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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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Boldo M, Di Marco R, Martini E, Nardon M, Bertucco M, Bombieri N. On the reliability of single-camera markerless systems for overground gait monitoring. Comput Biol Med 2024; 171:108101. [PMID: 38340440 DOI: 10.1016/j.compbiomed.2024.108101] [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: 06/09/2023] [Revised: 01/16/2024] [Accepted: 02/04/2024] [Indexed: 02/12/2024]
Abstract
BACKGROUND AND OBJECTIVE Motion analysis is crucial for effective and timely rehabilitative interventions on people with motor disorders. Conventional marker-based (MB) gait analysis is highly time-consuming and calls for expensive equipment, dedicated facilities and personnel. Markerless (ML) systems may pave the way to less demanding gait monitoring, also in unsupervised environments (i.e., in telemedicine). However,scepticism on clinical usability of relevant outcome measures has hampered its use. ML is normally used to analyse treadmill walking, which is significantly different from the more physiological overground walking. This study aims to provide end-users with instructions on using a single-camera markerless system to obtain reliable motion data from overground walking, while clinicians will be instructed on the reliability of obtained quantities. METHODS The study compares kinematics obtained from ML systems to those concurrently obtained from marker-based systems, considering different stride counts and subject positioning within the capture volume. RESULTS The findings suggest that five straight walking trials are sufficient for collecting reliable kinematics with ML systems. Precision on joint kinematics decreased at the boundary of the capture volume. Excellent correlation was found between ML and MB systems for hip and knee angles (0.92 CONCLUSION Single-camera markerless motion capture systems have great potential in assessing human joint kinematics during overground walking. Clinicians can confidently rely on estimated joint kinematics while walking, enabling personalized interventions and improving accessibility to remote evaluation and rehabilitation services, as long as: (i) the camera is positioned to capture someone walking back and forth at least five times with good visibility of the entire body silhouette; (ii) the walking path is at least 2 m long; and (iii) images captured at the boundaries of the camera image plane should be discarded.
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Affiliation(s)
- Michele Boldo
- Department of Computer Science, University of Verona, Strada Le Grazie, 15, Verona, 37134, Italy.
| | - Roberto Di Marco
- Department of Engineering for Innovation Medicine, University of Verona, Strada Le Grazie, 15, Verona, 37134, Italy.
| | - Enrico Martini
- Department of Computer Science, University of Verona, Strada Le Grazie, 15, Verona, 37134, Italy.
| | - Mauro Nardon
- Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Via Casorati, 43, Verona, 37131, Italy.
| | - Matteo Bertucco
- Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Via Casorati, 43, Verona, 37131, Italy.
| | - Nicola Bombieri
- Department of Engineering for Innovation Medicine, University of Verona, Strada Le Grazie, 15, Verona, 37134, Italy.
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Sheng B, Chen X, Cheng J, Zhang Y, Xie SSQ, Tao J, Duan C. A novel scoring approach for the Wolf Motor Function Test in stroke survivors using motion-sensing technology and machine learning: A preliminary study. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 243:107887. [PMID: 37913714 DOI: 10.1016/j.cmpb.2023.107887] [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: 12/08/2022] [Revised: 10/23/2023] [Accepted: 10/24/2023] [Indexed: 11/03/2023]
Abstract
BACKGROUND AND OBJECTIVE Human-administered clinical scales, such as the Functional Ability Scale of the Wolf Motor Function Test (WMFT-FAS), are widely utilized to evaluate upper-limb motor function in stroke survivors. However, these scales are generally subjective and labor-intensive. To end this, we proposed a novel scoring approach for the motor function assessment. METHODS The proposed novel scoring approach mainly contained one Microsoft Kinect v2, one customized motion tracking system, and one customized intelligent scoring system. Specifically, the Kinect v2 was used to capture stroke survivors' functional movements, the motion tracking system was developed for recording the gathered movement data, and the intelligent scoring system (kernel: feed-forward neural network, FFNN) was developed to evaluate movement quality and provide corresponding WMFT-FAS scores. Several methods have been applied to enhance the approach's usability, such as singular spectrum analysis and multi-ReliefF method. RESULTS Sixteen stroke survivors and ten healthy subjects were recruited for validation. Inspiring results of the proposed approach were achieved when compared with the clinical scores provided by a physiotherapist: 0.924 ± 0.027 for accuracy, 0.875 ± 0.063 for F1-score, 0.915 ± 0.051 for sensitivity, 0.969 ± 0.013 for specificity, 0.952 ± 0.038 for AUC, 0.098 ± 0.037 for mean absolute error, and 0.214 ± 0.078 for root mean squared error. CONCLUSIONS The results indicate that the proposed novel scoring approach can provide objective and accurate assessment scores, which can help physiotherapists make individualized treatment decisions.
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Affiliation(s)
- Bo Sheng
- School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 200444, China
| | - Xiaohui Chen
- School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 200444, China
| | - Jian Cheng
- School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 200444, China
| | - Yanxin Zhang
- Department of Exercise Sciences, The University of Auckland, Auckland, 1010, New Zealand
| | - Shane Sheng Quan Xie
- School of Electronic and Electrical Engineering, University of Leeds, Leeds, LS2 9JT, United Kingdom
| | - Jing Tao
- School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 200444, China
| | - Chaoqun Duan
- School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 200444, China.
