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Vun DSY, Bowers R, McGarry A. Vision-based motion capture for the gait analysis of neurodegenerative diseases: A review. Gait Posture 2024; 112:95-107. [PMID: 38754258 DOI: 10.1016/j.gaitpost.2024.04.029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Revised: 04/25/2024] [Accepted: 04/26/2024] [Indexed: 05/18/2024]
Abstract
BACKGROUND Developments in vision-based systems and human pose estimation algorithms have the potential to detect, monitor and intervene early on neurodegenerative diseases through gait analysis. However, the gap between the technology available and actual clinical practice is evident as most clinicians still rely on subjective observational gait analysis or objective marker-based analysis that is time-consuming. RESEARCH QUESTION This paper aims to examine the main developments of vision-based motion capture and how such advances may be integrated into clinical practice. METHODS The literature review was conducted in six online databases using Boolean search terms. A commercial system search was also included. A predetermined methodological criterion was then used to assess the quality of the selected articles. RESULTS A total of seventeen studies were evaluated, with thirteen studies focusing on gait classification systems and four studies on gait measurement systems. Of the gait classification systems, nine studies utilized artificial intelligence-assisted techniques, while four studies employed statistical techniques. The results revealed high correlations of gait features identified by classifier models with existing clinical rating scales. These systems demonstrated generally high classification accuracies and were effective in diagnosing disease severity levels. Gait measurement systems that extract spatiotemporal and kinematic joint information from video data generally found accurate measurements of gait parameters with low mean absolute errors, high intra- and inter-rater reliability. SIGNIFICANCE Low cost, portable vision-based systems can provide proof of concept for the quantification of gait, expansion of gait assessment tools, remote gait analysis of neurodegenerative diseases and a point of care system for orthotic evaluation. However, certain challenges, including small sample sizes, occlusion risks, and selection bias in training models, need to be addressed. Nevertheless, these systems can serve as complementary tools, equipping clinicians with essential gait information to objectively assess disease severity and tailor personalized treatment for enhanced patient care.
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Affiliation(s)
- David Sing Yee Vun
- National Centre for Prosthetics and Orthotics, Department of Biomedical Engineering, University of Strathclyde, Glasgow, UK
| | - Robert Bowers
- National Centre for Prosthetics and Orthotics, Department of Biomedical Engineering, University of Strathclyde, Glasgow, UK
| | - Anthony McGarry
- National Centre for Prosthetics and Orthotics, Department of Biomedical Engineering, University of Strathclyde, Glasgow, UK.
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Lozano-Garcia M, Doheny EP, Mann E, Morgan-Jones P, Drew C, Busse-Morris M, Lowery MM. Estimation of Gait Parameters in Huntington's Disease Using Wearable Sensors in the Clinic and Free-living Conditions. IEEE Trans Neural Syst Rehabil Eng 2024; 32:2239-2249. [PMID: 38819972 DOI: 10.1109/tnsre.2024.3407887] [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: 06/02/2024]
Abstract
In Huntington's disease (HD), wearable inertial sensors could capture subtle changes in motor function. However, disease-specific validation of methods is necessary. This study presents an algorithm for walking bout and gait event detection in HD using a leg-worn accelerometer, validated only in the clinic and deployed in free-living conditions. Seventeen HD participants wore shank- and thigh-worn tri-axial accelerometers, and a wrist-worn device during two-minute walk tests in the clinic, with video reference data for validation. Thirteen participants wore one of the thigh-worn tri-axial accelerometers (AP: ActivPAL4) and the wrist-worn device for 7 days under free-living conditions, with proprietary AP data used as reference. Gait events were detected from shank and thigh acceleration using the Teager-Kaiser energy operator combined with unsupervised clustering. Estimated step count (SC) and temporal gait parameters were compared with reference data. In the clinic, low mean absolute percentage errors were observed for stride (shank/thigh: 0.6/0.9%) and stance (shank/thigh: 3.3/7.1%) times, and SC (shank/thigh: 3.1%). Similar errors were observed for proprietary AP SC (3.2%), with higher errors observed for the wrist-worn device (10.9%). At home, excellent agreement was observed between the proposed algorithm and AP software for SC and time spent walking (ICC [Formula: see text]). The wrist-worn device overestimated SC by 34.2%. The presented algorithm additionally allowed stride and stance time estimation, whose variability correlated significantly with clinical motor scores. The results demonstrate a new method for accurate estimation of HD gait parameters in the clinic and free-living conditions, using a single accelerometer worn on either the thigh or shank.
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Single M, Bruhin LC, Naef AC, Krack P, Nef T, Gerber SM. Unobtrusive measurement of gait parameters using seismographs: An observational study. Sci Rep 2024; 14:14487. [PMID: 38914628 PMCID: PMC11196696 DOI: 10.1038/s41598-024-64508-4] [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: 09/26/2023] [Accepted: 06/10/2024] [Indexed: 06/26/2024] Open
Abstract
Analyzing irregularities in walking patterns helps detect human locomotion abnormalities that can signal health changes. Traditional observation-based assessments have limitations due to subjective biases and capture only a single time point. Ambient and wearable sensor technologies allow continuous and objective locomotion monitoring but face challenges due to the need for specialized expertise and user compliance. This work proposes a seismograph-based algorithm for quantifying human gait, incorporating a step extraction algorithm derived from mathematical morphologies, with the goal of achieving the accuracy of clinical reference systems. To evaluate our method, we compared the gait parameters of 50 healthy participants, as recorded by seismographs, and those obtained from reference systems (a pressure-sensitive walkway and a camera system). Participants performed four walking tests, including traversing a walkway and completing the timed up-and-go (TUG) test. In our findings, we observed linear relationships with strong positive correlations (R2 > 0.9) and tight 95% confidence intervals for all gait parameters (step time, cycle time, ambulation time, and cadence). We demonstrated that clinical gait parameters and TUG mobility test timings can be accurately derived from seismographic signals, with our method exhibiting no significant differences from established clinical reference systems.
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Affiliation(s)
- Michael Single
- Gerontechnology and Rehabilitation Group, ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland.
| | - Lena C Bruhin
- Gerontechnology and Rehabilitation Group, ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
| | - Aileen C Naef
- Gerontechnology and Rehabilitation Group, ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
| | - Paul Krack
- Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Tobias Nef
- Gerontechnology and Rehabilitation Group, ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
- Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Stephan M Gerber
- Gerontechnology and Rehabilitation Group, ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
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Bonato P, Feipel V, Corniani G, Arin-Bal G, Leardini A. Position paper on how technology for human motion analysis and relevant clinical applications have evolved over the past decades: Striking a balance between accuracy and convenience. Gait Posture 2024; 113:191-203. [PMID: 38917666 DOI: 10.1016/j.gaitpost.2024.06.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Revised: 05/30/2024] [Accepted: 06/10/2024] [Indexed: 06/27/2024]
Abstract
BACKGROUND Over the past decades, tremendous technological advances have emerged in human motion analysis (HMA). RESEARCH QUESTION How has technology for analysing human motion evolved over the past decades, and what clinical applications has it enabled? METHODS The literature on HMA has been extensively reviewed, focusing on three main approaches: Fully-Instrumented Gait Analysis (FGA), Wearable Sensor Analysis (WSA), and Deep-Learning Video Analysis (DVA), considering both technical and clinical aspects. RESULTS FGA techniques relying on data collected using stereophotogrammetric systems, force plates, and electromyographic sensors have been dramatically improved providing highly accurate estimates of the biomechanics of motion. WSA techniques have been developed with the advances in data collection at home and in community settings. DVA techniques have emerged through artificial intelligence, which has marked the last decade. Some authors have considered WSA and DVA techniques as alternatives to "traditional" HMA techniques. They have suggested that WSA and DVA techniques are destined to replace FGA. SIGNIFICANCE We argue that FGA, WSA, and DVA complement each other and hence should be accounted as "synergistic" in the context of modern HMA and its clinical applications. We point out that DVA techniques are especially attractive as screening techniques, WSA methods enable data collection in the home and community for extensive periods of time, and FGA does maintain superior accuracy and should be the preferred technique when a complete and highly accurate biomechanical data is required. Accordingly, we envision that future clinical applications of HMA would favour screening patients using DVA in the outpatient setting. If deemed clinically appropriate, then WSA would be used to collect data in the home and community to derive relevant information. If accurate kinetic data is needed, then patients should be referred to specialized centres where an FGA system is available, together with medical imaging and thorough clinical assessments.
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Affiliation(s)
- Paolo Bonato
- Dept of PM&R, Harvard Medical School, Spaulding Rehabilitation Hospital, Boston, USA
| | - Véronique Feipel
- Laboratory of Functional Anatomy, Faculty of Motor Sciences, Laboratory of Anatomy, Biomechanics and Organogenesis, Faculty of Medicine, Université Libre de Bruxelles, Brussels, Belgium
| | - Giulia Corniani
- Dept of PM&R, Harvard Medical School, Spaulding Rehabilitation Hospital, Boston, USA
| | - Gamze Arin-Bal
- Faculty of Physical Therapy and Rehabilitation, Hacettepe University, Ankara, Turkey; Movement Analysis Laboratory, IRCCS Istituto Ortopedico Rizzoli, Bologna, Italy.
| | - Alberto Leardini
- Movement Analysis Laboratory, IRCCS Istituto Ortopedico Rizzoli, Bologna, Italy
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Zhao H, Xie J, Chen Y, Cao J, Liao WH, Cao H. Diagnosis of neurodegenerative diseases with a refined Lempel-Ziv complexity. Cogn Neurodyn 2024; 18:1153-1166. [PMID: 38826647 PMCID: PMC11143150 DOI: 10.1007/s11571-023-09973-9] [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: 08/06/2022] [Revised: 04/03/2023] [Accepted: 04/19/2023] [Indexed: 06/04/2024] Open
Abstract
The investigation into the distinctive difference of gait is of significance for the clinical diagnosis of neurodegenerative diseases. However, human gait is affected by many factors like behavior, occupation and so on, and they may confuse the gait differences among Parkinson's disease, amyotrophic lateral sclerosis, and Huntington's disease. For the purpose of examining distinctive gait differences of neurodegenerative diseases, this study extracts various features from both vertical ground reaction force and time intervals. Moreover, refined Lempel-Ziv complexity is proposed considering the detailed distribution of signals based on the median and quartiles. Basic features (mean, coefficient of variance, and the asymmetry index), nonlinear dynamic features (Hurst exponent, correlation dimension, largest Lyapunov exponent), and refined Lempel-Ziv complexity of different neurodegenerative diseases are compared statistically by violin plot and Kruskal-Wallis test to reveal distinction and regularities. The comparative analysis results illustrate the gait differences across these neurodegenerative diseases by basic features and nonlinear dynamic features. Classification results by random forest indicate that the refined Lempel-Ziv complexity can robustly enhance the diagnosis accuracy when combined with basic features.
