Fall Risk Assessment in Stroke Survivors: A Machine Learning Model Using Detailed Motion Data from Common Clinical Tests and Motor-Cognitive Dual-Tasking

Abstract

Background: Falls are common and dangerous for stroke survivors. Current fall risk assessment methods rely on subjective scales. Objective sensor-based methods could improve prediction accuracy. Objective: Develop machine learning models using inertial sensors to objectively classify fall risk in stroke survivors. Determine optimal sensor configurations and clinical test protocols. Methods: 21 stroke survivors performed balance, Timed Up and Go, 10 Meter Walk, and Sit-to-Stand tests with and without dual-tasking. A total of 8 motion sensors captured lower limb and trunk kinematics, and 92 spatiotemporal gait and clinical features were extracted. Supervised models—Support Vector Machine, Logistic Regression, and Random Forest—were implemented to classify high vs. low fall risk. Sensor setups and test combinations were evaluated. Results: The Random Forest model achieved 91% accuracy using dual-task balance sway and Timed Up and Go walk time features. Single thorax sensor models performed similarly to multi-sensor models. Balance and Timed Up and Go best-predicted fall risk. Conclusion: Machine learning models using minimal inertial sensors during clinical assessments can accurately quantify fall risk in stroke survivors. Single thorax sensor setups are effective. Findings demonstrate a feasible objective fall screening approach to assist rehabilitation.

Detecting Fatigue during Exoskeleton-Assisted Trunk Flexion Tasks: A Machine Learning Approach

“…The results of our analysis demonstrated that the Random Forest (RF) and XGBoost (XGB) algorithms consistently outperformed the Support Vector Machine (SVM) and Logistic Regression (LR) algorithms in terms of accuracy (A), recall (R), and specificity (S) across all three types of measures-muscle activity, whole-body stability, and trunk motion. Similar outcomes have been reported in past studies, where RF and Gradient Boosted Decision Tree algorithms showed better performance over SVM [35,38,43]. Particularly noteworthy were the high values obtained for measures of muscle activity, with the RF (A: 94.5%, R: 93.6%, S: 95.5%) and XGB (A: 94.6%, R: 94.9%, S: 94.5%) algorithms utilizing 48 features from all four EMG sensors.…”

“…In the context of predicting differences in physiological conditions, machine learning classification algorithms play a pivotal role [33,34]. These include (a) decision trees, which partition the feature space into distinct regions based on simple decision rules, are interpretable, and are suitable for tasks with categorical outcomes [35], (b) Support Vector Machines (SVM), which aim to find the hyperplane that best separates different classes while maximizing the margin between them, making them effective for binary classification tasks with high-dimensional feature spaces [36], and (c) ensemble methods such as Random Forests and Gradient Boosting, which combine multiple classifiers to improve predictive performance and robustness [37][38][39]. Previous studies have demonstrated the utility of machine learning techniques in detecting fatigue during tasks such as walking, thereby reducing the risk of injury due to overexertion.…”

Post-Stroke Functional Changes: In-Depth Analysis of Clinical Tests and Motor-Cognitive Dual-Tasking Using Wearable Sensors

“…The metrics showing pronounced stroke-related impairments like TUG turn time and 10MWT gait speed could also serve as sensitive outcomes to track subtle longitudinal improvements resulting from interventions. Collaborations spanning engineers, clinicians, and data scientists would allow the development of predictive models leveraging these metrics to forecast fall risk or functional prognosis [55,56]. Overall, the methodology presented contributes to future technologically enabled precision rehabilitation paradigms and could optimize the quality of care.…”

Machine Learning-Based Fatigue Level Prediction for Exoskeleton-Assisted Trunk Flexion Tasks Using Wearable Sensors

“…Decision trees incorporate a hierarchical tree form with structured nodes (root, decision, and leaf nodes) to categorize datapoints into subsets [12][13][14], while Support Vector Machines (SVM) aim to establish a boundary between predefined sets of datapoints [15][16][17]. Meanwhile, ensemble methods like Random Forests combine multiple decision trees to improve predictive performance and robustness [18][19][20]. Prior studies have implemented these algorithms for detecting fatigue.…”