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Stroke commonly affects the ability of the upper extremities (UEs) to move normally. In clinical settings, identifying and measuring movement abnormality is challenging due to the imprecision and impracticality of available assessments. These challenges interfere with therapeutic tracking, communication, and treatment. We thus sought to develop an approach that blends precision and pragmatism, combining high-dimensional motion capture with out-of-distribution (OOD) detection. We used an array of wearable inertial measurement units to capture upper body motion in healthy and chronic stroke subjects performing a semi-structured, unconstrained 3D tabletop task. After data were labeled by human coders, we trained two deep learning models exclusively on healthy subject data to classify elemental movements (functional primitives). We tested these healthy subject-trained models on previously unseen healthy and stroke motion data. We found that model confidence, indexed by prediction probabilities, was generally high for healthy test data but significantly dropped when encountering OOD stroke data. Prediction probabilities worsened with more severe motor impairment categories and were directly correlated with individual impairment scores. Data inputs from the paretic UE, rather than trunk, most strongly influenced model confidence. We demonstrate for the first time that using OOD detection with high-dimensional motion data can reveal clinically meaningful movement abnormality in subjects with chronic stroke.
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This content will become publicly available on May 12, 2026
Examining Postural Responses to Perturbations in 3D: A Pilot Study
Affecting muscle spasticity, strength, and coordination, stroke results in alterations to muscle control and ability to compensate from unexpected perturbations. Post-stroke, upper extremity movements are heavily modified from perturbations, which increase the difficulty of activities of daily living (ADLs). Postural responses from upper extremity perturbations in healthy and stroke populations have been examined in movements constrained to 2D planar motion, and may provide insight as an assessment tool to help inform therapists to better structure rehabilitation training regimens towards individualized health care for improved long-term outcomes. However, implications on constraining motion in the horizontal plane are not clear and may reduce the generalizability of the findings to the movement through unconstrained 3D space necessary for ADLs. In this paper, we explore the effects of joint perturbations on the elbow and shoulder in unconstrained, gravity-compensated position holding tasks. We present a metric-diverse, dynamic task framework building upon previous 2D experiments designed to better assess rehabilitative efforts in movement trajectories with applied gravity compensation in three dimensional space aimed towards the generalizability of 3D motion. Results suggest that motion of multi-DoF joints display varied movement qualities in 3D space with robotic gravity compensation when compared to constrained planar movements.
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- Award ID(s):
- 2230971
- PAR ID:
- 10651810
- Publisher / Repository:
- IEEE
- Date Published:
- Page Range / eLocation ID:
- 1653 to 1658
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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