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Affiliation(s)
- Huan Zhao
- Key Laboratory of Education Ministry for Modern Design and Rotor Bearing System, Xi’an Jiaotong University, 28 Xianning West Road, 710049 Xi’an, China
| | - Junxiao Xie
- Key Laboratory of Education Ministry for Modern Design and Rotor Bearing System, Xi’an Jiaotong University, 28 Xianning West Road, 710049 Xi’an, China
| | - Yangquan Chen
- School of Engineering, University of California at Merced, Merced, CA 95343 USA
| | - Junyi Cao
- Key Laboratory of Education Ministry for Modern Design and Rotor Bearing System, Xi’an Jiaotong University, 28 Xianning West Road, 710049 Xi’an, China
| | - Wei-Hsin Liao
- Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, N.T., 999077 Hong Kong, China
| | - Hongmei Cao
- Department of Neurology, First Affiliated Hospital of Xi’an Jiaotong University, 277 West Yanta Road, 710061 Xi’an, China
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Lu H, Xu S, Zhao S, Hu X, Ma R, Hu B. EPIC: Emotion Perception by Spatio-Temporal Interaction Context of Gait. IEEE J Biomed Health Inform 2024; 28:2592-2601. [PMID: 37018306 DOI: 10.1109/jbhi.2022.3233597] [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: 01/03/2023]
Abstract
Recently, psychophysiological computing has received considerable attention. Due to easy acquisition at a distance and less conscious initiation, gait-based emotion recognition is considered as a valuable research branch in the field of psychophysiological computing. However, most existing methods rarely explore the spatio-temporal context of gait, which limits the ability to capture the higher-order relationship between emotion and gait. In this paper, we utilize a range of research, including psychophysiological computing and artificial intelligence, to propose an integrated emotion perception framework called EPIC, which can find novel joint topology and generate thousands of synthetic gaits by spatio-temporal interaction context. First, we analyze the joint coupling among non-adjacent joints by calculating Phase Lag Index (PLI), which can discover the latent connection among body joints. Second, to synthesize more sophisticated and accurate gait sequences, we explore the effect of spatio-temporal constraints, and propose a new loss function that utilizes the Dynamic Time Warping (DTW) algorithm and pseudo-velocity curve to constrain the output of Gated Recurrent Units (GRU). Finally, Spatial Temporal Graph Convolution Networks (ST-GCN) is used to classify emotions using the generation and the real data. Experimental results demonstrate our approach achieves the accuracy of 89.66%, and outperforms the state-of-the-art methods on Emotion-Gait dataset.
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Zhan F, Lin G, Su L, Xue L, Duan K, Chen L, Ni J. The association between methylmalonic acid, a biomarker of mitochondrial dysfunction, and cause-specific mortality in Alzheimer's disease and Parkinson's disease. Heliyon 2024; 10:e29357. [PMID: 38681550 PMCID: PMC11053175 DOI: 10.1016/j.heliyon.2024.e29357] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 04/04/2024] [Accepted: 04/05/2024] [Indexed: 05/01/2024] Open
Abstract
Background Alzheimer's disease (AD) and Parkinson's disease (PD) are the leading causes of death among the elderly. Recent research has demonstrated that mitochondrial dysfunction, which is hallmark of neurodegenerative diseases, is a contributor to the development of these diseases. Methods and materials Methylmalonic acid (MMA), AD, PD, inflammatory markers and covariates were extracted from the National Health and Nutrition Examination Survey (NHANES). The classification of the inflammatory markers was done through quartile conversion. A restricted cubic spike function was performed to study their dose-response relationship. MMA subgroups from published studies were used to explore the correlation between different subgroups and cause-specific mortality. Multivariable weighted Cox regression was carried out to investigate MMA and cause-specific mortality in patients with AD and PD. Weighted survival analysis was used to study the survival differences among MMA subgroups. Results A non-linear correlation was observed between MMA and AD-specific death and PD-specific mortality. The presence of MMA Q4 was linked to increased death rates among AD patients (HR = 6.39, 95%CI: 1.19-35.24, P = 0.03) after controlling for potential confounders in a multivariable weighted Cox regression model. In PD patients, the MMA Q4 (Q4: HR: 5.51, 95 % CI: 1.26-24, P = 0.02) was also related to increased mortality. The results of survival analysis indicated that the poorer prognoses were observed in AD and PD patients with MMA Q4. Conclusion The higher level of mitochondria-derived circulating MMA was associated with a higher mortality rate in AD and PD patients. MMA has the potential to be a valuable indicator for evaluating AD and PD patients' prognosis in the clinic.
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Affiliation(s)
- Fangfang Zhan
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, 350000, China
- Department of Rehabilitation Medicine, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, 350212, China
| | - Gaoteng Lin
- Department of Urology, The 900th Hospital of Joint Logistic Support Force, Fuzhou, China
| | - Lifang Su
- Department of Neurology, The Affiliated Hospital of Putian University, Putian, 351106, China
| | - Lihong Xue
- Department of Neurology, The Affiliated Hospital of Putian University, Putian, 351106, China
| | - Kefei Duan
- Department of Geriatric Medicine, Tianjin Medical University General Hospital, Tianjin, China
| | - Longfei Chen
- Department of Neurology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, 350212, China
- Department of Neurology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, 350000, China
| | - Jun Ni
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, 350000, China
- Department of Rehabilitation Medicine, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, 350212, China
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Luo X, Tan H, Wen W. Recent Advances in Wearable Healthcare Devices: From Material to Application. Bioengineering (Basel) 2024; 11:358. [PMID: 38671780 PMCID: PMC11048539 DOI: 10.3390/bioengineering11040358] [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: 03/06/2024] [Revised: 04/02/2024] [Accepted: 04/04/2024] [Indexed: 04/28/2024] Open
Abstract
In recent years, the proliferation of wearable healthcare devices has marked a revolutionary shift in the personal health monitoring and management paradigm. These devices, ranging from fitness trackers to advanced biosensors, have not only made healthcare more accessible, but have also transformed the way individuals engage with their health data. By continuously monitoring health signs, from physical-based to biochemical-based such as heart rate and blood glucose levels, wearable technology offers insights into human health, enabling a proactive rather than a reactive approach to healthcare. This shift towards personalized health monitoring empowers individuals with the knowledge and tools to make informed decisions about their lifestyle and medical care, potentially leading to the earlier detection of health issues and more tailored treatment plans. This review presents the fabrication methods of flexible wearable healthcare devices and their applications in medical care. The potential challenges and future prospectives are also discussed.
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Affiliation(s)
- Xiao Luo
- Department of Physics, The Hong Kong University of Science and Technology, Hong Kong 999077, China;
- HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute (SHCIRI), Futian, Shenzhen 518060, China
| | - Handong Tan
- Department of Individualized Interdisciplinary Program (Advanced Materials), The Hong Kong University of Science and Technology, Hong Kong 999077, China;
| | - Weijia Wen
- Department of Physics, The Hong Kong University of Science and Technology, Hong Kong 999077, China;
- HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute (SHCIRI), Futian, Shenzhen 518060, China
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Wang H, Su B, Lu L, Jung S, Qing L, Xie Z, Xu X. Markerless gait analysis through a single camera and computer vision. J Biomech 2024; 165:112027. [PMID: 38430608 DOI: 10.1016/j.jbiomech.2024.112027] [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: 04/21/2023] [Revised: 02/23/2024] [Accepted: 02/26/2024] [Indexed: 03/05/2024]
Abstract
The assessment of gait performance using quantitative measures can yield crucial insights into an individual's health status. Recently, computer vision-based human pose estimation has emerged as a promising solution for markerless gait analysis, as it allows for the direct extraction of gait parameters from videos. This study aimed to compare the lower extremity kinematics and spatiotemporal gait parameters obtained from a single-camera-based markerless method with those acquired from a marker-based motion tracking system across a healthy population. Additionally, we investigated the impact of camera viewing angles and distances on the accuracy of the markerless method. Our findings demonstrated a robust correlation and agreement (Rxy > 0.75, Rc > 0.7) between the markerless and marker-based methods for most spatiotemporal gait parameters. We also observed strong correlations (Rxy > 0.8) between the two methods for hip flexion/extension, knee flexion/extension, hip abduction/adduction, and hip internal/external rotation. Statistical tests revealed significant effects of viewing angles and distances on the accuracy of the identified gait parameters. While the markerless method offers an alternative for general gait analysis, particularly when marker use is impractical, its accuracy for clinical applications remains insufficient and requires substantial improvement. Future investigations should explore the potential of the markerless system to measure gait parameters in pathological gaits.
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Affiliation(s)
- Hanwen Wang
- Edward P. Fitts Department of Industrial and Systems Engineering North, Carolina State University, Raleigh NC, 27695, USA
| | - Bingyi Su
- Edward P. Fitts Department of Industrial and Systems Engineering North, Carolina State University, Raleigh NC, 27695, USA
| | - Lu Lu
- Edward P. Fitts Department of Industrial and Systems Engineering North, Carolina State University, Raleigh NC, 27695, USA
| | - Sehee Jung
- Edward P. Fitts Department of Industrial and Systems Engineering North, Carolina State University, Raleigh NC, 27695, USA
| | - Liwei Qing
- Edward P. Fitts Department of Industrial and Systems Engineering North, Carolina State University, Raleigh NC, 27695, USA
| | - Ziyang Xie
- Edward P. Fitts Department of Industrial and Systems Engineering North, Carolina State University, Raleigh NC, 27695, USA
| | - Xu Xu
- Edward P. Fitts Department of Industrial and Systems Engineering North, Carolina State University, Raleigh NC, 27695, USA.
