker-based motion analysis systems in absence of toes' and heels' markers; and (3) it enables improving the results by denoising and smoothing the ankles' depth trajectory. Hence, the proposed kinematic-geometric model facilitates the development of portable markerless systems for accurate gait analysis.
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Affiliation(s)
- Mehran Hatamzadeh
- Université Côte d'Azur, LAMHESS, Nice, France; Université Côte d'Azur, Inria, Sophia Antipolis, France; Université Côte d'Azur, CHU, Cimiez, Plateforme fragilité, Nice, France.
| | - Laurent Busé
- Université Côte d'Azur, Inria, Sophia Antipolis, France
| | - Frédéric Chorin
- Université Côte d'Azur, CHU, Cimiez, Plateforme fragilité, Nice, France
| | - Pierre Alliez
- Université Côte d'Azur, Inria, Sophia Antipolis, France
| | | | - Raphael Zory
- Université Côte d'Azur, LAMHESS, Nice, France; Université Côte d'Azur, CHU, Cimiez, Plateforme fragilité, Nice, France; Institut Universitaire de France (IUF), Paris, France
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23
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Palermo M, Lopes JM, André J, Matias AC, Cerqueira J, Santos CP. A multi-camera and multimodal dataset for posture and gait analysis. Sci Data 2022; 9:603. [PMID: 36202855 PMCID: PMC9537285 DOI: 10.1038/s41597-022-01722-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Accepted: 09/23/2022] [Indexed: 11/09/2022] Open
Abstract
Monitoring gait and posture while using assisting robotic devices is relevant to attain effective assistance and assess the user's progression throughout time. This work presents a multi-camera, multimodal, and detailed dataset involving 14 healthy participants walking with a wheeled robotic walker equipped with a pair of affordable cameras. Depth data were acquired at 30 fps and synchronized with inertial data from Xsens MTw Awinda sensors and kinematic data from the segments of the Xsens biomechanical model, acquired at 60 Hz. Participants walked with the robotic walker at 3 different gait speeds, across 3 different walking scenarios/paths at 3 different locations. In total, this dataset provides approximately 92 minutes of total recording time, which corresponds to nearly 166.000 samples of synchronized data. This dataset may contribute to the scientific research by allowing the development and evaluation of: (i) vision-based pose estimation algorithms, exploring classic or deep learning approaches; (ii) human detection and tracking algorithms; (iii) movement forecasting; and (iv) biomechanical analysis of gait/posture when using a rehabilitation device.
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Affiliation(s)
- Manuel Palermo
- CMEMS-UMinho, University of Minho, Guimarães, Portugal.,LABBELS-Associate Laboratory, Braga/Guimarães, Portugal
| | - João M Lopes
- CMEMS-UMinho, University of Minho, Guimarães, Portugal.,LABBELS-Associate Laboratory, Braga/Guimarães, Portugal
| | - João André
- CMEMS-UMinho, University of Minho, Guimarães, Portugal.,LABBELS-Associate Laboratory, Braga/Guimarães, Portugal
| | - Ana C Matias
- Clinical Academic Center (2CA-Braga), Hospital of Braga, Braga, Portugal
| | - João Cerqueira
- Clinical Academic Center (2CA-Braga), Hospital of Braga, Braga, Portugal.,Life and Health Sciences Research Institute (ICVS), University of Minho, Braga, Portugal
| | - Cristina P Santos
- CMEMS-UMinho, University of Minho, Guimarães, Portugal. .,LABBELS-Associate Laboratory, Braga/Guimarães, Portugal. .,Clinical Academic Center (2CA-Braga), Hospital of Braga, Braga, Portugal.
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Promsri A. Assessing Walking Stability Based on Whole-Body Movement Derived from a Depth-Sensing Camera. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22197542. [PMID: 36236642 PMCID: PMC9571104 DOI: 10.3390/s22197542] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 09/23/2022] [Accepted: 10/02/2022] [Indexed: 05/13/2023]
Abstract
Stability during walking is considered a crucial aspect of assessing gait ability. The current study aimed to assess walking stability by applying principal component analysis (PCA) to decompose three-dimensional (3D) whole-body kinematic data of 104 healthy young adults (21.9 ± 3.5 years, 54 females) derived from a depth-sensing camera into a set of movement components/synergies called "principal movements" (PMs), forming together to achieve the task goal. The effect of sex as the focus area was tested on three PCA-based variables computed for each PM: the relative explained variance (rVAR) as a measure of the composition of movement structures; the largest Lyapunov exponent (LyE) as a measure of variability; and the number of zero-crossings (N) as a measure of the tightness of neuromuscular control. The results show that the sex effects appear in the specific PMs. Specifically, in PM1, resembling the swing-phase movement, females have greater LyE (p = 0.013) and N (p = 0.017) values than males. Moreover, in PM3, representing the mid-stance-phase movement, females have smaller rVAR (p = 0.020) but greater N (p = 0.008) values than males. These empirical findings suggest that the inherent sex differences in walking stability should be considered in assessing and training locomotion.
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Affiliation(s)
- Arunee Promsri
- Department of Physical Therapy, School of Allied Health Sciences, University of Phayao, 19 Moo 2, Maeka, Muang, Phayao 56000, Thailand;
- Unit of Excellence in Neuromechanics, School of Allied Health Sciences, University of Phayao, 19 Moo 2, Maeka, Muang, Phayao 56000, Thailand
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THOMAS JACOBM, KOLLOCK ROGERO. The Reliability of Three-Dimensional Inertial Measurement Units in Capturing Lower-Body Joint Kinematics during Single-Leg Landing Tasks. INTERNATIONAL JOURNAL OF EXERCISE SCIENCE 2022; 15:1306-1316. [PMID: 36582517 PMCID: PMC9762242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
3-D inertial measurement units (IMUs) have advantages over other types of motion capture systems, as IMUs cannot be obstructed by equipment and gear. Therefore, the purpose of this study was to assess the reliability of IMUs in measuring joint angles at the hip, knee, and ankle during two types of single-leg landings: 1) drop-landing (DL) and 2) leap-landing (LL). Nineteen subjects, both males (n = 9, 21.88 ± 1.64 yrs, 178.36 ± 9.68 cm, 185.68 ± 16.63 kg) and females (n = 11, 22.45 ± 4.32 yrs, 171.57 ± 6.55 cm, 70.95 ± 14.99 kg) participated in this study. Participants performed three trials of both tasks. The DL required the participant to drop onto their dominant leg from a 30 cm box onto force plate. The LL task required participants to leap over a 20 cm hurdle onto the force plate. ICC values and SEM calculations were used to assess the IMU's reliability. Overall, IMUs displayed fair-to-excellent reliability for both tasks (ICC = 0.442-0.962), aside from ankle inversion (ICC = 0.290) & ankle abduction (ICC = 0.216) at initial ground contact and ankle abduction (ICC = 0.234) at maximum vertical ground reaction force, both during the LL task. IMUs can be a reliable measurement tool for lower extremity motion during dynamic landing, so long as factors related to reliability at the ankle are considered.
