A Novel Methodology for Extracting and Evaluating Therapeutic Movements in Game-Based Motion Capture Rehabilitation Systems.

Virtual rehabilitation yields outcomes that are at least as good as traditional care for improving upper limb function and the capacity to carry out activities of daily living. Due to the advent of low-cost gaming systems and patient preference for game-based therapies, video game technology will likely be increasingly utilized in physical therapy practice in the coming years. Gaming systems that incorporate low-cost motion capture technology often generate large datasets of therapeutic movements performed over the course of rehabilitation. An infrastructure has yet to be established, however, to enable efficient processing of large quantities of movement data that are collected outside of a controlled laboratory setting. In this paper, a methodology is presented for extracting and evaluating therapeutic movements from game-based rehabilitation that occurs in uncontrolled and unmonitored settings. By overcoming these challenges, meaningful kinematic analysis of rehabilitation trajectory within an individual becomes feasible. Moreover, this methodological approach provides a vehicle for analyzing large datasets generated in uncontrolled clinical settings to enable better predictions of rehabilitation potential and dose-response relationships for personalized medicine.

Self-Supervised Learning for Electroencephalography.

Decades of research have shown machine learning superiority in discovering highly nonlinear patterns embedded in electroencephalography (EEG) records compared with conventional statistical techniques. However, even the most advanced machine learning techniques require relatively large, labeled EEG repositories. EEG data collection and labeling are costly. Moreover, combining available datasets to achieve a large data volume is usually infeasible due to inconsistent experimental paradigms across trials. Self-supervised learning (SSL) solves these challenges because it enables learning from EEG records across trials with variable experimental paradigms, even when the trials explore different phenomena. It aggregates multiple EEG repositories to increase accuracy, reduce bias, and mitigate overfitting in machine learning training. In addition, SSL could be employed in situations where there is limited labeled training data, and manual labeling is costly. This article: 1) provides a brief introduction to SSL; 2) describes some SSL techniques employed in recent studies, including EEG; 3) proposes current and potential SSL techniques for future investigations in EEG studies; 4) discusses the cons and pros of different SSL techniques; and 5) proposes holistic implementation tips and potential future directions for EEG SSL practices.

Predicting Improved Daily Use of the More Affected Arm Poststroke Following Constraint-Induced Movement Therapy.

BACKGROUND: Constraint-induced movement therapy (CI therapy) produces, on average, large and clinically meaningful improvements in the daily use of a more affected upper extremity in individuals with hemiparesis. However, individual responses vary widely. OBJECTIVE: The study objective was to investigate the extent to which individual characteristics before treatment predict improved use of the more affected arm following CI therapy. DESIGN: This study was a retrospective analysis of 47 people who had chronic (> 6 months) mild to moderate upper extremity hemiparesis and were consecutively enrolled in 2 CI therapy randomized controlled trials. METHODS: An enhanced probabilistic neural network model predicted whether individuals showed a low, medium, or high response to CI therapy, as measured with the Motor Activity Log, on the basis of the following baseline assessments: Wolf Motor Function Test, Semmes-Weinstein Monofilament Test of touch threshold, Motor Activity Log, and Montreal Cognitive Assessment. Then, a neural dynamic classification algorithm was applied to improve prognostic accuracy using the most accurate combination obtained in the previous step. RESULTS: Motor ability and tactile sense predicted improvement in arm use for daily activities following intensive upper extremity rehabilitation with an accuracy of nearly 100%. Complex patterns of interaction among these predictors were observed. LIMITATIONS: The fact that this study was a retrospective analysis with a moderate sample size was a limitation. CONCLUSIONS: Advanced machine learning/classification algorithms produce more accurate personalized predictions of rehabilitation outcomes than commonly used general linear models.