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Kwon DY. Predicting the Future Fall Risk Using Challenging Tasks: Importance of Sensor-Based Quantitative Measurements of Gait in Parkinson's Disease. J Clin Neurol 2024; 20:117-118. [PMID: 38433483 PMCID: PMC10921056 DOI: 10.3988/jcn.2024.0051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Accepted: 01/26/2024] [Indexed: 03/05/2024] Open
Affiliation(s)
- Do-Young Kwon
- Department of Neurology, Korea University Ansan Hospital, Korea University College of Medicine, Ansan, Korea.
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Single M, Bruhin LC, Colombo A, Möri K, Gerber SM, Lahr J, Krack P, Klöppel S, Müri RM, Mosimann UP, Nef T. A Transferable Lidar-Based Method to Conduct Contactless Assessments of Gait Parameters in Diverse Home-like Environments. SENSORS (BASEL, SWITZERLAND) 2024; 24:1172. [PMID: 38400329 PMCID: PMC10893300 DOI: 10.3390/s24041172] [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: 12/19/2023] [Revised: 02/08/2024] [Accepted: 02/09/2024] [Indexed: 02/25/2024]
Abstract
Gait abnormalities in older adults are linked to increased risks of falls, institutionalization, and mortality, necessitating accurate and frequent gait assessments beyond traditional clinical settings. Current methods, such as pressure-sensitive walkways, often lack the continuous natural environment monitoring needed to understand an individual's gait fully during their daily activities. To address this gap, we present a Lidar-based method capable of unobtrusively and continuously tracking human leg movements in diverse home-like environments, aiming to match the accuracy of a clinical reference measurement system. We developed a calibration-free step extraction algorithm based on mathematical morphology to realize Lidar-based gait analysis. Clinical gait parameters of 45 healthy individuals were measured using Lidar and reference systems (a pressure-sensitive walkway and a video recording system). Each participant participated in three predefined ambulation experiments by walking over the walkway. We observed linear relationships with strong positive correlations (R2>0.9) between the values of the gait parameters (step and stride length, step and stride time, cadence, and velocity) measured with the Lidar sensors and the pressure-sensitive walkway reference system. Moreover, the lower and upper 95% confidence intervals of all gait parameters were tight. The proposed algorithm can accurately derive gait parameters from Lidar data captured in home-like environments, with a performance not significantly less accurate than clinical reference systems.
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Affiliation(s)
- Michael Single
- Gerontechnology and Rehabilitation Group, ARTORG Center for Biomedical Engineering Research, University of Bern, 3012 Bern, Switzerland; (M.S.); (L.C.B.); (A.C.); (K.M.); (S.M.G.); (R.M.M.); (U.P.M.)
| | - Lena C. Bruhin
- Gerontechnology and Rehabilitation Group, ARTORG Center for Biomedical Engineering Research, University of Bern, 3012 Bern, Switzerland; (M.S.); (L.C.B.); (A.C.); (K.M.); (S.M.G.); (R.M.M.); (U.P.M.)
| | - Aaron Colombo
- Gerontechnology and Rehabilitation Group, ARTORG Center for Biomedical Engineering Research, University of Bern, 3012 Bern, Switzerland; (M.S.); (L.C.B.); (A.C.); (K.M.); (S.M.G.); (R.M.M.); (U.P.M.)
| | - Kevin Möri
- Gerontechnology and Rehabilitation Group, ARTORG Center for Biomedical Engineering Research, University of Bern, 3012 Bern, Switzerland; (M.S.); (L.C.B.); (A.C.); (K.M.); (S.M.G.); (R.M.M.); (U.P.M.)
| | - Stephan M. Gerber
- Gerontechnology and Rehabilitation Group, ARTORG Center for Biomedical Engineering Research, University of Bern, 3012 Bern, Switzerland; (M.S.); (L.C.B.); (A.C.); (K.M.); (S.M.G.); (R.M.M.); (U.P.M.)
| | - Jacob Lahr
- University Hospital of Old Age Psychiatry and Psychotherapy, University of Bern, 3012 Bern, Switzerland; (J.L.); (S.K.)
| | - Paul Krack
- Department of Neurology, Inselspital, University Hospital Bern, University of Bern, 3012 Bern, Switzerland
| | - Stefan Klöppel
- University Hospital of Old Age Psychiatry and Psychotherapy, University of Bern, 3012 Bern, Switzerland; (J.L.); (S.K.)
| | - René M. Müri
- Gerontechnology and Rehabilitation Group, ARTORG Center for Biomedical Engineering Research, University of Bern, 3012 Bern, Switzerland; (M.S.); (L.C.B.); (A.C.); (K.M.); (S.M.G.); (R.M.M.); (U.P.M.)
| | - Urs P. Mosimann
- Gerontechnology and Rehabilitation Group, ARTORG Center for Biomedical Engineering Research, University of Bern, 3012 Bern, Switzerland; (M.S.); (L.C.B.); (A.C.); (K.M.); (S.M.G.); (R.M.M.); (U.P.M.)
| | - Tobias Nef
- Gerontechnology and Rehabilitation Group, ARTORG Center for Biomedical Engineering Research, University of Bern, 3012 Bern, Switzerland; (M.S.); (L.C.B.); (A.C.); (K.M.); (S.M.G.); (R.M.M.); (U.P.M.)
- Department of Neurology, Inselspital, University Hospital Bern, University of Bern, 3012 Bern, Switzerland
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Ho MY, Kuo MC, Chen CS, Wu RM, Chuang CC, Shih CS, Tseng YJ. Pathological Gait Analysis With an Open-Source Cloud-Enabled Platform Empowered by Semi-Supervised Learning-PathoOpenGait. IEEE J Biomed Health Inform 2024; 28:1066-1077. [PMID: 38064333 DOI: 10.1109/jbhi.2023.3340716] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2024]
Abstract
We present PathoOpenGait, a cloud-based platform for comprehensive gait analysis. Gait assessment is crucial in neurodegenerative diseases such as Parkinson's and multiple system atrophy, yet current techniques are neither affordable nor efficient. PathoOpenGait utilizes 2D and 3D data from a binocular 3D camera for monitoring and analyzing gait parameters. Our algorithms, including a semi-supervised learning-boosted neural network model for turn time estimation and deterministic algorithms to estimate gait parameters, were rigorously validated on annotated gait records, demonstrating high precision and consistency. We further demonstrate PathoOpenGait's applicability in clinical settings by analyzing gait trials from Parkinson's patients and healthy controls. PathoOpenGait is the first open-source, cloud-based system for gait analysis, providing a user-friendly tool for continuous patient care and monitoring. It offers a cost-effective and accessible solution for both clinicians and patients, revolutionizing the field of gait assessment. PathoOpenGait is available at https://pathoopengait.cmdm.tw.
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Zhao Z, Tang L, Chen J, Bai X, Chen Y, Ng L, Zhou Y, Deng Y. The effect of harvesting the anterior half of the peroneus longus tendon on foot morphology and gait. J Orthop Surg Res 2024; 19:69. [PMID: 38225652 PMCID: PMC10790475 DOI: 10.1186/s13018-023-04429-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Accepted: 11/29/2023] [Indexed: 01/17/2024] Open
Abstract
BACKGROUND AND OBJECTIVES In anterior cruciate ligament reconstruction, the strength of the graft was found to be unsatisfactory usually the anterior half of the peroneus longus tendon was taken for supplementation, but the effect on foot and ankle function and gait in the donor area is unclear. This study aims to explore the changes in the ankle and gait after using the harvested anterior half of the peroneus longus tendon as a reconstruction graft for the anterior cruciate ligament. METHODS A total of 20 patients, 6 males and 14 females, aged 18 to 44 years, with unilateral anterior cruciate ligament injuries, underwent reconstruction using the harvested anterior half of the peroneus longus tendon as a graft between June 2021 and December 2021. The part on which the anterior half of the peroneus longus tendon was harvested was considered the experimental group, while the contralateral foot was the control group. At the 6-month follow-up, the Lysholm knee score, AOFAS ankle score, and gait-related data (foot length, arch index, arch volume, arch volume index, and gait cycle parameters: percentage of time in each gait phase, step frequency, step length, foot strike angle, and push-off angle) were assessed using a 3D foot scanner and wearable sensors for both groups. RESULTS All 20 patients completed the six-month follow-up. There were no statistically significant differences between the experimental and control groups regarding knee scores, ankle scores, foot length, arch index, arch volume, arch volume index, step frequency, and step length (P > 0.05). However, there were statistically significant differences between the experimental and control groups in terms of the gait cycle parameters, including the percentage of time in the stance, mid-stance, and push-off phases, as well as foot strike angle and push-off angle (P < 0.05). CONCLUSION Through our study of the surgical experimental group we have shown that harvesting the anterior half of the peroneus longus tendon does not affect foot morphology and gait parameters; however, it does impact the gait cycle.
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Affiliation(s)
- Zhi Zhao
- Department of Sport Medicine, Chongqing Orthopedic Hospital of Traditional Chinese Medicine, Chongqing, 400012, China
| | - Li Tang
- Chongqing Rongzhi Biotechnology Company Limited, Chongqing, 400012, China
| | - Jing Chen
- Chongqing Rongzhi Biotechnology Company Limited, Chongqing, 400012, China
| | - Xinwen Bai
- Department of Sport Medicine, Chongqing Orthopedic Hospital of Traditional Chinese Medicine, Chongqing, 400012, China
| | - Yu Chen
- Department of Sport Medicine, Chongqing Orthopedic Hospital of Traditional Chinese Medicine, Chongqing, 400012, China
| | - Liqi Ng
- Institute of Orthopaedic and Musculoskeletal Science, University College London, Royal National Orthopaedic Hospital, Stanmore, London, HA7 4LP, UK
| | - Yu Zhou
- Postdoctoral Research Workstation, Chongqing Orthopedic Hospital of Traditional Chinese Medicine, Chongqing, 400012, China.
| | - Yu Deng
- Department of Sport Medicine, Chongqing Orthopedic Hospital of Traditional Chinese Medicine, Chongqing, 400012, China.