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Guffanti D, Brunete A, Hernando M, Gambao E, Alvarez D. ANN-Based Optimization of Human Gait Data Obtained From a Robot-Mounted 3D Camera: A Multiple Sclerosis Case Study. IEEE Robot Autom Lett 2022. [DOI: 10.1109/lra.2022.3189433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Diego Guffanti
- Department of Electrical, Electronic and Automation Engineering and Applied Physics, ETSIDI, Universidad Politécnica de Madrid, Madrid, Spain
| | - Alberto Brunete
- Centre for Automation and Robotics (CAR) UPM-CSIC, Madrid, Spain
| | - Miguel Hernando
- Centre for Automation and Robotics (CAR) UPM-CSIC, Madrid, Spain
| | - Ernesto Gambao
- Centre for Automation and Robotics (CAR) UPM-CSIC, Madrid, Spain
| | - David Alvarez
- Centre for Automation and Robotics (CAR) UPM-CSIC, Madrid, Spain
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Wen Y, Li B, Chen D, Zhu T. Reliability and validity analysis of personality assessment model based on gait video. Front Behav Neurosci 2022; 16:901568. [PMID: 35983477 PMCID: PMC9380895 DOI: 10.3389/fnbeh.2022.901568] [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] [Received: 05/04/2022] [Accepted: 07/04/2022] [Indexed: 11/30/2022] Open
Abstract
Personality affects an individual’s academic achievements, occupational tendencies, marriage quality and physical health, so more convenient and objective personality assessment methods are needed. Gait is a natural, stable, and easy-to-observe body movement that is closely related to personality. The purpose of this paper is to propose a personality assessment model based on gait video and evaluate the reliability and validity of the multidimensional model. This study recruited 152 participants and used cameras to record their gait videos. Each participant completed a 44-item Big Five Inventory (BFI-44) assessment. We constructed diverse static and dynamic time-frequency features based on gait skeleton coordinates, interframe differences, distances between joints, angles between joints, and wavelet decomposition coefficient arrays. We established multidimensional personality trait assessment models through machine learning algorithms and evaluated the criterion validity, split-half reliability, convergent validity, and discriminant validity of these models. The results showed that the reliability and validity of the Gaussian process regression (GPR) and linear regression (LR) models were best. The mean values of their criterion validity were 0.478 and 0.508, respectively, and the mean values of their split-half reliability were all greater than 0.8. In the formed multitrait-multimethod matrix, these methods also had higher convergent and discriminative validity. The proposed approach shows that gait video can be effectively used to evaluate personality traits, providing a new idea for the formation of convenient and non-invasive personality assessment methods.
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Affiliation(s)
- Yeye Wen
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, China
- Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - Baobin Li
- School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, China
| | - Deyuan Chen
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, China
- *Correspondence: Tingshao Zhu,
| | - Tingshao Zhu
- Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
- Deyuan Chen,
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Aoyagi Y, Yamada S, Ueda S, Iseki C, Kondo T, Mori K, Kobayashi Y, Fukami T, Hoshimaru M, Ishikawa M, Ohta Y. Development of Smartphone Application for Markerless Three-Dimensional Motion Capture Based on Deep Learning Model. SENSORS (BASEL, SWITZERLAND) 2022; 22:5282. [PMID: 35890959 PMCID: PMC9322512 DOI: 10.3390/s22145282] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 07/08/2022] [Accepted: 07/12/2022] [Indexed: 06/15/2023]
Abstract
To quantitatively assess pathological gait, we developed a novel smartphone application for full-body human motion tracking in real time from markerless video-based images using a smartphone monocular camera and deep learning. As training data for deep learning, the original three-dimensional (3D) dataset comprising more than 1 million captured images from the 3D motion of 90 humanoid characters and the two-dimensional dataset of COCO 2017 were prepared. The 3D heatmap offset data consisting of 28 × 28 × 28 blocks with three red-green-blue colors at the 24 key points of the entire body motion were learned using the convolutional neural network, modified ResNet34. At each key point, the hottest spot deviating from the center of the cell was learned using the tanh function. Our new iOS application could detect the relative tri-axial coordinates of the 24 whole-body key points centered on the navel in real time without any markers for motion capture. By using the relative coordinates, the 3D angles of the neck, lumbar, bilateral hip, knee, and ankle joints were estimated. Any human motion could be quantitatively and easily assessed using a new smartphone application named Three-Dimensional Pose Tracker for Gait Test (TDPT-GT) without any body markers or multipoint cameras.
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Affiliation(s)
| | - Shigeki Yamada
- Department of Neurosurgery, Shiga University of Medical Science, Otsu 520-2192, Japan
- Department of Neurosurgery, Nagoya City University Graduate School of Medical Science, Nagoya 467-8601, Japan
- Normal Pressure Hydrocephalus Center, Rakuwakai Otowa Hospital, Kyoto 607-8062, Japan;
- Interfaculty Initiative in Information Studies/Institute of Industrial Science, The University of Tokyo, Tokyo 153-8505, Japan
| | - Shigeo Ueda
- Shin-Aikai Spine Center, Katano Hospital, Katano 576-0043, Japan; (S.U.); (M.H.)
| | - Chifumi Iseki
- Division of Neurology and Clinical Neuroscience, Department of Internal Medicine III, Yamagata University School of Medicine, Yamagata 990-9585, Japan; (C.I.); (T.K.); (Y.O.)
| | - Toshiyuki Kondo
- Division of Neurology and Clinical Neuroscience, Department of Internal Medicine III, Yamagata University School of Medicine, Yamagata 990-9585, Japan; (C.I.); (T.K.); (Y.O.)
| | - Keisuke Mori
- School of Medicine, Shiga University of Medical Science, Otsu 520-2192, 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;
| | - Tadanori Fukami
- Department of Informatics and Electronics, Faculty of Engineering, Yamagata University, Yamagata 992-8510, Japan;
| | - Minoru Hoshimaru
- Shin-Aikai Spine Center, Katano Hospital, Katano 576-0043, Japan; (S.U.); (M.H.)
| | - Masatsune Ishikawa
- Normal Pressure Hydrocephalus Center, Rakuwakai Otowa Hospital, Kyoto 607-8062, Japan;
- Rakuwa Villa Ilios, Rakuwakai Healthcare System, Kyoto 604-8402, Japan
| | - Yasuyuki Ohta
- Division of Neurology and Clinical Neuroscience, Department of Internal Medicine III, Yamagata University School of Medicine, Yamagata 990-9585, Japan; (C.I.); (T.K.); (Y.O.)
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Fiorini L, Coviello L, Sorrentino A, Sancarlo D, Ciccone F, D’Onofrio G, Mancioppi G, Rovini E, Cavallo F. User Profiling to Enhance Clinical Assessment and Human-Robot Interaction: A Feasibility Study. Int J Soc Robot 2022; 15:501-516. [PMID: 35846164 PMCID: PMC9266091 DOI: 10.1007/s12369-022-00901-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/07/2022] [Indexed: 11/18/2022]
Abstract
Socially Assistive Robots (SARs) are designed to support us in our daily life as a companion, and assistance but also to support the caregivers' work. SARs should show personalized and human-like behavior to improve their acceptance and, consequently, their use. Additionally, they should be trustworthy by caregivers and professionals to be used as support for their work (e.g. objective assessment, decision support tools). In this context the aim of the paper is dual. Firstly, this paper aims to present and discuss the robot behavioral model based on sensing, perception, decision support, and interaction modules. The novel idea behind the proposed model is to extract and use the same multimodal features set for two purposes: (i) to profile the user, so to be used by the caregiver as a decision support tool for the assessment and monitoring of the patient; (ii) to fine-tune the human-robot interaction if they can be correlated to the social cues. Secondly, this paper aims to test in a real environment the proposed model using a SAR robot, namely ASTRO. Particularly, it measures the body posture, the gait cycle, and the handgrip strength during the walking support task. Those collected data were analyzed to assess the clinical profile and to fine-tune the physical interaction. Ten older people (65.2 ± 15.6 years) were enrolled for this study and were asked to walk with ASTRO at their normal speed for 10 m. The obtained results underline a good estimation (p < 0.05) of gait parameters, handgrip strength, and angular excursion of the torso with respect to most used instruments. Additionally, the sensory outputs were combined in the perceptual model to profile the user using non-classical and unsupervised techniques for dimensionality reduction namely T-distributed Stochastic Neighbor Embedding (t-SNE) and non-classic multidimensional scaling (nMDS). Indeed, these methods can group the participants according to their residual walking abilities.
