Using a Mobile Application to Assess Knee Valgus in Healthy and Post-Anterior Cruciate Ligament Reconstruction Participants

Ho, Kang-Jey; Deaver, Brenda Benson; Nelson, Tyrel; Turner, Catherine

doi:10.1123/jsr.2018-0278

Cited by 2 publications

(1 citation statement)

References 8 publications

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“…High precision sensing and tracking systems can work well in spacious and supervised clinical environments, but are currently not yet appropriate for a typical home setting. The use of marker-based tracking systems or complex exoskeletons are simply too expensive, challenging to use, and obtrusive in the home (11)(12)(13).…”

Section: Introductionmentioning

confidence: 99%

Automated Movement Assessment in Stroke Rehabilitation

et al. 2021

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We are developing a system for long term Semi-Automated Rehabilitation At the Home (SARAH) that relies on low-cost and unobtrusive video-based sensing. We present a cyber-human methodology used by the SARAH system for automated assessment of upper extremity stroke rehabilitation at the home. We propose a hierarchical model for automatically segmenting stroke survivor's movements and generating training task performance assessment scores during rehabilitation. The hierarchical model fuses expert therapist knowledge-based approaches with data-driven techniques. The expert knowledge is more observable in the higher layers of the hierarchy (task and segment) and therefore more accessible to algorithms incorporating high level constraints relating to activity structure (i.e., type and order of segments per task). We utilize an HMM and a Decision Tree model to connect these high level priors to data driven analysis. The lower layers (RGB images and raw kinematics) need to be addressed primarily through data driven techniques. We use a transformer based architecture operating on low-level action features (tracking of individual body joints and objects) and a Multi-Stage Temporal Convolutional Network(MS-TCN) operating on raw RGB images. We develop a sequence combining these complimentary algorithms effectively, thus encoding the information from different layers of the movement hierarchy. Through this combination, we produce a robust segmentation and task assessment results on noisy, variable and limited data, which is characteristic of low cost video capture of rehabilitation at the home. Our proposed approach achieves 85% accuracy in per-frame labeling, 99% accuracy in segment classification and 93% accuracy in task completion assessment. Although the methodology proposed in this paper applies to upper extremity rehabilitation using the SARAH system, it can potentially be used, with minor alterations, to assist automation in many other movement rehabilitation contexts (i.e., lower extremity training for neurological accidents).

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Section: Introductionmentioning

confidence: 99%

Automated Movement Assessment in Stroke Rehabilitation

et al. 2021

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Examination of the Feasibility of a 2-Dimensional Portable Assessment of Knee Joint Stability: A Pilot Study

Zerega

Killelea

Losciale

et al. 2020

Journal of Applied Biomechanics

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Rupture of the anterior cruciate ligament (ACL) remains extremely common, with over 250,000 injuries annually. Currently, clinical tests have poor utility to accurately screen for ACL injury risk in athletes. In this study, the position of a knee marker was tracked in 2-dimensional planes to predict biomechanical variables associated with ACL injury risk. Three-dimensional kinematics and ground reaction forces were collected during bilateral, single-leg stop-jump tasks for 44 healthy male military personnel. Knee marker position data were extracted to construct 2-dimensional 95% prediction ellipses in each anatomical plane. Knee marker variables included: ellipse areas, major/minor axes lengths, orientation of ellipse axes, absolute ranges of knee position, and medial knee collapse. These variables were then used as predictor variables in stepwise multiple linear regression analyses for 7 biomechanical variables associated with ACL injury risk. Knee flexion excursion, normalized peak vertical ground reaction forces, and knee flexion angle at initial contact were the response variables that generated the highest adjusted R2 values: .71, .37, and .31, respectively. The results of this study provide initial support for the hypothesis that tracking a single marker during 2-dimensional analysis can accurately reflect the information gathered from 3-dimensional motion analysis during a task assessing knee joint stability.

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Using a Mobile Application to Assess Knee Valgus in Healthy and Post-Anterior Cruciate Ligament Reconstruction Participants

Cited by 2 publications

References 8 publications

Automated Movement Assessment in Stroke Rehabilitation

Automated Movement Assessment in Stroke Rehabilitation

Examination of the Feasibility of a 2-Dimensional Portable Assessment of Knee Joint Stability: A Pilot Study

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