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He Y, Chen Y, Tang L, Chen J, Tang J, Yang X, Su S, Zhao C, Xiao N. Accuracy validation of a wearable IMU-based gait analysis in healthy female. BMC Sports Sci Med Rehabil 2024; 16:2. [PMID: 38167148 PMCID: PMC10762813 DOI: 10.1186/s13102-023-00792-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 12/13/2023] [Indexed: 01/05/2024]
Abstract
OBJECTIVE The aim of this study was to assess the accuracy and test-retest reliability of a wearable inertial measurement unit (IMU) system for gait analysis in healthy female compared to a gold-standard optoelectronic motion capture (OMC) system. METHODS In our study, we collected data from 5 healthy young females. Participants were attached with markers from both the OMC system and the IMU system simultaneously. Data was collected when participants walked on a 7 m walking path. Each participant performed 50 repetitions of walking on the path. To ensure the collection of complete gait cycle data, a gait cycle was considered valid only if the participant passed through the center of the walking path at the same time that the OMC system detected a valid marker signal. As a result, 5 gait cycles that met the standards of the OMC system were included in the final analysis. The stride length, cadence, velocity, stance phase and swing phase of the spatio-temporal parameters were included in the analysis. A generalized linear mixture model was used to assess the repeatability of the two systems. The Wilcoxon rank-sum test for continuous variables was used to compare the mean differences between the two systems. For evaluating the reliability of the IMU system, we calculated the Intra-class Correlation Coefficient (ICC). Additionally, Bland-Altman plots were used to compare the levels of agreement between the two systems. RESULTS The measurements of Spatio-temporal parameters, including the stance phase (P = 0.78, 0.13, L-R), swing phase (P = 0.78, 0.13, L-R), velocity (P = 0.14, 0.13, L-R), cadence (P = 0.53, 0.22, L-R), stride length (P = 0.05, 0.19, L-R), by the IMU system and OMC system were similar. Which suggested that IMU and OMC systems could be used interchangeably for gait measurements. The intra-rater reliability showed an excellent correlation for the stance phase, swing phase, velocity and cadence (Intraclass Correlation Coefficient, ICC > 0.9) for both systems. However, the correlation of stride length was poor (ICC = 0.36, P = 0.34, L) to medium (ICC = 0.56, P = 0.22, R). Additionally, the measurements of IMU systems were repeatable. CONCLUSIONS The results of IMU system and OMC system shown good repeatability. Wearable IMU system could analyze gait data accurately. In particular, the measurement of stance phase, swing phase, velocity and cadence showed excellent reliability. IMU system provided an alternative measurement to OMC for gait analysis. However, the measurement of stride length by IMU needs further consideration.
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Affiliation(s)
- Yi He
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Chongqing Medical University, No.1 Youyi Road, Yuzhong District, Chongqing, 400016, China
| | - Yuxia Chen
- Department of Rehabilitation, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, No. 136 Zhongshan 2nd Road, Yuzhong District, Chongqing, 400016, China
| | - Li Tang
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Chongqing Medical University, No.1 Youyi Road, Yuzhong District, Chongqing, 400016, China
| | - Jing Chen
- Shanqi (Chongqing) Smart Medical Technology Co., Ltd., Chongqing, China
| | - Jing Tang
- Shanqi (Chongqing) Smart Medical Technology Co., Ltd., Chongqing, China
| | - Xiaoxuan Yang
- Shanqi (Chongqing) Smart Medical Technology Co., Ltd., Chongqing, China
| | - Songchuan Su
- Chongqing Orthopedics Hospital of Traditional Chinese Medicine, Chongqing, China
| | - Chen Zhao
- Department of Orthopedic Surgery, The First Affiliated Hospital of Chongqing Medical University, No.1 Youyi Road, Yuzhong District, Chongqing, 400016, China.
| | - Nong Xiao
- Department of Rehabilitation, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, No. 136 Zhongshan 2nd Road, Yuzhong District, Chongqing, 400016, China.
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Yoon DH, Kim JH, Lee K, Cho JS, Jang SH, Lee SU. Inertial measurement unit sensor-based gait analysis in adults and older adults: A cross-sectional study. Gait Posture 2024; 107:212-217. [PMID: 37863672 DOI: 10.1016/j.gaitpost.2023.10.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/01/2023] [Revised: 09/18/2023] [Accepted: 10/04/2023] [Indexed: 10/22/2023]
Abstract
BACKGROUND Gait assessment has been used in a wide range of clinical applications, and gait velocity is also a leading predictor of disease and physical functional aspects in older adults. RESEARCH QUESTION The study aim to examine the changes in IMU-based gait parameters according to age in healthy adults aged 50 and older, to analyze differences between aging patients. METHODS A total of 296 healthy adults (65.32 ± 6.74 yrs; 83.10 % female) were recruited. Gait assessment was performed using an IMU sensor-based gait analysis system, and 3D motion information of hip and knee joints was obtained using magnetic sensors. The basic characteristics of the study sample were stratified by age category, and the baseline characteristics between the groups were compared using analysis of variance (ANOVA). Pearson's correlation analysis was used to analyze the relationship between age as the dependent variable and several measures of gait parameters and joint angles as independent variables. RESULTS The results of this study found that there were significant differences in gait velocity and both terminal double support in the three groups according to age, and statistically significant differences in the three groups in hip joint angle and knee joints angle. In addition, it was found that the gait velocity and knee/hip joint angle changed with age, and the gait velocity and knee/hip joint angle were also different in the elderly and adult groups. CONCLUSIONS We found changes in gait parameters and joint angles according to age in healthy adults and older adults and confirmed the difference in gait velocity and joint angles between adults and older adults.
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Affiliation(s)
- Dong Hyun Yoon
- Department of Rehabilitation Medicine, Seoul National University College of Medicine, Seoul, South Korea; Institute on Aging, Seoul National University, Seoul, South Korea
| | - Jeong-Hyun Kim
- Department of Rehabilitation Medicine, Seoul Metropolitan Government Boramae Medical Center, Seoul, South Korea
| | - Kyuwon Lee
- Department of Rehabilitation Medicine, Seoul Metropolitan Government Boramae Medical Center, Seoul, South Korea
| | - Jae-Sung Cho
- Korea Orthopedics & Rehabilitation Engineering Center, Incheon, South Korea
| | - Seong-Ho Jang
- Department of Rehabilitation Medicine, Hanyang University College of Medicine, Gyeonggi-do, South Korea
| | - Shi-Uk Lee
- Department of Rehabilitation Medicine, Seoul National University College of Medicine, Seoul, South Korea; Department of Rehabilitation Medicine, Seoul Metropolitan Government Boramae Medical Center, Seoul, South Korea.
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Castro F, Impedovo D, Pirlo G. A Hybrid Protection Scheme for the Gait Analysis in Early Dementia Recognition. SENSORS (BASEL, SWITZERLAND) 2023; 24:24. [PMID: 38202886 PMCID: PMC10780691 DOI: 10.3390/s24010024] [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: 11/08/2023] [Revised: 12/07/2023] [Accepted: 12/15/2023] [Indexed: 01/12/2024]
Abstract
Human activity recognition (HAR) through gait analysis is a very promising research area for early detection of neurodegenerative diseases because gait abnormalities are typical symptoms of some neurodegenerative diseases, such as early dementia. While working with such biometric data, the performance parameters must be considered along with privacy and security issues. In other words, such biometric data should be processed under specific security and privacy requirements. This work proposes an innovative hybrid protection scheme combining a partially homomorphic encryption scheme and a cancelable biometric technique based on random projection to protect gait features, ensuring patient privacy according to ISO/IEC 24745. The proposed hybrid protection scheme has been implemented along a long short-term memory (LSTM) neural network to realize a secure early dementia diagnosis system. The proposed protection scheme is scalable and implementable with any type of neural network because it is independent of the network's architecture. The conducted experiments demonstrate that the proposed protection scheme enables a high trade-off between safety and performance. The accuracy degradation is at most 1.20% compared with the early dementia recognition system without the protection scheme. Moreover, security and computational analyses of the proposed scheme have been conducted and reported.
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Affiliation(s)
- Francesco Castro
- Department of Computer Science, University of Bari Aldo Moro, 70125 Bari, Italy; (D.I.); (G.P.)
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Li J, Liang W, Yin X, Li J, Guan W. Multimodal Gait Abnormality Recognition Using a Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) Network Based on Multi-Sensor Data Fusion. SENSORS (BASEL, SWITZERLAND) 2023; 23:9101. [PMID: 38005489 PMCID: PMC10675737 DOI: 10.3390/s23229101] [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: 09/21/2023] [Revised: 10/31/2023] [Accepted: 11/07/2023] [Indexed: 11/26/2023]
Abstract
Global aging leads to a surge in neurological diseases. Quantitative gait analysis for the early detection of neurological diseases can effectively reduce the impact of the diseases. Recently, extensive research has focused on gait-abnormality-recognition algorithms using a single type of portable sensor. However, these studies are limited by the sensor's type and the task specificity, constraining the widespread application of quantitative gait recognition. In this study, we propose a multimodal gait-abnormality-recognition framework based on a Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) network. The as-established framework effectively addresses the challenges arising from smooth data interference and lengthy time series by employing an adaptive sliding window technique. Then, we convert the time series into time-frequency plots to capture the characteristic variations in different abnormality gaits and achieve a unified representation of the multiple data types. This makes our signal processing method adaptable to several types of sensors. Additionally, we use a pre-trained Deep Convolutional Neural Network (DCNN) for feature extraction, and the consequently established CNN-BiLSTM network can achieve high-accuracy recognition by fusing and classifying the multi-sensor input data. To validate the proposed method, we conducted diversified experiments to recognize the gait abnormalities caused by different neuropathic diseases, such as amyotrophic lateral sclerosis (ALS), Parkinson's disease (PD), and Huntington's disease (HD). In the PDgait dataset, the framework achieved an accuracy of 98.89% in the classification of Parkinson's disease severity, surpassing DCLSTM's 96.71%. Moreover, the recognition accuracy of ALS, PD, and HD on the PDgait dataset was 100%, 96.97%, and 95.43% respectively, surpassing the majority of previously reported methods. These experimental results strongly demonstrate the potential of the proposed multimodal framework for gait abnormality identification. Due to the advantages of the framework, such as its suitability for different types of sensors and fewer training parameters, it is more suitable for gait monitoring in daily life and the customization of medical rehabilitation schedules, which will help more patients alleviate the harm caused by their diseases.