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Affiliation(s)
- Laura Fiorini
- Department of Industrial Engineering, University of Florence, Florence, Italy
- The BioRobotics Institute, Scuola Superiore Sant’Anna, Pontedera (Pisa), Italy
- Department of Excellence in Robotics & AI, Scuola Superiore Sant’Anna, Pisa, Italy
| | - Luigi Coviello
- The BioRobotics Institute, Scuola Superiore Sant’Anna, Pontedera (Pisa), Italy
- Department of Excellence in Robotics & AI, Scuola Superiore Sant’Anna, Pisa, Italy
| | | | - Daniele Sancarlo
- The Complex Unit of Geriatrics, Department of Medical Sciences, Fondazione “Casa Sollievo della Sofferenza” – IRCCS, San Giovanni Rotondo, Foggia, Italy
| | - Filomena Ciccone
- Clinical Psychology Service, Health Department, Fondazione IRCCS Casa Sollievo Della Sofferenza, San Giovanni Rotondo, Foggia, Italy
| | - Grazia D’Onofrio
- Clinical Psychology Service, Health Department, Fondazione IRCCS Casa Sollievo Della Sofferenza, San Giovanni Rotondo, Foggia, Italy
| | - Gianmaria Mancioppi
- The BioRobotics Institute, Scuola Superiore Sant’Anna, Pontedera (Pisa), Italy
- Department of Excellence in Robotics & AI, Scuola Superiore Sant’Anna, Pisa, Italy
| | - Erika Rovini
- Department of Industrial Engineering, University of Florence, Florence, Italy
| | - Filippo Cavallo
- Department of Industrial Engineering, University of Florence, Florence, Italy
- The BioRobotics Institute, Scuola Superiore Sant’Anna, Pontedera (Pisa), Italy
- Department of Excellence in Robotics & AI, Scuola Superiore Sant’Anna, Pisa, Italy
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Seifallahi M, Mehraban AH, Galvin JE, Ghoraani B. Alzheimer's Disease Detection Using Comprehensive Analysis of Timed Up and Go Test via Kinect V.2 Camera and Machine Learning. IEEE Trans Neural Syst Rehabil Eng 2022; 30:1589-1600. [PMID: 35675251 PMCID: PMC10771634 DOI: 10.1109/tnsre.2022.3181252] [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] [Indexed: 11/08/2022]
Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative disease affecting cognitive and functional abilities. However, many patients presume lower cognitive or functional abilities because of aging and do not undergo clinical assessments until the symptoms become too advanced. Developing a low-cost and easy-to-use AD detection tool, which can be used in any clinical or non-clinical setting, can enable widespread AD assessments and diagnosis. This paper investigated the feasibility of developing such a tool to detect AD vs. healthy control (HC) from a simple balance and walking assessment called the Timed Up and Go (TUG) test. We collected joint position data of 47 HC and 38 AD subjects as they performed TUG in front of a Kinect V.2 camera. Our signal processing and statistical analyses provided a comprehensive analysis of balance and gait with 12 significant features for discriminating AD from HC after adjusting for age and the Geriatric Depression Scale. Using these features and a support vector machine classifier, our model classified the two groups with an average accuracy of 97.75% and an F-score of 97.67% for five-fold cross-validation and 98.68% and 98.67% for leave-one-subject out cross-validation. These results demonstrate the potential of our approach as a new quantitative complementary tool for detecting AD among older adults. Our work is novel as it presents the first application of Kinect V.2 camera and machine learning to provide a comprehensive and quantitative analysis of the TUG test to detect AD patients from HC. This study supports the feasibility of developing a low-cost and convenient AD assessment tool that can be used during routine checkups or even at home; however, future investigations could confirm its clinical diagnostic value in a larger cohort.
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Ripic Z, Kuenze C, Andersen MS, Theodorakos I, Signorile J, Eltoukhy M. Ground reaction force and joint moment estimation during gait using an Azure Kinect-driven musculoskeletal modeling approach. Gait Posture 2022; 95:49-55. [PMID: 35428024 DOI: 10.1016/j.gaitpost.2022.04.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 04/02/2022] [Accepted: 04/07/2022] [Indexed: 02/02/2023]
Abstract
BACKGROUND Gait analysis is burdened by time and equipment costs, interpretation, and accessibility of three-dimensional motion analysis systems. Evidence suggests growing adoption of gait testing in the shift toward evidence-based medicine. Further developments addressing these barriers will aid its efficacy in clinical practice. Previous research aiming to develop gait analysis systems for kinetics estimation using the Kinect V2 have provided promising results yet modified approaches using the latest hardware may further aid kinetics estimation accuracy RESEARCH QUESTION: Can a single Azure Kinect sensor combined with a musculoskeletal modeling approach provide kinetics estimations during gait similar to those obtained from marker-based systems with embedded force platforms? METHODS Ten subjects were recruited to perform three walking trials at their normal speed. Trials were recorded using an eight-camera optoelectronic system with two embedded force plates and a single Azure Kinect sensor. Marker and depth data were both used to drive a musculoskeletal model using the AnyBody Modeling System. Predicted kinetics from the Azure Kinect-driven model, including ground reaction force (GRF) and joint moments, were compared to measured values using root meansquared error (RMSE), normalized RMSE, Pearson correlation, concordance correlation, and statistical parametric mapping RESULTS: High to very high correlations were observed for anteroposterior GRF (ρ = 0.889), vertical GRF (ρ = 0.940), and sagittal hip (ρ = 0.805) and ankle (ρ = 0.876) moments. RMSEs were 1.2 ± 2.2 (%BW), 3.2 ± 5.7 (%BW), 0.7 ± 0.1.3 (%BWH), and 0.6 ± 1.0 (%BWH) SIGNIFICANCE: The proposed approach using the Azure Kinect provided higher accuracy compared to previous studies using the Kinect V2 potentially due to improved foot tracking by the Azure Kinect. Future studies should seek to optimize ground contact parameters and focus on regions of error between predicted and measured kinetics highlighted currently for further improvements in kinetic estimations.
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Affiliation(s)
- Zachary Ripic
- Department of Kinesiology and Sport Sciences, School of Education & Human Development, University of Miami, Coral Gables, FL 33143, USA
| | - Christopher Kuenze
- Department of Kinesiology, School of Education, Michigan State University, East Lansing, MI 48824, USA
| | - Michael Skipper Andersen
- Department of Materials and Production, Aalborg University, Fibigerstraede 16, 9220 Aalborg East, Denmark
| | - Ilias Theodorakos
- Department of Materials and Production, Aalborg University, Fibigerstraede 16, 9220 Aalborg East, Denmark
| | - Joseph Signorile
- Department of Kinesiology and Sport Sciences, School of Education & Human Development, University of Miami, Coral Gables, FL 33143, USA; Center on Aging, Miller School of Medicine, University of Miami, Coral Gables, FL 33146, USA
| | - Moataz Eltoukhy
- Department of Kinesiology and Sport Sciences, School of Education & Human Development, University of Miami, Coral Gables, FL 33143, USA.
