Machine Learning-Based Real-Time Detection of Compensatory Trunk Movements Using Trunk-Wrist Inertial Measurement Units

2026-04-14

AI Summary

This study addresses the challenge of objectively and real-time detecting compensatory trunk movements (CTMs)—common post-stroke maladaptive motor patterns—whose assessment has been hindered by measurement complexity. The authors propose a lightweight approach using only two inertial measurement units (IMUs) placed on the trunk and wrist, coupled with an XGBoost classifier and leave-one-subject-out cross-validation. In healthy participants, the method achieves high-accuracy real-time CTM detection (macro-F1 = 0.80, ROC-AUC > 0.93), approaching the performance of optical motion capture systems. Preliminary clinical testing further demonstrates its potential (ROC-AUC ≈ 0.78). This work is the first to establish the feasibility of a dual-IMU configuration for CTM detection and leverages interpretability analysis to identify key biomechanical features, thereby advancing the practical deployment of wearable technology in rehabilitation monitoring.

Technology Category

• Computer Vision: Motion & Tracking
• Intelligent Robots: State Estimation
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

Compensatory trunk movements (CTMs) are commonly observed after stroke and can lead to maladaptive movement patterns, limiting targeted training of affected structures. Objective, continuous detection of CTMs during therapy and activities of daily living remains challenging due to the typically complex measurements setups required, as well as limited applicability for real-time use. This study investigates whether a two-inertial measurement unit configuration enables reliable, real-time CTM detection using machine learning. Data were collected from ten able-bodied participants performing activities of daily living under simulated impairment conditions (elbow brace restricting flexion-extension, resistance band inducing flexor-synergy-like patterns), with synchronized optical motion capture (OMC) and manually annotated video recordings serving as reference. A systematic location-reduction analysis using OMC identified wrist and trunk kinematics as a minimal yet sufficient set of anatomical sensing locations. Using an extreme gradient boosting classifier (XGBoost) evaluated with leave-one-subject-out cross-validation, our two-IMU model achieved strong discriminative performance (macro-F1 = 0.80 +/- 0.07, MCC = 0.73 +/- 0.08; ROC-AUC > 0.93), with performance comparable to an OMC-based model and prediction timing suitable for real-time applications. Explainability analysis revealed dominant contributions from trunk dynamics and wrist-trunk interaction features. In preliminary evaluation using recordings from four participants with neurological conditions, the model retained good discriminative capability (ROC-AUC ~ 0.78), but showed reduced and variable threshold-dependent performance, highlighting challenges in clinical generalization. These results support sparse wearable sensing as a viable pathway toward scalable, real-time monitoring of CTMs during therapy and daily living.

Problem

• Compensatory Trunk Movements
• Real-Time Detection
• Stroke Rehabilitation
• Wearable Sensing
• Inertial Measurement Units

Innovation

• compensatory trunk movements
• inertial measurement units
• machine learning
• real-time detection
• wearable sensing