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Affiliation(s)
- Jing Li
- School of Mechanical Engineering and Hubei Modern Manufacturing Quality Engineering Key Laboratory, Hubei University of Technology, Wuhan 430068, China
- School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Weisheng Liang
- School of Mechanical Engineering and Hubei Modern Manufacturing Quality Engineering Key Laboratory, Hubei University of Technology, Wuhan 430068, China
| | - Xiyan Yin
- School of Mechanical Engineering and Hubei Modern Manufacturing Quality Engineering Key Laboratory, Hubei University of Technology, Wuhan 430068, China
| | - Jun Li
- Detroit Green Technology Institute, Hubei University of Technology, Wuhan 430068, China; (J.L.); (W.G.)
| | - Weizheng Guan
- Detroit Green Technology Institute, Hubei University of Technology, Wuhan 430068, China; (J.L.); (W.G.)
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Ahn JC, Coyle SM. Comparative profiling of cellular gait on adhesive micropatterns defines statistical patterns of activity that underlie native and cancerous cell dynamics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.27.564389. [PMID: 37961146 PMCID: PMC10634873 DOI: 10.1101/2023.10.27.564389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Cell dynamics are powered by patterns of activity, but it is not straightforward to quantify these patterns or compare them across different environmental conditions or cell-types. Here we digitize the long-term shape fluctuations of metazoan cells grown on micropatterned fibronectin islands to define and extract statistical features of cell dynamics without the need for genetic modification or fluorescence imaging. These shape fluctuations generate single-cell morphological signals that can be decomposed into two major components: a continuous, slow-timescale meandering of morphology about an average steady-state shape; and short-lived "events" of rapid morphology change that sporadically occur throughout the timecourse. By developing statistical metrics for each of these components, we used thousands of hours of single-cell data to quantitatively define how each axis of cell dynamics was impacted by environmental conditions or cell-type. We found the size and spatial complexity of the micropattern island modulated the statistics of morphological events-lifetime, frequency, and orientation-but not its baseline shape fluctuations. Extending this approach to profile a panel of triple negative breast cancer cell-lines, we found that different cell-types could be distinguished from one another along specific and unique statistical axes of their behavior. Our results suggest that micropatterned substrates provide a generalizable method to build statistical profiles of cell dynamics to classify and compare emergent cell behaviors.
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Affiliation(s)
- John C. Ahn
- Department of Biochemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, USA
- Integrated Program in Biochemistry Graduate Program, University of Wisconsin-Madison, Madison, Wisconsin 53706, USA
| | - Scott M. Coyle
- Department of Biochemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, USA
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Aout T, Begon M, Jegou B, Peyrot N, Caderby T. Effects of Functional Electrical Stimulation on Gait Characteristics in Healthy Individuals: A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:8684. [PMID: 37960383 PMCID: PMC10648660 DOI: 10.3390/s23218684] [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: 08/26/2023] [Revised: 10/09/2023] [Accepted: 10/17/2023] [Indexed: 11/15/2023]
Abstract
BACKGROUND This systematic review aimed to provide a comprehensive overview of the effects of functional electrical stimulation (FES) on gait characteristics in healthy individuals. METHODS Six electronic databases (PubMed, Embase, Epistemonikos, PEDro, COCHRANE Library, and Scopus) were searched for studies evaluating the effects of FES on spatiotemporal, kinematic, and kinetic gait parameters in healthy individuals. Two examiners evaluated the eligibility and quality of the included studies using the PEDro scale. RESULTS A total of 15 studies met the inclusion criteria. The findings from the literature reveal that FES can be used to modify lower-limb joint kinematics, i.e., to increase or reduce the range of motion of the hip, knee, and ankle joints. In addition, FES can be used to alter kinetics parameters, including ground reaction forces, center of pressure trajectory, or knee joint reaction force. As a consequence of these kinetics and kinematics changes, FES can lead to changes in spatiotemporal gait parameters, such as gait speed, step cadence, and stance duration. CONCLUSIONS The findings of this review improve our understanding of the effects of FES on gait biomechanics in healthy individuals and highlight the potential of this technology as a training or assistive solution for improving gait performance in this population.
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Affiliation(s)
- Thomas Aout
- Laboratoire IRISSE, EA4075, UFR des Sciences de l’Homme et de l’Environnement, Université de La Réunion, 97430 Le Tampon, France; (B.J.); (N.P.); (T.C.)
| | - Mickael Begon
- Laboratoire de Simulation et Modélisation du Mouvement, École de Kinésiologie et des Sciences de l’Activité Physique, Université de Montréal, Montreal, QC H3T 1J4, Canada;
- Centre de Recherche du CHU Sainte-Justine, Université de Montréal, Montreal, QC H3C 3J7, Canada
| | - Baptiste Jegou
- Laboratoire IRISSE, EA4075, UFR des Sciences de l’Homme et de l’Environnement, Université de La Réunion, 97430 Le Tampon, France; (B.J.); (N.P.); (T.C.)
| | - Nicolas Peyrot
- Laboratoire IRISSE, EA4075, UFR des Sciences de l’Homme et de l’Environnement, Université de La Réunion, 97430 Le Tampon, France; (B.J.); (N.P.); (T.C.)
- Mouvement-Interactions-Performance (MIP), Le Mans Université, EA 4334, 72000 Le Mans, France
| | - Teddy Caderby
- Laboratoire IRISSE, EA4075, UFR des Sciences de l’Homme et de l’Environnement, Université de La Réunion, 97430 Le Tampon, France; (B.J.); (N.P.); (T.C.)
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Du S, Ma X, Wang J, Mi Y, Zhang J, Du C, Li X, Tan H, Liang C, Yang T, Shi W, Zhang G, Tian Y. Spatiotemporal gait parameter fluctuations in older adults affected by mild cognitive impairment: comparisons among three cognitive dual-task tests. BMC Geriatr 2023; 23:603. [PMID: 37759185 PMCID: PMC10523758 DOI: 10.1186/s12877-023-04281-7] [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: 01/18/2023] [Accepted: 09/05/2023] [Indexed: 09/29/2023] Open
Abstract
BACKGROUNDS Gait disorder is associated with cognitive functional impairment, and this disturbance is more pronouncedly when performing additional cognitive tasks. Our study aimed to characterize gait disorders in mild cognitive impairment (MCI) under three dual tasks and determine the association between gait performance and cognitive function. METHODS A total of 260 participants were enrolled in this cross-sectional study and divided into MCI and cognitively normal control. Spatiotemporal and kinematic gait parameters (31 items) in single task and three dual tasks (serial 100-7, naming animals and words recall) were measured using a wearable sensor. Baseline characteristics of the two groups were balanced using propensity score matching. Important gait features were filtered using random forest method and LASSO regression and further described using logistic analysis. RESULTS After matching, 106 participants with MCI and 106 normal controls were recruited. Top 5 gait features in random forest and 4 ~ 6 important features in LASSO regression were selected. Robust variables associating with cognitive function were temporal gait parameters. Participants with MCI exhibited decreased swing time and terminal swing, increased mid stance and variability of stride length compared with normal control. Subjects walked slower when performing an extra dual cognitive task. In the three dual tasks, words recall test exhibited more pronounced impact on gait regularity, velocity, and dual task cost than the other two cognitive tests. CONCLUSION Gait assessment under dual task conditions, particularly in words recall test, using portable sensors could be useful as a complementary strategy for early detection of MCI.
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Affiliation(s)
- Shan Du
- Department of Neurology, the Affiliated Hospital of Northwest University, Xi'an No.3 Hospital, Shaanxi, Xi'an, 710018, China
- Xi'an Key Laboratory of Cardiovascular and Cerebrovascular Diseases, the Affiliated Hospital of Northwest University, Xi'an No.3 Hospital, Shaanxi, Xi'an, 710018, China
| | - Xiaojuan Ma
- Clinical Medical Research Center, the Affiliated Hospital of Northwest University, Xi'an No.3 Hospital, Shaanxi, Xi'an, 710018, China
- Xi'an Key Laboratory of Cardiovascular and Cerebrovascular Diseases, the Affiliated Hospital of Northwest University, Xi'an No.3 Hospital, Shaanxi, Xi'an, 710018, China
| | - Jiachen Wang
- Department of Neurology, the Affiliated Hospital of Northwest University, Xi'an No.3 Hospital, Shaanxi, Xi'an, 710018, China
- Xi'an Key Laboratory of Cardiovascular and Cerebrovascular Diseases, the Affiliated Hospital of Northwest University, Xi'an No.3 Hospital, Shaanxi, Xi'an, 710018, China
| | - Yan Mi
- Department of Neurology, the Affiliated Hospital of Northwest University, Xi'an No.3 Hospital, Shaanxi, Xi'an, 710018, China
- Xi'an Key Laboratory of Cardiovascular and Cerebrovascular Diseases, the Affiliated Hospital of Northwest University, Xi'an No.3 Hospital, Shaanxi, Xi'an, 710018, China
| | - Jie Zhang
- Department of Neurology, the Affiliated Hospital of Northwest University, Xi'an No.3 Hospital, Shaanxi, Xi'an, 710018, China
- Xi'an Key Laboratory of Cardiovascular and Cerebrovascular Diseases, the Affiliated Hospital of Northwest University, Xi'an No.3 Hospital, Shaanxi, Xi'an, 710018, China
| | - Chengxue Du
- Department of Neurology, the Affiliated Hospital of Northwest University, Xi'an No.3 Hospital, Shaanxi, Xi'an, 710018, China
- Xi'an Key Laboratory of Cardiovascular and Cerebrovascular Diseases, the Affiliated Hospital of Northwest University, Xi'an No.3 Hospital, Shaanxi, Xi'an, 710018, China
| | - Xiaobo Li
- Department of Neurology, the Affiliated Hospital of Northwest University, Xi'an No.3 Hospital, Shaanxi, Xi'an, 710018, China
- Xi'an Key Laboratory of Cardiovascular and Cerebrovascular Diseases, the Affiliated Hospital of Northwest University, Xi'an No.3 Hospital, Shaanxi, Xi'an, 710018, China
| | - Huihui Tan
- Department of Neurology, the Affiliated Hospital of Northwest University, Xi'an No.3 Hospital, Shaanxi, Xi'an, 710018, China
- Xi'an Key Laboratory of Cardiovascular and Cerebrovascular Diseases, the Affiliated Hospital of Northwest University, Xi'an No.3 Hospital, Shaanxi, Xi'an, 710018, China
| | - Chen Liang
- Clinical Medical Research Center, the Affiliated Hospital of Northwest University, Xi'an No.3 Hospital, Shaanxi, Xi'an, 710018, China
- Xi'an Key Laboratory of Cardiovascular and Cerebrovascular Diseases, the Affiliated Hospital of Northwest University, Xi'an No.3 Hospital, Shaanxi, Xi'an, 710018, China
| | - Tian Yang
- Clinical Medical Research Center, the Affiliated Hospital of Northwest University, Xi'an No.3 Hospital, Shaanxi, Xi'an, 710018, China
- Xi'an Key Laboratory of Cardiovascular and Cerebrovascular Diseases, the Affiliated Hospital of Northwest University, Xi'an No.3 Hospital, Shaanxi, Xi'an, 710018, China
| | - Wenzhen Shi
- Clinical Medical Research Center, the Affiliated Hospital of Northwest University, Xi'an No.3 Hospital, Shaanxi, Xi'an, 710018, China.