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Abstract
Computer-vision-based frameworks enable markerless human motion capture on consumer-grade devices in real-time. They open up new possibilities for application, such as in the health and medical sector. So far, research on mobile solutions has been focused on 2-dimensional motion capture frameworks. 2D motion analysis is limited by the viewing angle of the positioned camera. New frameworks enable 3-dimensional human motion capture and can be supported through additional smartphone sensors such as LiDAR. 3D motion capture promises to overcome the limitations of 2D frameworks by considering all three movement planes independent of the camera angle. In this study, we performed a laboratory experiment with ten subjects, comparing the joint angles in eight different body-weight exercises tracked by Apple ARKit, a mobile 3D motion capture framework, against a gold-standard system for motion capture: the Vicon system. The 3D motion capture framework exposed a weighted Mean Absolute Error of 18.80° ± 12.12° (ranging from 3.75° ± 0.99° to 47.06° ± 5.11° per tracked joint angle and exercise) and a Mean Spearman Rank Correlation Coefficient of 0.76 for the whole data set. The data set shows a high variance of those two metrics between the observed angles and performed exercises. The observed accuracy is influenced by the visibility of the joints and the observed motion. While the 3D motion capture framework is a promising technology that could enable several use cases in the entertainment, health, and medical area, its limitations should be considered for each potential application area.
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Vafadar S, Skalli W, Bonnet-Lebrun A, Assi A, Gajny L. Assessment of a novel deep learning-based marker-less motion capture system for gait study. Gait Posture 2022; 94:138-143. [PMID: 35306382 DOI: 10.1016/j.gaitpost.2022.03.008] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Revised: 02/25/2022] [Accepted: 03/14/2022] [Indexed: 02/02/2023]
Abstract
BACKGROUND Marker-less systems based on digital video cameras and deep learning for gait analysis could have a deep impact in clinical routine. A recently developed system has shown promising results in terms of joint center position but has not been yet evaluated in terms of gait outcomes. RESEARCH QUESTION How does this novel marker-less system compare to a marker-based reference system in terms of clinically relevant gait parameters? METHODS The deep learning method behind the developed marker-less system was trained on a dedicated dataset consisting of forty-one asymptomatic and pathological subjects each performing ten walking trials. The system could estimate the three-dimensional position of seventeen joint centers or keypoints (e.g., neck, shoulders, hip, knee, and ankles). We evaluated the marker-less system against a marker-based system in terms of differences in joint position (Euclidean distance), detection of gait events (e.g., heel strike and toe-off), spatiotemporal parameters (e.g., step length, time), kinematic parameters (e.g., hip and knee extension-flexion), and inter-trial reliability for kinematic parameters. RESULTS The marker-less system was able to estimate the three-dimensional position of joint centers with a mean difference of 13.1 mm (SD = 10.2 mm). 99% of the estimated gait events were estimated within 10 ms of the corresponding reference values. Estimated spatiotemporal parameters showed zero bias. The mean and standard deviation of the differences of the estimated kinematic parameters varied by parameter (for example, the mean and standard deviation for knee extension flexion angle were -3.0° and 2.7°). Inter-trial reliability of the measured parameters was similar to that of the marker-based references. SIGNIFICANCE The developed marker-less system can measure the spatiotemporal parameters within the range of the minimum detectable changes obtained using the marker-based reference system. Moreover, except for hip extension flexion, the system showed promising results in terms of several kinematic parameters.
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Affiliation(s)
- Saman Vafadar
- Institut de Biomecanique Humaine Georges Charpak, Arts et Metiers, Institute of Technology, Paris, France.
| | - Wafa Skalli
- Institut de Biomecanique Humaine Georges Charpak, Arts et Metiers, Institute of Technology, Paris, France.
| | - Aurore Bonnet-Lebrun
- Institut de Biomecanique Humaine Georges Charpak, Arts et Metiers, Institute of Technology, Paris, France.
| | - Ayman Assi
- Faculty of Medicine, University of Saint-Joseph in Beirut, Beirut, Lebanon.
| | - Laurent Gajny
- Institut de Biomecanique Humaine Georges Charpak, Arts et Metiers, Institute of Technology, Paris, France.
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Hoang TH, Zehni M, Xu H, Heintz G, Zallek C, Do MN. Towards a Comprehensive Solution for a Vision-based Digitized Neurological Examination. IEEE J Biomed Health Inform 2022; 26:4020-4031. [PMID: 35439148 DOI: 10.1109/jbhi.2022.3167927] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
The ability to use digitally recorded and quantified neurological exam information is important to help healthcare systems deliver better care, in-person and via telehealth, as they compensate for a growing shortage of neurologists. Current neurological digital biomarker pipelines, however, are narrowed down to a specific neurological exam component or applied for assessing specific conditions. In this paper, we propose an accessible vision-based exam and documentation solution called Digitized Neurological Examination (DNE) to expand exam biomarker recording options and clinical applications using a smartphone/tablet. Through our DNE software, healthcare providers in clinical settings and people at home are enabled to video capture an examination while performing instructed neurological tests, including finger tapping, finger to finger, forearm roll, and stand-up and walk. Our modular design of the DNE software supports integrations of additional tests. The DNE extracts from the recorded examinations the 2D/3D human-body pose and quantifies kinematic and spatio-temporal features. The features are clinically relevant and allow clinicians to document and observe the quantified movements and the changes of these metrics over time. A web server and a user interface for recordings viewing and feature visualizations are available. DNE was evaluated on a collected dataset of 21 subjects containing normal and simulated-impaired movements. The overall accuracy of DNE is demonstrated by classifying the recorded movements using various machine learning models. Our tests show an accuracy beyond 90% for upper-limb tests and 80% for the stand-up and walk tests.
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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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Faity G, Mottet D, Froger J. Validity and Reliability of Kinect v2 for Quantifying Upper Body Kinematics during Seated Reaching. SENSORS 2022; 22:s22072735. [PMID: 35408349 PMCID: PMC9003545 DOI: 10.3390/s22072735] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 03/30/2022] [Accepted: 03/31/2022] [Indexed: 11/16/2022]
Abstract
Kinematic analysis of the upper limbs is a good way to assess and monitor recovery in individuals with stroke, but it remains little used in clinical routine due to its low feasibility. The aim of this study is to assess the validity and reliability of the Kinect v2 for the analysis of upper limb reaching kinematics. Twenty-six healthy participants performed seated hand-reaching tasks while holding a dumbbell to induce behaviour similar to that of stroke survivors. With the Kinect v2 and with the VICON, 3D upper limb and trunk motions were simultaneously recorded. The Kinect assesses trunk compensations, hand range of motion, movement time and mean velocity with a moderate to excellent reliability. In contrast, elbow and shoulder range of motion, time to peak velocity and path length ratio have a poor to moderate reliability. Finally, instantaneous hand and elbow tracking are not precise enough to reliably assess the number of velocity peaks and the peak hand velocity. Thanks to its ease of use and markerless properties, the Kinect can be used in clinical routine for semi-automated quantitative diagnostics guiding individualised rehabilitation of the upper limb. However, engineers and therapists must bear in mind the tracking limitations of the Kinect.
