Gait disorder rehabilitation using vision and non-vision based sensors: A systematic review

Ali, Md. Asraf; Sundaraj, Kenneth; Ahmad, Badlishah; Ahamed, Nizam Uddin; Islam, Md. Anamul

doi:10.17305/bjbms.2012.2484

Cited by 29 publications

(17 citation statements)

References 85 publications

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“…The current reference standard in motion capture technology is optical motion capture (OMC) (Ancillao, 2016). However, OMC's clinical appeal is hampered by issues including high costs, expert operation requirements, cumbersome setup and post-processing procedures, marker occlusion and limited ecological validity due to being laboratory-restricted (Ali et al, 2012;Iosa et al, 2016). Thus, despite providing high quality data, OMC presents an access barrier to clinicians and patients.…”

Section: Introductionmentioning

confidence: 99%

Concurrent validity and within-session reliability of gait kinematics measured using an inertial motion capture system with repeated calibration

Berner

Cockcroft

Morris

et al. 2020

Journal of Bodywork and Movement Therapies

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Introduction: Wearable inertial measurement units (IMUs) enable gait analysis in the clinic, but require calibrations that may affect subsequent gait measurements. This study assessed concurrent validity and within-session reliability of gait kinematics measured by a frequently calibrated IMU-based system. Calibration pose accuracy and intra-rater repeatability, and IMU orientation tracking accuracy, were additionally quantified. Methods: Calibration poses and gait were recorded in 15 women using IMUs and optical motion capture (OMC) (reference standard) simultaneously. Participants performed six consecutive trials: each comprising a calibration pose and a walk. IMU tracking was assessed separately (once-off) using technical static and dynamic tests. Differences of > 5 constituted clinical significance. Results: Concurrent validity for gait revealed clinically significant between-system differences for sagittal angles (root-mean-square error [RMSE] 6.7 e15.0 ; bias À9.3 e3.0) and hip rotation (RMSE 7.9 ; bias À4.2). After removing modelling offsets, differences for all angles (except hip rotation) were < 5. Gait curves correlated highly between systems (r > 0.8), except hip rotation, pelvic tilt and-obliquity. Within-session reliability of IMU-measured gait angles was clinically acceptable (standard error of measurement [SEM] < 5). Calibration poses were repeatable (SEM 0.3 e2.2). Pose accuracy revealed mean absolute differences (MAD) < 5 for all angles except sagittal ankle, hip and pelvis. IMU tracking accuracy demonstrated RMSE 2.0. Conclusion: A frequently calibrated IMU system provides reliable gait measurements; comparing highly to OMC after removing modelling differences. Calibration poses can be implemented accurately for most angles and consistently. IMU-measured gait data are clinically useful and comparable within participants, but should not be compared to OMC-measured data.

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Section: Introductionmentioning

confidence: 99%

Concurrent validity and within-session reliability of gait kinematics measured using an inertial motion capture system with repeated calibration

Berner

Cockcroft

Morris

et al. 2020

Journal of Bodywork and Movement Therapies

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“…A final approach is by fusing both visual (both with and without using optical markers) and inertial sensors, in order to solve the intrinsic problems in each system [19]. Concerning the rehabilitation procedure of gait disorders, all the approaches, such as using just inertial sensors, or optical sensors or fusing both of them have been tested and studied [20], [21], [22], [23].…”

Section: State Of the Artmentioning

confidence: 99%

Low Cost Inertial Measurement Unit for Motion Capture in Biomedical Applications

Lourenço¹,

Martins²,

Almeida³

et al. 2016

Technological Innovation for Cyber-Physical Systems

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A low-cost inertial measurement unit has been developed for accurate motion capture, allowing real-time spatial position registration (linear and angular) of the user's whole-body. For this, we implemented a dedicated circuit for 9 degrees of freedom motion sensors, composed of an accelerometer, gyroscope and a magnetometer. We also applied signal processing and data fusion algorithms to prevent the inherent drift of the position signal. This drift is known to exist during the sensor integration process and the implemented algorithms showed promising results. This system is meant to be used in two specific biomedical applications. The first one is linked to the development of a low-cost system for gait analysis of the whole-body, which can be used in home-based rehabilitation systems. The second application is related to the realtime analysis of working postures and the identification of ergonomic risk factors for musculoskeletal disorders.

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“…The two main families of sensors which have been commonly used in human motion capture for rehabilitation engineering are optoelectronics and nonoptoelectronics sensors [ 2 , 3 ]. The first groups may or may not use markers to track movements.…”

Section: Introductionmentioning

confidence: 99%

A Review on Technical and Clinical Impact of Microsoft Kinect on Physical Therapy and Rehabilitation

Hondori

Khademi

2014

Journal of Medical Engineering

239

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This paper reviews technical and clinical impact of the Microsoft Kinect in physical therapy and rehabilitation. It covers the studies on patients with neurological disorders including stroke, Parkinson's, cerebral palsy, and MS as well as the elderly patients. Search results in Pubmed and Google scholar reveal increasing interest in using Kinect in medical application. Relevant papers are reviewed and divided into three groups: (1) papers which evaluated Kinect's accuracy and reliability, (2) papers which used Kinect for a rehabilitation system and provided clinical evaluation involving patients, and (3) papers which proposed a Kinect-based system for rehabilitation but fell short of providing clinical validation. At last, to serve as technical comparison to help future rehabilitation design other sensors similar to Kinect are reviewed.

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Gait disorder rehabilitation using vision and non-vision based sensors: A systematic review

Cited by 29 publications

References 85 publications

Concurrent validity and within-session reliability of gait kinematics measured using an inertial motion capture system with repeated calibration

Concurrent validity and within-session reliability of gait kinematics measured using an inertial motion capture system with repeated calibration

Low Cost Inertial Measurement Unit for Motion Capture in Biomedical Applications

A Review on Technical and Clinical Impact of Microsoft Kinect on Physical Therapy and Rehabilitation

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