- Xi'an Key Laboratory of Cardiovascular and Cerebrovascular Diseases, the Affiliated Hospital of Northwest University, Xi'an No.3 Hospital, Shaanxi, Xi'an, 710018, China.
| | - Gejuan Zhang
- Department of Neurology, the Affiliated Hospital of Northwest University, Xi'an No.3 Hospital, Shaanxi, Xi'an, 710018, China.
- Xi'an Key Laboratory of Cardiovascular and Cerebrovascular Diseases, the Affiliated Hospital of Northwest University, Xi'an No.3 Hospital, Shaanxi, Xi'an, 710018, China.
| | - Ye Tian
- Department of Neurology, the Affiliated Hospital of Northwest University, Xi'an No.3 Hospital, Shaanxi, Xi'an, 710018, China.
- Xi'an Key Laboratory of Cardiovascular and Cerebrovascular Diseases, the Affiliated Hospital of Northwest University, Xi'an No.3 Hospital, Shaanxi, Xi'an, 710018, China.
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Bonanno M, De Nunzio AM, Quartarone A, Militi A, Petralito F, Calabrò RS. Gait Analysis in Neurorehabilitation: From Research to Clinical Practice. Bioengineering (Basel) 2023; 10:785. [PMID: 37508812 PMCID: PMC10376523 DOI: 10.3390/bioengineering10070785] [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: 05/02/2023] [Revised: 06/16/2023] [Accepted: 06/28/2023] [Indexed: 07/30/2023] Open
Abstract
When brain damage occurs, gait and balance are often impaired. Evaluation of the gait cycle, therefore, has a pivotal role during the rehabilitation path of subjects who suffer from neurological disorders. Gait analysis can be performed through laboratory systems, non-wearable sensors (NWS), and/or wearable sensors (WS). Using these tools, physiotherapists and neurologists have more objective measures of motion function and can plan tailored and specific gait and balance training early to achieve better outcomes and improve patients' quality of life. However, most of these innovative tools are used for research purposes (especially the laboratory systems and NWS), although they deserve more attention in the rehabilitation field, considering their potential in improving clinical practice. In this narrative review, we aimed to summarize the most used gait analysis systems in neurological patients, shedding some light on their clinical value and implications for neurorehabilitation practice.
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Affiliation(s)
- Mirjam Bonanno
- IRCCS Centro Neurolesi "Bonino-Pulejo", Via Palermo, SS 113, C. da Casazza, 98123 Messina, Italy
| | - Alessandro Marco De Nunzio
- Department of Research and Development, LUNEX International University of Health, Exercise and Sports, Avenue du Parc des Sports, 50, 4671 Differdange, Luxembourg
| | - Angelo Quartarone
- IRCCS Centro Neurolesi "Bonino-Pulejo", Via Palermo, SS 113, C. da Casazza, 98123 Messina, Italy
| | - Annalisa Militi
- IRCCS Centro Neurolesi "Bonino-Pulejo", Via Palermo, SS 113, C. da Casazza, 98123 Messina, Italy
| | - Francesco Petralito
- IRCCS Centro Neurolesi "Bonino-Pulejo", Via Palermo, SS 113, C. da Casazza, 98123 Messina, Italy
| | - Rocco Salvatore Calabrò
- IRCCS Centro Neurolesi "Bonino-Pulejo", Via Palermo, SS 113, C. da Casazza, 98123 Messina, Italy
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22
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Raab D, Kecskeméthy A. [Clinical value of instrumental gait analysis]. ORTHOPADIE (HEIDELBERG, GERMANY) 2023:10.1007/s00132-023-04397-z. [PMID: 37286624 DOI: 10.1007/s00132-023-04397-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 05/05/2023] [Indexed: 06/09/2023]
Abstract
BACKGROUND Instrumental gait analysis is becoming an established addition to conventional diagnostic methods for the clinical assessment of complex movement disorders. It can provide objective and high resolution motion data and contains information that is not observable with conventional clinical methods, such as muscle activation during gait. UTILISATION Instrumental gait analysis can add observer independent parameters to the treatment planning of individuals as well as provide insights into pathomechanisms with clinical research studies. Limiting factors for the use of gait analysis technology are currently the time and personnel expenditures for measurements and data processing, as well as the extensive amount of training time required for data interpretation. This article illustrates the clinical value of instrumental gait analysis and specifies its synergies with conventional diagnostic methods.
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Affiliation(s)
- Dominik Raab
- Lehrstuhl für Mechanik und Robotik, Universität Duisburg-Essen, Lotharstr. 1, 47057, Duisburg, Deutschland.
| | - Andrés Kecskeméthy
- Lehrstuhl für Mechanik und Robotik, Universität Duisburg-Essen, Lotharstr. 1, 47057, Duisburg, Deutschland
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23
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Abd Mutalib N, Syed Mohamad SA, Jusril NA, Hasbullah NI, Mohd Amin MCI, Ismail NH. Lactic Acid Bacteria (LAB) and Neuroprotection, What Is New? An Up-To-Date Systematic Review. Pharmaceuticals (Basel) 2023; 16:ph16050712. [PMID: 37242494 DOI: 10.3390/ph16050712] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 04/13/2023] [Accepted: 04/29/2023] [Indexed: 05/28/2023] Open
Abstract
BACKGROUND In recent years, the potential role of probiotics has become prominent in the discoveries of neurotherapy against neurodegenerative diseases, such as Alzheimer's and Parkinson's diseases. Lactic acid bacteria (LAB) exhibit neuroprotective properties and exert their effects via various mechanisms of actions. This review aimed to evaluate the effects of LAB on neuroprotection reported in the literature. METHODS A database search on Google Scholar, PubMed, and Science Direct revealed a total of 467 references, of which 25 were included in this review based on inclusion criteria which comprises 7 in vitro, 16 in vivo, and 2 clinical studies. RESULTS From the studies, LAB treatment alone or in probiotics formulations demonstrated significant neuroprotective activities. In animals and humans, LAB probiotics supplementation has improved memory and cognitive performance mainly via antioxidant and anti-inflammatory pathways. CONCLUSIONS Despite promising findings, due to limited studies available in the literature, further studies still need to be explored regarding synergistic effects, efficacy, and optimum dosage of LAB oral bacteriotherapy as treatment or prevention against neurodegenerative diseases.
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Affiliation(s)
- Nurliana Abd Mutalib
- Atta-ur-Rahman Institute for Natural Product Discovery, Universiti Teknologi MARA Cawangan Selangor, Puncak Alam 42300, Selangor, Malaysia
| | - Sharifah Aminah Syed Mohamad
- Atta-ur-Rahman Institute for Natural Product Discovery, Universiti Teknologi MARA Cawangan Selangor, Puncak Alam 42300, Selangor, Malaysia
- Faculty of Applied Sciences, Universiti Teknologi MARA, Shah Alam 40450, Selangor, Malaysia
| | - Nor Atiqah Jusril
- Faculty Bioresources and Food Industry, Universiti Sultan Zainal Abidin, Besut Campus, Besut 22200, Terengganu, Malaysia
| | - Nur Intan Hasbullah
- Atta-ur-Rahman Institute for Natural Product Discovery, Universiti Teknologi MARA Cawangan Selangor, Puncak Alam 42300, Selangor, Malaysia
- Faculty of Applied Sciences, Universiti Teknologi MARA, Cawangan Negeri Sembilan, Kampus Kuala Pilah, Kuala Pilah 72000, Negeri Sembilan, Malaysia
| | - Mohd Cairul Iqbal Mohd Amin
- Centre for Drug Delivery Technology, Faculty of Pharmacy, Universiti Kebangsaan Malaysia, Jalan Raja Muda Abdul Aziz, Kuala Lumpur 50300, Selangor, Malaysia
| | - Nor Hadiani Ismail
- Atta-ur-Rahman Institute for Natural Product Discovery, Universiti Teknologi MARA Cawangan Selangor, Puncak Alam 42300, Selangor, Malaysia
- Faculty of Applied Sciences, Universiti Teknologi MARA, Shah Alam 40450, Selangor, Malaysia
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24
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Tsakanikas V, Ntanis A, Rigas G, Androutsos C, Boucharas D, Tachos N, Skaramagkas V, Chatzaki C, Kefalopoulou Z, Tsiknakis M, Fotiadis D. Evaluating Gait Impairment in Parkinson's Disease from Instrumented Insole and IMU Sensor Data. SENSORS (BASEL, SWITZERLAND) 2023; 23:3902. [PMID: 37112243 PMCID: PMC10143543 DOI: 10.3390/s23083902] [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: 03/07/2023] [Revised: 04/06/2023] [Accepted: 04/07/2023] [Indexed: 06/19/2023]
Abstract
Parkinson's disease (PD) is characterized by a variety of motor and non-motor symptoms, some of them pertaining to gait and balance. The use of sensors for the monitoring of patients' mobility and the extraction of gait parameters, has emerged as an objective method for assessing the efficacy of their treatment and the progression of the disease. To that end, two popular solutions are pressure insoles and body-worn IMU-based devices, which have been used for precise, continuous, remote, and passive gait assessment. In this work, insole and IMU-based solutions were evaluated for assessing gait impairment, and were subsequently compared, producing evidence to support the use of instrumentation in everyday clinical practice. The evaluation was conducted using two datasets, generated during a clinical study, in which patients with PD wore, simultaneously, a pair of instrumented insoles and a set of wearable IMU-based devices. The data from the study were used to extract and compare gait features, independently, from the two aforementioned systems. Subsequently, subsets comprised of the extracted features, were used by machine learning algorithms for gait impairment assessment. The results indicated that insole gait kinematic features were highly correlated with those extracted from IMU-based devices. Moreover, both had the capacity to train accurate machine learning models for the detection of PD gait impairment.