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Affiliation(s)
- Germain Faity
- Euromov Digital Health in Motion, Univ Montpellier, IMT Mines Ales, 34090 Montpellier, France;
| | - Denis Mottet
- Euromov Digital Health in Motion, Univ Montpellier, IMT Mines Ales, 34090 Montpellier, France;
- Correspondence:
| | - Jérôme Froger
- Euromov Digital Health in Motion, Univ Montpellier, IMT Mines Ales, CHU de Nîmes, 30240 Le Grau du Roi, France;
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Development of an Area Scan Step Length Measuring System Using a Polynomial Estimate of the Heel Cloud Point. SIGNALS 2022. [DOI: 10.3390/signals3020011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Due to impaired mobility caused by aging, it is very important to employ early detection and monitoring of gait parameters to prevent the inevitable huge amount of medical cost at a later age. For gait training and potential tele-monitoring application outside clinical settings, low-cost yet highly reliable gait analysis systems are needed. This research proposes using a single LiDAR system to perform automatic gait analysis with polynomial fitting. The experimental setup for this study consists of two different walking speeds, fast walk and normal walk, along a 5-m straight line. There were ten test subjects (mean age 28, SD 5.2) who voluntarily participated in the study. We performed polynomial fitting to estimate the step length from the heel projection cloud point laser data as the subject walks forwards and compared the values with the visual inspection method. The results showed that the visual inspection method is accurate up to 6 cm while the polynomial method achieves 8 cm in the worst case (fast walking). With the accuracy difference estimated to be at most 2 cm, the polynomial method provides reliability of heel location estimation as compared with the observational gait analysis. The proposed method in this study presents an improvement accuracy of 4% as opposed to the proposed dual-laser range sensor method that reported 57.87 cm ± 10.48, an error of 10%. Meanwhile, our proposed method reported ±0.0633 m, a 6% error for normal walking.
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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: 17] [Impact Index Per Article: 8.5] [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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Tang YM, Wang YH, Feng XY, Zou QS, Wang Q, Ding J, Shi RCJ, Wang X. Diagnostic value of a vision-based intelligent gait analyzer in screening for gait abnormalities. Gait Posture 2022; 91:205-211. [PMID: 34740057 DOI: 10.1016/j.gaitpost.2021.10.028] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Revised: 10/17/2021] [Accepted: 10/18/2021] [Indexed: 02/02/2023]
Abstract
BACKGROUND Early detection of gait abnormalities is critical for preventing severe injuries in future falls. The timed up and go (TUG) test is a commonly used clinical gait screening test; however, the interpretation of its results is limited to the TUG total time. RESEARCH QUESTION What is diagnostic accuracy of the low-cost, markerless, automated gait analyzer, with the aid of vision-based artificial intelligence technology, which extract gait spatiotemporal features and screen for abnormal walking patterns through video recordings of the TUG test? METHODS Our dataset contained retrospective data from outpatients from the Department of Neurology or Rehabilitation of two tertiary hospitals in Shanghai. A panel of three expert neurologists specialized in movement disorders reviewed the gait performance in each TUG video, and labeled them separately, with the most commonly assigned label being used as the reference standard. The gait analyzer performed the AlphaPose algorithm to track the human joint position and calculated the spatiotemporal parameters by filtering and double-threshold signal detection. Gait spatiotemporal features and expert labels were input into machine learning models, and the accuracy of each model was tested with leave-one-out cross-validation (LOOCV). RESULTS A total of 284 participants were recruited. Among these, 100 were labeled as having abnormal gait performance by experts. The Naive Bayes classifier achieved the best performance with a full-data accuracy of 90.14% and a LOOCV accuracy of 89.08% for screening abnormal gait performance. SIGNIFICANCE This study is the first to investigate the accuracy of a vision-based intelligent gait analyzer for screening abnormal clinical gait performance. By virtue of a pose estimation algorithm and machine learning models, our intelligent gait analyzer can detect abnormal walking patterns approximate to judgements made by experienced neurologists, which is expected to be a supplementary gait assessment protocol for basic-level doctors in the future.
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Affiliation(s)
- Yan-Min Tang
- Department of Neurology, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai 200032, China.
| | - Yan-Hong Wang
- Institute of Brain-inspired Circuits and Systems, Fudan University, 825 Zhangheng Road, Shanghai 201203, China.
| | - Xin-Yu Feng
- Institute of Brain-inspired Circuits and Systems, Fudan University, 825 Zhangheng Road, Shanghai 201203, China.
| | - Qiao-Sha Zou
- Institute of Brain-inspired Circuits and Systems, Fudan University, 825 Zhangheng Road, Shanghai 201203, China.
| | - Qing Wang
- Institute of Brain-inspired Circuits and Systems, Fudan University, 825 Zhangheng Road, Shanghai 201203, China.
| | - Jing Ding
- Department of Neurology, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai 200032, China; Institute of Neuroscience, Chinese Academy of Sciences Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, 320 Yueyang Road, Shanghai 200031, China.
| | - Richard Chuan-Jin Shi
- Institute of Brain-inspired Circuits and Systems, Fudan University, 825 Zhangheng Road, Shanghai 201203, China; Department of Electrical and Computer Engineering, University of Washington, Seattle, WA 98195-3770, USA.
| | - Xin Wang
- Department of Neurology, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai 200032, China; Institute of Neuroscience, Chinese Academy of Sciences Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, 320 Yueyang Road, Shanghai 200031, China.
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Bilesan A, Komizunai S, Tsujita T, Konno A. Improved 3D Human Motion Capture Using Kinect Skeleton and Depth Sensor. JOURNAL OF ROBOTICS AND MECHATRONICS 2021. [DOI: 10.20965/jrm.2021.p1408] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Kinect has been utilized as a cost-effective, easy-to-use motion capture sensor using the Kinect skeleton algorithm. However, a limited number of landmarks and inaccuracies in tracking the landmarks’ positions restrict Kinect’s capability. In order to increase the accuracy of motion capturing using Kinect, joint use of the Kinect skeleton algorithm and Kinect-based marker tracking was applied to track the 3D coordinates of multiple landmarks on human. The motion’s kinematic parameters were calculated using the landmarks’ positions by applying the joint constraints and inverse kinematics techniques. The accuracy of the proposed method and OptiTrack (NaturalPoint, Inc., USA) was evaluated in capturing the joint angles of a humanoid (as ground truth) in a walking test. In order to evaluate the accuracy of the proposed method in capturing the kinematic parameters of a human, lower body joint angles of five healthy subjects were extracted using a Kinect, and the results were compared to Perception Neuron (Noitom Ltd., China) and OptiTrack data during ten gait trials. The absolute agreement and consistency between each optical system and the robot data in the robot test and between each motion capture system and OptiTrack data in the human gait test were determined using intraclass correlations coefficients (ICC3). The reproducibility between systems was evaluated using Lin’s concordance correlation coefficient (CCC). The correlation coefficients with 95% confidence intervals (95%CI) were interpreted substantial for both OptiTrack and proposed method (ICC > 0.75 and CCC > 0.95) in humanoid test. The results of the human gait experiments demonstrated the advantage of the proposed method (ICC > 0.75 and RMSE = 1.1460°) over the Kinect skeleton model (ICC < 0.4 and RMSE = 6.5843°).
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Yamamoto M, Shimatani K, Hasegawa M, Kurita Y, Ishige Y, Takemura H. Accuracy of Temporo-Spatial and Lower Limb Joint Kinematics Parameters Using OpenPose for Various Gait Patterns With Orthosis. IEEE Trans Neural Syst Rehabil Eng 2021; 29:2666-2675. [PMID: 34914592 DOI: 10.1109/tnsre.2021.3135879] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
A cost-effective gait analysis system without attachments and specialized large environments can provide useful information to determine effective treatment in clinical sites. This study investigates the capability of a single camera-based pose estimation system using OpenPose (OP) to measure the temporo-spatial and joint kinematics parameters during gait with orthosis. Eleven healthy adult males walked under different conditions of speed and foot progression angle (FPA). Temporo-spatial and joint kinematics parameters were measured using a single camera-based system with OP and a three-dimensional motion capture system. The limit of agreement, mean absolute error, absolute agreement (ICC2, 1), and relative consistency (ICC3, 1) between the systems under each condition were assessed for reliability and validity. The results demonstrated that most of the ICC for temporo-spatial parameters and hip and knee kinematics parameters were good to excellent (0.60 - 0.98). Conversely, most of the ICC for ankle kinematics in all conditions were poor to fair (< 0.60). Thus, the gait analysis using OP can be used as a clinical assessment tool for determining the temporo-spatial, hip, and knee sagittal plane angles during gait.