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Ino T, Samukawa M, Ishida T, Wada N, Koshino Y, Kasahara S, Tohyama H. Validity of AI-Based Gait Analysis for Simultaneous Measurement of Bilateral Lower Limb Kinematics Using a Single Video Camera. SENSORS (BASEL, SWITZERLAND) 2023; 23:9799. [PMID: 38139644 PMCID: PMC10747245 DOI: 10.3390/s23249799] [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: 11/16/2023] [Revised: 12/02/2023] [Accepted: 12/12/2023] [Indexed: 12/24/2023]
Abstract
Accuracy validation of gait analysis using pose estimation with artificial intelligence (AI) remains inadequate, particularly in objective assessments of absolute error and similarity of waveform patterns. This study aimed to clarify objective measures for absolute error and waveform pattern similarity in gait analysis using pose estimation AI (OpenPose). Additionally, we investigated the feasibility of simultaneous measuring both lower limbs using a single camera from one side. We compared motion analysis data from pose estimation AI using video footage that was synchronized with a three-dimensional motion analysis device. The comparisons involved mean absolute error (MAE) and the coefficient of multiple correlation (CMC) to compare the waveform pattern similarity. The MAE ranged from 2.3 to 3.1° on the camera side and from 3.1 to 4.1° on the opposite side, with slightly higher accuracy on the camera side. Moreover, the CMC ranged from 0.936 to 0.994 on the camera side and from 0.890 to 0.988 on the opposite side, indicating a "very good to excellent" waveform similarity. Gait analysis using a single camera revealed that the precision on both sides was sufficiently robust for clinical evaluation, while measurement accuracy was slightly superior on the camera side.
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Affiliation(s)
- Takumi Ino
- Graduate School of Health Sciences, Hokkaido University, Sapporo 0600812, Japan;
- Department of Physical Therapy, Faculty of Health Sciences, Hokkaido University of Science, Sapporo 0068585, Japan
| | - Mina Samukawa
- Faculty of Health Sciences, Hokkaido University, Sapporo 0600812, Japan
| | - Tomoya Ishida
- Faculty of Health Sciences, Hokkaido University, Sapporo 0600812, Japan
| | - Naofumi Wada
- Department of Information and Computer Science, Faculty of Engineering, Hokkaido University of Science, Sapporo 0068585, Japan;
| | - Yuta Koshino
- Faculty of Health Sciences, Hokkaido University, Sapporo 0600812, Japan
| | - Satoshi Kasahara
- Faculty of Health Sciences, Hokkaido University, Sapporo 0600812, Japan
| | - Harukazu Tohyama
- Faculty of Health Sciences, Hokkaido University, Sapporo 0600812, Japan
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Su JM, Chen KY, Wu SM, Lee KY, Ho SC. A mobile-based airway clearance care system using deep learning-based vision technology to support personalized home-based pulmonary rehabilitation for COAD patients: Development and usability testing. Digit Health 2023; 9:20552076231207206. [PMID: 37841513 PMCID: PMC10571692 DOI: 10.1177/20552076231207206] [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: 02/17/2023] [Accepted: 09/26/2023] [Indexed: 10/17/2023] Open
Abstract
Background Excessive mucus secretion is a serious issue for patients with chronic obstructive airway disease (COAD), which can be effectively managed through postural drainage and percussion (PD + P) during pulmonary rehabilitation (PR). Home-based (H)-PR can be as effective as center-based PR but lacks professional supervision and timely feedback, leading to low motivation and adherence. Telehealth home-based pulmonary (TH-PR) has emerged to assist H-PR, but video conferencing and telephone calls remain the main approaches for COAD patients. Therefore, research on effectively assisting patients in performing PD + P during TH-PR is limited. Objective This study developed a mobile-based airway clearance care for chronic obstructive airway disease (COAD-MoAcCare) system to support personalized TH-PR for COAD patients and evaluated its usability through expert validation. Methods The COAD-MoAcCare system uses a mobile device through deep learning-based vision technology to monitor, guide, and evaluate COAD patients' PD + P operations in real time during TH-PR programs. Medical personnel can manage and monitor their personalized PD + P and operational statuses through the system to improve TH-PR performance. Respiratory therapists from different hospitals evaluated the system usability using system questionnaires based on the technology acceptance model, system usability scale (SUS), and task load index (NASA-TLX). Results Eleven participant therapists were highly satisfied with the COAD-MoAcCare system, rating it between 4.1 and 4.6 out of 5.0 on all scales. The system demonstrated good usability (SUS score of 74.1 out of 100) and a lower task load (NASA-TLX score of 30.0 out of 100). The overall accuracy of PD + P operations reached a high level of 97.5% by comparing evaluation results of the system by experts. Conclusions The COAD-MoAcCare system is the first mobile-based method to assist COAD patients in conducting PD + P in TH-PR. It was proven to be usable by respiratory therapists, so it is expected to benefit medical personnel and COAD patients. It will be further evaluated through clinical trials.
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Affiliation(s)
- Jun-Ming Su
- Department of Information and Learning Technology, National University of Tainan, Tainan, Taiwan
| | - Kuan-Yuan Chen
- Division of Pulmonary Medicine, Department of Internal Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan
- Graduate Institute of Clinical Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Sheng-Ming Wu
- Division of Pulmonary Medicine, Department of Internal Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan
- Division of Pulmonary Medicine, Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Kang-Yun Lee
- Division of Pulmonary Medicine, Department of Internal Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan
- Graduate Institute of Clinical Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
- Division of Pulmonary Medicine, Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Shu-Chuan Ho
- Division of Pulmonary Medicine, Department of Internal Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan
- School of Respiratory Therapy, College of Medicine, Taipei Medical University, Taipei, Taiwan
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