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Affiliation(s)
- Vassilis Tsakanikas
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, GR 45110 Ioannina, Greece
| | | | - George Rigas
- PD Neurotechnology Ltd., GR 45500 Ioannina, Greece
| | - Christos Androutsos
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, GR 45110 Ioannina, Greece
| | - Dimitrios Boucharas
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, GR 45110 Ioannina, Greece
| | - Nikolaos Tachos
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, GR 45110 Ioannina, Greece
- Biomedical Research Institute, Foundation for Research and Technology—Hellas, GR 45500 Ioannina, Greece
| | - Vasileios Skaramagkas
- Institute of Computer Science, Foundation for Research and Technology—Hellas, GR 70013 Heraklion, Greece
- Department of Electrical and Computer Engineering, Hellenic Mediterranean University, GR 71004 Heraklion, Greece
| | - Chariklia Chatzaki
- Institute of Computer Science, Foundation for Research and Technology—Hellas, GR 70013 Heraklion, Greece
| | - Zinovia Kefalopoulou
- Department of Neurology, General University Hospital of Patras, GR 26504 Patras, Greece
| | - Manolis Tsiknakis
- Institute of Computer Science, Foundation for Research and Technology—Hellas, GR 70013 Heraklion, Greece
- Department of Electrical and Computer Engineering, Hellenic Mediterranean University, GR 71004 Heraklion, Greece
| | - Dimitrios Fotiadis
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, GR 45110 Ioannina, Greece
- Biomedical Research Institute, Foundation for Research and Technology—Hellas, GR 45500 Ioannina, Greece
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25
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Wu Z, Gu M. A novel attention-guided ECA-CNN architecture for sEMG-based gait classification. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:7140-7153. [PMID: 37161144 DOI: 10.3934/mbe.2023308] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Gait recognition and classification technology is one of the essential technologies for detecting neurodegenerative dysfunction. This paper presents a gait classification model based on a convolutional neural network (CNN) with an efficient channel attention (ECA) module for gait detection applications using surface electromyographic (sEMG) signals. First, the sEMG sensor was used to collect the experimental sample data, and various gaits of different persons were collected to construct the sEMG signal data sets of different gaits. The CNN is used to extract the features of the one-dimensional input sEMG signal to obtain the feature vector, which is input into the ECA module to realize cross-channel interaction. Then, the next part of the convolutional layer is input to learn the signal features further. Finally, the model is output and tested to obtain the results. Comparative experiments show that the accuracy of the ECA-CNN network model can reach 97.75%.
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Affiliation(s)
- Zhangjie Wu
- School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China
| | - Minming Gu
- School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China
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26
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Russo M, Amboni M, Barone P, Pellecchia MT, Romano M, Ricciardi C, Amato F. Identification of a Gait Pattern for Detecting Mild Cognitive Impairment in Parkinson's Disease. SENSORS (BASEL, SWITZERLAND) 2023; 23:1985. [PMID: 36850582 PMCID: PMC9963713 DOI: 10.3390/s23041985] [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: 12/09/2022] [Revised: 01/04/2023] [Accepted: 02/07/2023] [Indexed: 06/18/2023]
Abstract
The aim of this study was to determine a gait pattern, i.e., a subset of spatial and temporal parameters, through a supervised machine learning (ML) approach, which could be used to reliably distinguish Parkinson's Disease (PD) patients with and without mild cognitive impairment (MCI). Thus, 80 PD patients underwent gait analysis and spatial-temporal parameters were acquired in three different conditions (normal gait, motor dual task and cognitive dual task). Statistical analysis was performed to investigate the data and, then, five ML algorithms and the wrapper method were implemented: Decision Tree (DT), Random Forest (RF), Naïve Bayes (NB), Support Vector Machine (SVM) and K-Nearest Neighbour (KNN). First, the algorithms for classifying PD patients with MCI were trained and validated on an internal dataset (sixty patients) and, then, the performance was tested by using an external dataset (twenty patients). Specificity, sensitivity, precision, accuracy and area under the receiver operating characteristic curve were calculated. SVM and RF showed the best performance and detected MCI with an accuracy of over 80.0%. The key features emerging from this study are stance phase, mean velocity, step length and cycle length; moreover, the major number of features selected by the wrapper belonged to the cognitive dual task, thus, supporting the close relationship between gait dysfunction and MCI in PD.
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Affiliation(s)
- Michela Russo
- Department of Electrical Engineering and Information Technology, University of Naples Federico II, 80125 Naples, Italy
| | - Marianna Amboni
- Department of Medicine, Surgery and Dentistry, Scuola Medica Salernitana, University of Salerno, 84081 Baronissi, Italy
- IDC Hermitage Capodimonte, 80133 Naples, Italy
| | - Paolo Barone
- Department of Medicine, Surgery and Dentistry, Scuola Medica Salernitana, University of Salerno, 84081 Baronissi, Italy
| | - Maria Teresa Pellecchia
- Department of Medicine, Surgery and Dentistry, Scuola Medica Salernitana, University of Salerno, 84081 Baronissi, Italy
| | - Maria Romano
- Department of Electrical Engineering and Information Technology, University of Naples Federico II, 80125 Naples, Italy
| | - Carlo Ricciardi
- Department of Electrical Engineering and Information Technology, University of Naples Federico II, 80125 Naples, Italy
| | - Francesco Amato
- Department of Electrical Engineering and Information Technology, University of Naples Federico II, 80125 Naples, Italy
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27
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Zhao H, Cao J, Xie J, Liao WH, Lei Y, Cao H, Qu Q, Bowen C. Wearable sensors and features for diagnosis of neurodegenerative diseases: A systematic review. Digit Health 2023; 9:20552076231173569. [PMID: 37214662 PMCID: PMC10192816 DOI: 10.1177/20552076231173569] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 04/17/2023] [Indexed: 05/24/2023] Open
Abstract
Objective Neurodegenerative diseases affect millions of families around the world, while various wearable sensors and corresponding data analysis can be of great support for clinical diagnosis and health assessment. This systematic review aims to provide a comprehensive overview of the existing research that uses wearable sensors and features for the diagnosis of neurodegenerative diseases. Methods A systematic review was conducted of studies published between 2015 and 2022 in major scientific databases such as Web of Science, Google Scholar, PubMed, and Scopes. The obtained studies were analyzed and organized into the process of diagnosis: wearable sensors, feature extraction, and feature selection. Results The search led to 171 eligible studies included in this overview. Wearable sensors such as force sensors, inertial sensors, electromyography, electroencephalography, acoustic sensors, optical fiber sensors, and global positioning systems were employed to monitor and diagnose neurodegenerative diseases. Various features including physical features, statistical features, nonlinear features, and features from the network can be extracted from these wearable sensors, and the alteration of features toward neurodegenerative diseases was illustrated. Moreover, different kinds of feature selection methods such as filter, wrapper, and embedded methods help to find the distinctive indicator of the diseases and benefit to a better diagnosis performance. Conclusions This systematic review enables a comprehensive understanding of wearable sensors and features for the diagnosis of neurodegenerative diseases.
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Affiliation(s)
- Huan Zhao
- School of Mechanical Engineering, Xi’an Jiaotong University, Xi'an, P.R. China
| | - Junyi Cao
- School of Mechanical Engineering, Xi’an Jiaotong University, Xi'an, P.R. China
| | - Junxiao Xie
- School of Mechanical Engineering, Xi’an Jiaotong University, Xi'an, P.R. China
| | - Wei-Hsin Liao
- Department of Mechanical and Automation
Engineering, The Chinese University of Hong
Kong, Shatin, N.T., Hong Kong, China
| | - Yaguo Lei
- School of Mechanical Engineering, Xi’an Jiaotong University, Xi'an, P.R. China
| | - Hongmei Cao
- Department of Neurology, The First
Affiliated Hospital of Xi’an Jiaotong University, Xi’an, P.R. China
| | - Qiumin Qu
- Department of Neurology, The First
Affiliated Hospital of Xi’an Jiaotong University, Xi’an, P.R. China
| | - Chris Bowen
- Department of Mechanical Engineering, University of Bath, Bath, UK
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28
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Prieto-Avalos G, Sánchez-Morales LN, Alor-Hernández G, Sánchez-Cervantes JL. A Review of Commercial and Non-Commercial Wearables Devices for Monitoring Motor Impairments Caused by Neurodegenerative Diseases. BIOSENSORS 2022; 13:72. [PMID: 36671907 PMCID: PMC9856141 DOI: 10.3390/bios13010072] [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: 11/10/2022] [Revised: 12/24/2022] [Accepted: 12/28/2022] [Indexed: 06/17/2023]
Abstract
Neurodegenerative diseases (NDDs) are among the 10 causes of death worldwide. The effects of NDDs, including irreversible motor impairments, have an impact not only on patients themselves but also on their families and social environments. One strategy to mitigate the pain of NDDs is to early identify and remotely monitor related motor impairments using wearable devices. Technological progress has contributed to reducing the hardware complexity of mobile devices while simultaneously improving their efficiency in terms of data collection and processing and energy consumption. However, perhaps the greatest challenges of current mobile devices are to successfully manage the security and privacy of patient medical data and maintain reasonable costs with respect to the traditional patient consultation scheme. In this work, we conclude: (1) Falls are most monitored for Parkinson's disease, while tremors predominate in epilepsy and Alzheimer's disease. These findings will provide guidance for wearable device manufacturers to strengthen areas of opportunity that need to be addressed, and (2) Of the total universe of commercial wearables devices that are available on the market, only a few have FDA approval, which means that there is a large number of devices that do not safeguard the integrity of the users who use them.