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Mehdizadeh S, Faieghi M, Sabo A, Nabavi H, Mansfield A, Flint AJ, Taati B, Iaboni A. Gait changes over time in hospitalized older adults with advanced dementia: Predictors of mobility change. PLoS One 2021; 16:e0259975. [PMID: 34788342 PMCID: PMC8598066 DOI: 10.1371/journal.pone.0259975] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Accepted: 10/30/2021] [Indexed: 11/19/2022] Open
Abstract
People with dementia are at risk of mobility decline. In this study, we measured changes in quantitative gait measures over a maximum 10-week period during the course of a psychogeriatric admission in older adults with dementia, with the aims to describe mobility changes over the duration of the admission, and to determine which factors were associated with this change. Fifty-four individuals admitted to a specialized dementia inpatient unit participated in this study. A vision-based markerless motion capture system was used to record participants' natural gait. Mixed effect models were developed with gait measures as the dependent variables and clinical and demographic variables as predictors. We found that gait stability, step time, and step length decreased, and step time variability and step length variability increased over 10 weeks. Gait stability of men decreased more than that of women, associated with an increased sacrum mediolateral range of motion over time. In addition, the sacrum mediolateral range of motion decreased in those with mild neuropsychiatric symptoms over 10 weeks, but increased in those with more severe neuropsychiatric symptoms. Our study provides evidence of worsening of gait mechanics and control over the course of a hospitalization in older adults with dementia. Quantitative gait monitoring in hospital environments may provide opportunities to intervene to prevent adverse events, decelerate mobility decline, and monitor rehabilitation outcomes.
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Affiliation(s)
- Sina Mehdizadeh
- KITE- Toronto Rehabilitation Institute, University Health Network, Toronto, Ontario, Canada
| | - Mohammadreza Faieghi
- KITE- Toronto Rehabilitation Institute, University Health Network, Toronto, Ontario, Canada
| | - Andrea Sabo
- KITE- Toronto Rehabilitation Institute, University Health Network, Toronto, Ontario, Canada
| | - Hoda Nabavi
- KITE- Toronto Rehabilitation Institute, University Health Network, Toronto, Ontario, Canada
| | - Avril Mansfield
- KITE- Toronto Rehabilitation Institute, University Health Network, Toronto, Ontario, Canada
- Evaluative Clinical Sciences, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, Ontario, Canada
- Department of Physical Therapy, University of Toronto, Toronto, Ontario, Canada
| | - Alastair J. Flint
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
- Centre for Mental Health, University Health Network, Toronto, Ontario, Canada
| | - Babak Taati
- KITE- Toronto Rehabilitation Institute, University Health Network, Toronto, Ontario, Canada
- Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada
- Vector Institute for Artificial Intelligence, Toronto, Ontario, Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
| | - Andrea Iaboni
- KITE- Toronto Rehabilitation Institute, University Health Network, Toronto, Ontario, Canada
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
- Centre for Mental Health, University Health Network, Toronto, Ontario, Canada
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A Review on the Use of Microsoft Kinect for Gait Abnormality and Postural Disorder Assessment. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:4360122. [PMID: 34760141 PMCID: PMC8575610 DOI: 10.1155/2021/4360122] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Accepted: 10/16/2021] [Indexed: 11/18/2022]
Abstract
Gait and posture studies have gained much prominence among researchers and have attracted the interest of clinicians. The ability to detect gait abnormality and posture disorder plays a crucial role in the diagnosis and treatment of some diseases. Microsoft Kinect is presented as a noninvasive sensor essential for medical diagnostic and therapeutic purposes. There are currently no relevant studies that attempt to summarise the existing literature on gait and posture abnormalities using Kinect technology. The purpose of this study is to critically evaluate the existing research on gait and posture abnormalities using the Kinect sensor as the main diagnostic tool. Our studies search identified 458 for gait abnormality, 283 for posture disorder of which 26 studies were included for gait abnormality, and 13 for posture. The results indicate that Kinect sensor is a useful tool for the assessment of kinematic features. In conclusion, Microsoft Kinect sensor is presented as a useful tool for gait abnormality, postural disorder analysis, and physiotherapy. It can also help track the progress of patients who are undergoing rehabilitation.
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Pashley GL, Kahn MB, Williams G, Mentiplay BF, Banky M, Clark RA. Assessment of upper limb abnormalities using the Kinect: Reliability, validity and detection accuracy in people living with acquired brain injury. J Biomech 2021; 129:110825. [PMID: 34736087 DOI: 10.1016/j.jbiomech.2021.110825] [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] [Received: 12/04/2020] [Revised: 10/14/2021] [Accepted: 10/18/2021] [Indexed: 12/30/2022]
Abstract
Upper limb kinematic abnormalities are prevalent in people with acquired brain injury (ABI). We examined if the Microsoft Kinect for Xbox One (Kinect) reliably (test-retest) and validly (concurrent) quantifies upper limb kinematics, and accurately classifies abnormalities (sensitivity/specificity), in an ABI cohort when compared to three-dimensional motion analysis (3DMA) and a subjective rating scale. We compared 42 adults with ABI to 36 healthy control (HC) participants. Walking trials were recorded by 3DMA and Kinect at self-selected (SSWS) and fast (FWS) walking speeds. When classifying abnormalities for 3DMA and Kinect, a 95% reference range (based on HC data) was calculated using the Kinematic Deviation Score worst axis (KDSw); values outside of this range were classified abnormal. Scores ≥ 2 in the subjective rating scale, based on International Classification of Functioning, Disability and Health Framework's Qualifiers Scale, were considered abnormal. Test-retest reliability and concurrent validity were determined using intra-class correlation coefficient (Absolute ICC2,1) and Pearson's or Spearman's correlation respectively. Fisher's Exact Test was conducted to determine sensitivity and specificity between each combination of the two methods. Strong test-retest reliability was observed for 3DMA (median(IQR) ICC:0.86(0.85-0.90)). Kinect showed overall strong SSWS test-retest reliability (ICC:0.87(0.84-0.91)) and moderate FWS test-retest reliability (ICC:0.61(0.56-0.65)). Concurrent validity between 3DMA and Kinect was overall moderate. Sensitivity and specificity between 3DMA, Kinect and subjective scores were overall modest. Our results suggest caution should be used if implementing Kinect as its validity is modest against criterion-reference 3DMA; however, given its reliability and similar sensitivity/specificity to 3DMA further responsiveness research is warranted.
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Affiliation(s)
- Gabrielle L Pashley
- School of Health and Behavioural Sciences, University of the Sunshine Coast, QLD, Australia
| | - Michelle B Kahn
- School of Health and Behavioural Sciences, University of the Sunshine Coast, QLD, Australia; Department of Physiotherapy, Epworth Rehabilitation, Epworth Healthcare, Melbourne, VIC, Australia
| | - Gavin Williams
- Department of Physiotherapy, Epworth Rehabilitation, Epworth Healthcare, Melbourne, VIC, Australia; School of Physiotherapy, The University of Melbourne, Melbourne, Australia
| | - Benjamin F Mentiplay
- La Trobe Sport and Exercise Medicine Research Centre, La Trobe University, Australia
| | - Megan Banky
- Department of Physiotherapy, Epworth Rehabilitation, Epworth Healthcare, Melbourne, VIC, Australia
| | - Ross A Clark
- School of Health and Behavioural Sciences, University of the Sunshine Coast, QLD, Australia.
