Non-Invasive Motion Analysis for Stroke Rehabilitation using off the Shelf 3D Sensors

Eichler, Nadav; Hel‐Or, Hagit; Shmishoni, Ilan; Itah, Dorit; Gross, Bella; Raz, Shmuel

doi:10.1109/ijcnn.2018.8489593

Cited by 13 publications

(24 citation statements)

References 26 publications

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“…This is a very difficult task considering the multitude of factors involved in assessment. Eichler et al [47] proposed a two Kinect camera based system for automated FMA. The two cameras were placed at a 45 degree angle with respect to the subject.…”

Section: Clinical Scoringmentioning

confidence: 99%

A review of computer vision-based approaches for physical rehabilitation and assessment

et al. 2021

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The computer vision community has extensively researched the area of human motion analysis, which primarily focuses on pose estimation, activity recognition, pose or gesture recognition and so on. However for many applications, like monitoring of functional rehabilitation of patients with musculo skeletal or physical impairments, the requirement is to comparatively evaluate human motion. In this survey, we capture important literature on vision-based monitoring and physical rehabilitation that focuses on comparative evaluation of human motion during the past two decades and discuss the state of current research in this area. Unlike other reviews in this area, which are written from a clinical objective, this article presents research in this area from a computer vision application perspective. We propose our own taxonomy of computer vision-based rehabilitation and assessment research which are further divided into sub-categories to capture novelties of each research. The review discusses the challenges of this domain due to the wide ranging human motion abnormalities and difficulty in automatically assessing those abnormalities. Finally, suggestions on the future direction of research are offered.

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Section: Clinical Scoringmentioning

confidence: 99%

A review of computer vision-based approaches for physical rehabilitation and assessment

et al. 2021

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“…The first type focuses on detecting abnormalities in relatively long, repetitive move-ments [1,2,3,4,5,6,7,8,9], such as gait, as visualized in Figure 1. The second type of movements, on the other hand, focuses on assessing structured movements [10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27], such as movements from the Fugl-Meyer Assessment (FMA) [28] or Berg Balance Scale (BBS) [29] medical assessments, as visualized in Figure 2, which usually have clear definitions of starting positions, ending positions, objectives and constraints. Fig.…”

Section: Movement Assessmentmentioning

confidence: 99%

A Comprehensive Review of Skeleton-based Movement Assessment Methods

Hakim

2020

Preprint

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The raising availability of 3D cameras and dramatic improvement of computer vision algorithms in the recent decade, accelerated the research of automatic movement assessment solutions. Such solutions can be implemented at home, using affordable equipment and dedicated software. In this paper, we divide the movement assessment task into secondary tasks and explain why they are needed and how they can be addressed. We review the recent solutions for automatic movement assessment from skeleton videos, comparing them by their objectives, features, movement domains and algorithmic approaches. In addition, we discuss the status of the research on this topic in a high level.

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“…This process typically requires a specialized calibration session with specific calibration tools, a process that is impractical and infeasible for systems such as ours that are targeted for use in the community. Thus, we used a novel multi-camera tracking system developed by our team [48,70] in which synchronization and calibration are performed automatically and on the fly by exploiting the patient's motion. The skeletal data acquired by the two calibrated cameras can then be easily integrated.…”

Section: Motion Capture and Trackingmentioning

confidence: 99%

Automatic and Efficient Fall Risk Assessment Based on Machine Learning

Eichler

Raz

Toledano-Shubi³

et al. 2022

Sensors

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Automating fall risk assessment, in an efficient, non-invasive manner, specifically in the elderly population, serves as an efficient means for implementing wide screening of individuals for fall risk and determining their need for participation in fall prevention programs. We present an automated and efficient system for fall risk assessment based on a multi-depth camera human motion tracking system, which captures patients performing the well-known and validated Berg Balance Scale (BBS). Trained machine learning classifiers predict the patient’s 14 scores of the BBS by extracting spatio-temporal features from the captured human motion records. Additionally, we used machine learning tools to develop fall risk predictors that enable reducing the number of BBS tasks required to assess fall risk, from 14 to 4–6 tasks, without compromising the quality and accuracy of the BBS assessment. The reduced battery, termed Efficient-BBS (E-BBS), can be performed by physiotherapists in a traditional setting or deployed using our automated system, allowing an efficient and effective BBS evaluation. We report on a pilot study, run in a major hospital, including accuracy and statistical evaluations. We show the accuracy and confidence levels of the E-BBS, as well as the average number of BBS tasks required to reach the accuracy thresholds. The trained E-BBS system was shown to reduce the number of tasks in the BBS test by approximately 50% while maintaining 97% accuracy. The presented approach enables a wide screening of individuals for fall risk in a manner that does not require significant time or resources from the medical community. Furthermore, the technology and machine learning algorithms can be implemented on other batteries of medical tests and evaluations.

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Non-Invasive Motion Analysis for Stroke Rehabilitation using off the Shelf 3D Sensors

Cited by 13 publications

References 26 publications

A review of computer vision-based approaches for physical rehabilitation and assessment

A review of computer vision-based approaches for physical rehabilitation and assessment

A Comprehensive Review of Skeleton-based Movement Assessment Methods

Automatic and Efficient Fall Risk Assessment Based on Machine Learning

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