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Affiliation(s)
- Guillermo Prieto-Avalos
- Tecnológico Nacional de México/I.T. Orizaba, Av. Oriente 9 No. 852 Col. Emiliano Zapata, Orizaba 94320, Veracruz, Mexico
| | - Laura Nely Sánchez-Morales
- CONACYT-Tecnológico Nacional de México/I.T. Orizaba, Av. Oriente 9 No. 852 Col. Emiliano Zapata, Orizaba 94320, Veracruz, Mexico
| | - Giner Alor-Hernández
- Tecnológico Nacional de México/I.T. Orizaba, Av. Oriente 9 No. 852 Col. Emiliano Zapata, Orizaba 94320, Veracruz, Mexico
| | - José Luis Sánchez-Cervantes
- CONACYT-Tecnológico Nacional de México/I.T. Orizaba, Av. Oriente 9 No. 852 Col. Emiliano Zapata, Orizaba 94320, Veracruz, Mexico
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29
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Chen B, Chen C, Hu J, Sayeed Z, Qi J, Darwiche HF, Little BE, Lou S, Darwish M, Foote C, Palacio-Lascano C. Computer Vision and Machine Learning-Based Gait Pattern Recognition for Flat Fall Prediction. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22207960. [PMID: 36298311 PMCID: PMC9612353 DOI: 10.3390/s22207960] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 10/12/2022] [Accepted: 10/14/2022] [Indexed: 05/27/2023]
Abstract
BACKGROUND Gait recognition has been applied in the prediction of the probability of elderly flat ground fall, functional evaluation during rehabilitation, and the training of patients with lower extremity motor dysfunction. Gait distinguishing between seemingly similar kinematic patterns associated with different pathological entities is a challenge for the clinician. How to realize automatic identification and judgment of abnormal gait is a significant challenge in clinical practice. The long-term goal of our study is to develop a gait recognition computer vision system using artificial intelligence (AI) and machine learning (ML) computing. This study aims to find an optimal ML algorithm using computer vision techniques and measure variables from lower limbs to classify gait patterns in healthy people. The purpose of this study is to determine the feasibility of computer vision and machine learning (ML) computing in discriminating different gait patterns associated with flat-ground falls. METHODS We used the Kinect® Motion system to capture the spatiotemporal gait data from seven healthy subjects in three walking trials, including normal gait, pelvic-obliquity-gait, and knee-hyperextension-gait walking. Four different classification methods including convolutional neural network (CNN), support vector machine (SVM), K-nearest neighbors (KNN), and long short-term memory (LSTM) neural networks were used to automatically classify three gait patterns. Overall, 750 sets of data were collected, and the dataset was divided into 80% for algorithm training and 20% for evaluation. RESULTS The SVM and KNN had a higher accuracy than CNN and LSTM. The SVM (94.9 ± 3.36%) had the highest accuracy in the classification of gait patterns, followed by KNN (94.0 ± 4.22%). The accuracy of CNN was 87.6 ± 7.50% and that of LSTM 83.6 ± 5.35%. CONCLUSIONS This study revealed that the proposed AI machine learning (ML) techniques can be used to design gait biometric systems and machine vision for gait pattern recognition. Potentially, this method can be used to remotely evaluate elderly patients and help clinicians make decisions regarding disposition, follow-up, and treatment.
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Affiliation(s)
- Biao Chen
- State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Chaoyang Chen
- Orthopaedic Surgery and Sports Medicine, Detroit Medical Center, Detroit, MI 48201, USA
| | - Jie Hu
- State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Zain Sayeed
- Orthopaedic Surgery and Sports Medicine, Detroit Medical Center, Detroit, MI 48201, USA
| | - Jin Qi
- State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Hussein F. Darwiche
- Orthopaedic Surgery and Sports Medicine, Detroit Medical Center, Detroit, MI 48201, USA
| | - Bryan E. Little
- Orthopaedic Surgery and Sports Medicine, Detroit Medical Center, Detroit, MI 48201, USA
| | - Shenna Lou
- South Texas Health System—McAllen Department of Trauma, McAllen, TX 78503, USA
| | - Muhammad Darwish
- South Texas Health System—McAllen Department of Trauma, McAllen, TX 78503, USA
| | - Christopher Foote
- South Texas Health System—McAllen Department of Trauma, McAllen, TX 78503, USA
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30
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Non-age-related gait kinematics and kinetics in the elderly. BMC Musculoskelet Disord 2022; 23:623. [PMID: 35768797 PMCID: PMC9241214 DOI: 10.1186/s12891-022-05577-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Accepted: 06/17/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The change of gait kinematics and kinetics along aging were reported to indicate age-related gait patterns. However, few studies focus on non-age-related gait analysis. This study aims to explore the non-age-related gait kinematics and kinetics by comparing gait analysis outcomes among the healthy elderly and young subjects. METHODS Gait analysis at self-paced was conducted on 12 healthy young subjects and 8 healthy elderly subjects. Kinematic and kinetic features of ankle, knee and hip joints were analyzed and compared in two groups. The degree of variation between the young and elderly in each kinematic or kinetic feature was calculated from pattern distance and percentage of significant difference. The k-means clustering and Elbow Method were applied to select and validate non-age-related features. The average waveforms with standard deviation were plotted for the comparison of the results. RESULTS A total of five kinematic and five kinetic features were analyzed on ankle, knee and hip joints in healthy young and elderly groups. The degrees of variation in ankle moment, knee angle, hip flexion angle, and hip adduction moment were 0.1074, 0.1593, 0.1407, and 0.1593, respectively. The turning point was where the k value equals two. The clustering centers were 0.1417 and 0.3691, and the two critical values closest to the cutoff were 0.1593 and 0.3037. The average waveforms of the kinematic or kinetic features mentioned above were highly overlapped with a minor standard deviation between the healthy young and elderly but showed larger variations between the healthy and abnormal. CONCLUSIONS The cluster with a minor degree of variation in kinematic and kinetic features between the young and elderly were identified as non-age-related, including ankle moment, knee angle, hip flexion angle, and hip adduction moment. Non-age-related gait kinematics and kinetics are essential indicators for gait with normal function, which is essential in the evaluation of mobility and functional ability of the elderly, and data fusion of the assistant device.
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31
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Das R, Paul S, Mourya GK, Kumar N, Hussain M. Recent Trends and Practices Toward Assessment and Rehabilitation of Neurodegenerative Disorders: Insights From Human Gait. Front Neurosci 2022; 16:859298. [PMID: 35495059 PMCID: PMC9051393 DOI: 10.3389/fnins.2022.859298] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 03/01/2022] [Indexed: 12/06/2022] Open
Abstract
The study of human movement and biomechanics forms an integral part of various clinical assessments and provides valuable information toward diagnosing neurodegenerative disorders where the motor symptoms predominate. Conventional gait and postural balance analysis techniques like force platforms, motion cameras, etc., are complex, expensive equipment requiring specialist operators, thereby posing a significant challenge toward translation to the clinics. The current manuscript presents an overview and relevant literature summarizing the umbrella of factors associated with neurodegenerative disorder management: from the pathogenesis and motor symptoms of commonly occurring disorders to current alternate practices toward its quantification and mitigation. This article reviews recent advances in technologies and methodologies for managing important neurodegenerative gait and balance disorders, emphasizing assessment and rehabilitation/assistance. The review predominantly focuses on the application of inertial sensors toward various facets of gait analysis, including event detection, spatiotemporal gait parameter measurement, estimation of joint kinematics, and postural balance analysis. In addition, the use of other sensing principles such as foot-force interaction measurement, electromyography techniques, electrogoniometers, force-myography, ultrasonic, piezoelectric, and microphone sensors has also been explored. The review also examined the commercially available wearable gait analysis systems. Additionally, a summary of recent progress in therapeutic approaches, viz., wearables, virtual reality (VR), and phytochemical compounds, has also been presented, explicitly targeting the neuro-motor and functional impairments associated with these disorders. Efforts toward therapeutic and functional rehabilitation through VR, wearables, and different phytochemical compounds are presented using recent examples of research across the commonly occurring neurodegenerative conditions [viz., Parkinson's disease (PD), Alzheimer's disease (AD), multiple sclerosis, Huntington's disease (HD), and amyotrophic lateral sclerosis (ALS)]. Studies exploring the potential role of Phyto compounds in mitigating commonly associated neurodegenerative pathologies such as mitochondrial dysfunction, α-synuclein accumulation, imbalance of free radicals, etc., are also discussed in breadth. Parameters such as joint angles, plantar pressure, and muscle force can be measured using portable and wearable sensors like accelerometers, gyroscopes, footswitches, force sensors, etc. Kinetic foot insoles and inertial measurement tools are widely explored for studying kinematic and kinetic parameters associated with gait. With advanced correlation algorithms and extensive RCTs, such measurement techniques can be an effective clinical and home-based monitoring and rehabilitation tool for neuro-impaired gait. As evident from the present literature, although the vast majority of works reported are not clinically and extensively validated to derive a firm conclusion about the effectiveness of such techniques, wearable sensors present a promising impact toward dealing with neurodegenerative motor disorders.
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Affiliation(s)
- Ratan Das
- Department of Biomedical Engineering, North-Eastern Hill University, Shillong, India
| | - Sudip Paul
- Department of Biomedical Engineering, North-Eastern Hill University, Shillong, India
| | - Gajendra Kumar Mourya
- Department of Biomedical Engineering, North-Eastern Hill University, Shillong, India
| | - Neelesh Kumar
- Biomedical Applications Unit, Central Scientific Instruments Organisation, Chandigarh, India
| | - Masaraf Hussain
- Department of Neurology, North Eastern Indira Gandhi Regional Institute of Health and Medical Sciences, Shillong, India
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