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Mehdizadeh S, Nabavi H, Sabo A, Arora T, Iaboni A, Taati B. Concurrent validity of human pose tracking in video for measuring gait parameters in older adults: a preliminary analysis with multiple trackers, viewing angles, and walking directions. J Neuroeng Rehabil 2021; 18:139. [PMID: 34526074 PMCID: PMC8443117 DOI: 10.1186/s12984-021-00933-0] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Accepted: 09/01/2021] [Indexed: 12/02/2022] Open
Abstract
Background Many of the available gait monitoring technologies are expensive, require specialized expertise, are time consuming to use, and are not widely available for clinical use. The advent of video-based pose tracking provides an opportunity for inexpensive automated analysis of human walking in older adults using video cameras. However, there is a need to validate gait parameters calculated by these algorithms against gold standard methods for measuring human gait data in this population. Methods We compared quantitative gait variables of 11 older adults (mean age = 85.2) calculated from video recordings using three pose trackers (AlphaPose, OpenPose, Detectron) to those calculated from a 3D motion capture system. We performed comparisons for videos captured by two cameras at two different viewing angles, and viewed from the front or back. We also analyzed the data when including gait variables of individual steps of each participant or each participant’s averaged gait variables. Results Our findings revealed that, i) temporal (cadence and step time), but not spatial and variability gait measures (step width, estimated margin of stability, coefficient of variation of step time and width), calculated from the video pose tracking algorithms correlate significantly to that of motion capture system, and ii) there are minimal differences between the two camera heights, and walks viewed from the front or back in terms of correlation of gait variables, and iii) gait variables extracted from AlphaPose and Detectron had the highest agreement while OpenPose had the lowest agreement. Conclusions There are important opportunities to evaluate models capable of 3D pose estimation in video data, improve the training of pose-tracking algorithms for older adult and clinical populations, and develop video-based 3D pose trackers specifically optimized for quantitative gait measurement.
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Affiliation(s)
- Sina Mehdizadeh
- KITE- Toronto Rehabilitation Institute, University Health Network, 550 University Ave., Toronto, ON, M5G 2A2, Canada
| | - Hoda Nabavi
- KITE- Toronto Rehabilitation Institute, University Health Network, 550 University Ave., Toronto, ON, M5G 2A2, Canada
| | - Andrea Sabo
- KITE- Toronto Rehabilitation Institute, University Health Network, 550 University Ave., Toronto, ON, M5G 2A2, Canada
| | - Twinkle Arora
- KITE- Toronto Rehabilitation Institute, University Health Network, 550 University Ave., Toronto, ON, M5G 2A2, Canada
| | - Andrea Iaboni
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada.,KITE- Toronto Rehabilitation Institute, University Health Network, 550 University Ave., Toronto, ON, M5G 2A2, Canada.,Centre for Mental Health, University Health Network, Toronto, ON, Canada
| | - Babak Taati
- Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON, Canada. .,Department of Computer Science, University of Toronto, Toronto, ON, Canada. .,KITE- Toronto Rehabilitation Institute, University Health Network, 550 University Ave., Toronto, ON, M5G 2A2, Canada. .,Vector Institute for Artificial Intelligence, Toronto, ON, Canada.
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46
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Gu X, Guo Y, Yang GZ, Lo B. Cross-Domain Self-Supervised Complete Geometric Representation Learning for Real-Scanned Point Cloud Based Pathological Gait Analysis. IEEE J Biomed Health Inform 2021; 26:1034-1044. [PMID: 34449400 DOI: 10.1109/jbhi.2021.3107532] [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: 11/09/2022]
Abstract
Accurate lower-limb pose estimation is a prerequisite of skeleton based pathological gait analysis. To achieve this goal in free-living environments for long-term monitoring, single depth sensor has been proposed in research. However, the depth map acquired from a single viewpoint encodes only partial geometric information of the lower limbs and exhibits large variations across different viewpoints. Existing off-the-shelf three-dimensional (3D) pose tracking algorithms and public datasets for depth based human pose estimation are mainly targeted at activity recognition applications. They are relatively insensitive to skeleton estimation accuracy, especially at the foot segments. Furthermore, acquiring ground truth skeleton data for detailed biomechanics analysis also requires considerable effort. To address these issues, we propose a novel cross-domain self-supervised complete geometric representation learning framework, with knowledge transfer from the unlabelled synthetic point clouds of full lower-limb surfaces. The proposed method can significantly reduce the number of ground truth skeletons (with only 1\%) in the training phase, meanwhile ensuring accurate and precise pose estimation and capturing discriminative features across different pathological gait patterns compared to other methods.
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Bravi M, Massaroni C, Santacaterina F, Di Tocco J, Schena E, Sterzi S, Bressi F, Miccinilli S. Validity Analysis of WalkerView TM Instrumented Treadmill for Measuring Spatiotemporal and Kinematic Gait Parameters. SENSORS 2021; 21:s21144795. [PMID: 34300534 PMCID: PMC8309770 DOI: 10.3390/s21144795] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Revised: 06/25/2021] [Accepted: 07/09/2021] [Indexed: 11/20/2022]
Abstract
The detection of gait abnormalities is essential for professionals involved in the rehabilitation of walking disorders. Instrumented treadmills are spreading as an alternative to overground gait analysis. To date, the use of these instruments for recording kinematic gait parameters is still limited in clinical practice due to the lack of validation studies. This study aims to investigate the performance of a multi-sensor instrumented treadmill (i.e., WalkerViewTM, WV) for performing gait analysis. Seventeen participants performed a single gait test on the WV at three different speeds (i.e., 3 km/h, 5 km/h, and 6.6 km/h). In each trial, spatiotemporal and kinematic parameters were recorded simultaneously by the WV and by a motion capture system used as the reference. Intraclass correlation coefficient (ICC) of spatiotemporal parameters showed fair to excellent agreement at the three walking speeds for steps time, cadence, and step length (range 0.502–0.996); weaker levels of agreement were found for stance and swing time at all the tested walking speeds. Bland–Altman analysis of spatiotemporal parameters showed a mean of difference (MOD) maximum value of 0.04 s for swing/stance time and WV underestimation of 2.16 cm for step length. As for kinematic variables, ICC showed fair to excellent agreement (ICC > 0.5) for total range of motion (ROM) of hip at 3 km/h (range 0.579–0.735); weaker levels of ICC were found at 5 km/h and 6.6 km/h (range 0.219–0.447). ICC values of total knee ROM showed poor levels of agreement at all the tested walking speeds. Bland–Altman analysis of hip ROM revealed a higher MOD value at higher speeds up to 3.91°; the MOD values of the knee ROM were always higher than 7.67° with a 60° mean value of ROM. We demonstrated that the WV is a valid tool for analyzing the spatiotemporal parameters of walking and assessing the hip’s total ROM. Knee total ROM and all kinematic peak values should be carefully evaluated, having shown lower levels of agreement.
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Affiliation(s)
- Marco Bravi
- Unit of Physical Medicine and Rehabilitation, Università Campus Bio-Medico di Roma, via Alvaro Del Portillo 5, 00128 Rome, Italy; (M.B.); (F.S.); (S.S.); (F.B.); (S.M.)
| | - Carlo Massaroni
- Unit of Measurements and Biomedical Instrumentation, Università Campus Bio-Medico di Roma, via Alvaro Del Portillo 21, 00128 Rome, Italy; (J.D.T.); (E.S.)
- Correspondence:
| | - Fabio Santacaterina
- Unit of Physical Medicine and Rehabilitation, Università Campus Bio-Medico di Roma, via Alvaro Del Portillo 5, 00128 Rome, Italy; (M.B.); (F.S.); (S.S.); (F.B.); (S.M.)
| | - Joshua Di Tocco
- Unit of Measurements and Biomedical Instrumentation, Università Campus Bio-Medico di Roma, via Alvaro Del Portillo 21, 00128 Rome, Italy; (J.D.T.); (E.S.)
| | - Emiliano Schena
- Unit of Measurements and Biomedical Instrumentation, Università Campus Bio-Medico di Roma, via Alvaro Del Portillo 21, 00128 Rome, Italy; (J.D.T.); (E.S.)
| | - Silvia Sterzi
- Unit of Physical Medicine and Rehabilitation, Università Campus Bio-Medico di Roma, via Alvaro Del Portillo 5, 00128 Rome, Italy; (M.B.); (F.S.); (S.S.); (F.B.); (S.M.)
| | - Federica Bressi
- Unit of Physical Medicine and Rehabilitation, Università Campus Bio-Medico di Roma, via Alvaro Del Portillo 5, 00128 Rome, Italy; (M.B.); (F.S.); (S.S.); (F.B.); (S.M.)
| | - Sandra Miccinilli
- Unit of Physical Medicine and Rehabilitation, Università Campus Bio-Medico di Roma, via Alvaro Del Portillo 5, 00128 Rome, Italy; (M.B.); (F.S.); (S.S.); (F.B.); (S.M.)
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Carrillo-Díaz M, Lacomba-Trejo L, Romero-Maroto M, González-Olmo MJ. Facial Self-Touching and the Propagation of COVID-19: The Role of Gloves in the Dental Practice. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18136983. [PMID: 34209991 PMCID: PMC8296903 DOI: 10.3390/ijerph18136983] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/30/2021] [Revised: 06/18/2021] [Accepted: 06/26/2021] [Indexed: 12/23/2022]
Abstract
BACKGROUND Despite facial self-touching being a possible source of transmission of SARS-Co-V-2 its role in dental practice has not been studied. Factors such as anxiety symptoms or threat perception of COVID-19 may increase the possibility of contagion. The objective was to compare the impact of control measures, such as gloves or signs in the reduction in facial self-touching. METHODS An intra-subject design was undertaken with 150 adults. The patients' movements in the waiting room were monitored with Microsoft Kinect software on three occasions: without any control measures, using plastic gloves or using advisory signs against self-touching. Additionally, the participants completed the sub-scale of STAI (State-Anxiety) and the BIP-Q5 (Brief Illness Perception Questionnaire); their blood pressure and heart rate were recorded. RESULTS The lowest incidence of facial self-touching occurred in the experimental situation in which gloves were introduced. The subjects with elevated anxiety symptoms realized more facial self-touching regardless of the control measures. However, the threat perception of COVID-19 is associated negatively with facial self-touching. CONCLUSIONS The use of gloves is a useful control measure in the reduction in facial touching. However, people with anxiety symptoms regardless of whether they have greater threat perception for COVID-19 exhibit more facial touching.
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Affiliation(s)
- María Carrillo-Díaz
- Department of Orthodontics and Pediatric Dentistry, Rey Juan Carlos University, Alcorcón, 28922 Madrid, Spain; (M.C.-D.); (M.R.-M.)
| | - Laura Lacomba-Trejo
- Department of Personality, Assessment and Psychological Treatments, Faculty of Psychology, University of Valencia, Av. Blasco Ibáñez, 21, 46010 Valencia, Spain;
| | - Martín Romero-Maroto
- Department of Orthodontics and Pediatric Dentistry, Rey Juan Carlos University, Alcorcón, 28922 Madrid, Spain; (M.C.-D.); (M.R.-M.)
| | - María José González-Olmo
- Department of Orthodontics and Pediatric Dentistry, Rey Juan Carlos University, Alcorcón, 28922 Madrid, Spain; (M.C.-D.); (M.R.-M.)
- Correspondence:
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Yeung LF, Yang Z, Cheng KCC, Du D, Tong RKY. Effects of camera viewing angles on tracking kinematic gait patterns using Azure Kinect, Kinect v2 and Orbbec Astra Pro v2. Gait Posture 2021; 87:19-26. [PMID: 33878509 DOI: 10.1016/j.gaitpost.2021.04.005] [Citation(s) in RCA: 54] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Revised: 03/17/2021] [Accepted: 04/02/2021] [Indexed: 02/02/2023]
Abstract
BACKGROUND Depth sensors could be a portable, affordable, marker-less alternative to three-dimension motion capture systems for gait analysis, but the effects of camera viewing angles on their joint angle tracking performance have not been fully investigated. RESEARCH QUESTIONS This study evaluated the accuracies of three depth sensors [Azure Kinect (AK); Kinect v2 (K2); Orbbec Astra (OA)] for tracking kinematic gait patterns during treadmill walking at five camera viewing angles (0°/22.5°/45°/67.5°/90°). METHODS Ten healthy subjects performed fifteen treadmill walking trials (3 speeds × 5 viewing angles) using the three depth sensors to measure joint angles in sagittal hip, frontal hip, sagittal knee, and sagittal ankle. Ten walking steps were recorded and averaged for each walking trial. Range of motion in terms of maximum and minimum joint angles measured by the depth sensors were compared with the Vicon motion capture system as the gold standard. Depth sensors tracking accuracies were compared against the Vicon reference using root-mean-square error (RMSE) on the joint angle time series. Effects of different walking speeds, viewing angles, and depth sensors on the tracking accuracy were observed using three-way repeated-measure analysis of variance (ANOVA). RESULTS ANOVA results on RMSE showed significant interaction effects between viewing angles and depth sensors for sagittal hip [F(8,72) = 4.404, p = 0.005] and for sagittal knee [F(8,72)=13.211, p < 0.001] joint angles. AK had better tracking performance when subjects walked at non-frontal camera viewing angles (22.5°/45°/67.5°/90°); while K2 performed better at frontal viewing angle (0°). The superior tracking performance of AK compared with K2/OA might be attributed to the improved depth sensor resolution and body tracking algorithm. SIGNIFICANCE Researchers should be cautious about camera viewing angle when using depth sensors for kinematic gait measurements. Our results demonstrated Azure Kinect had good tracking performance of sagittal hip and sagittal knee joint angles during treadmill walking tests at non-frontal camera viewing angles.
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Affiliation(s)
- Ling-Fung Yeung
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong
| | - Zhenqun Yang
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong
| | | | - Dan Du
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong; College of Computer Science, Sichuan University, Chengdu, Sichuan, China
| | - Raymond Kai-Yu Tong
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong.
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Liu X, Zhao C, Zheng B, Guo Q, Duan X, Wulamu A, Zhang D. Wearable Devices for Gait Analysis in Intelligent Healthcare. FRONTIERS IN COMPUTER SCIENCE 2021. [DOI: 10.3389/fcomp.2021.661676] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
In this study, we review the role of wearable devices in tracking our daily locomotion. We discuss types of wearable devices that can be used, methods for gait analyses, and multiple healthcare-related applications aided by artificial intelligence. Impaired walking and locomotion are common resulting from injuries, degenerative pathologies, musculoskeletal disorders, and various neurological damages. Daily tracking and gait analysis are convenient and efficient approaches for monitoring human walking, where concreate and rich data can be obtained for examining our posture control mechanism during body movement and providing enhanced clinical pieces of evidence for diagnoses and treatments. Many sensors in wearable devices can help to record data of walking and running; spatiotemporal and kinematic variables can be further calculated in gait analysis. We report our previous works in gait analysis, discussing applications of wearable devices for detecting foot and ankle lesions, supporting surgeons in early diagnosis, and helping physicians with rehabilitation.
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