Phase-specific multimodal biomarkers enable explainable assessment of upper limb dysfunction in chronic stroke

Objective and precise assessment of upper limb dysfunction post-stroke is critical for guiding rehabilitation. While promising, current methods using wearable sensors and machine learning (ML) often lack interpretability and neglect underlying, phase-specific kinetic deficits (e.g., muscle forces and joint torques) within functional tasks. This study aimed to develop and validate an explainable assessment framework that leverages musculoskeletal kinetic modeling to extract phase-specific, multimodal (kinematic and kinetic) biomarkers to assess upper limb dysfunction in chronic stroke.

Sixty-five adults with chronic stroke and 20 healthy controls performed a standardized hand-to-mouth (HTM) task. Stroke participants were allocated to a model-development cohort (n = 47) and an independent test cohort (n = 18). Using IMU and sEMG data, we employed musculoskeletal modeling to extract phase-specific kinematic (e.g., inter-joint coordination, trunk displacement) and kinetic (e.g., mechanical work, smoothness, co-contraction index) biomarkers from four task phases. A Lasso regression model was trained to predict FMA-UL scores, validated via 5-fold cross-validation and the independent test cohort. Explainable AI (SHAP) was used to identify key predictive features.

Compared with controls, patients showed phase-specific alterations including greater trunk displacement and reduced inter-joint coordination and mechanical work (all p < 0.05). The Lasso model achieved strong performance in internal validation (R ² = 0.932; MAE = 0.799) and generalized well to the independent test cohort (R ² = 0.881; MAE = 0.954). SHAP identified trunk displacement in phase 2 (TD_2), elbow-shoulder coordination in phase 3 (IC_elb_elv_3), and trunk displacement in phase 3 (TD_3) as dominant predictors; larger trunk displacement contributed negatively to predicted FMA-UL scores.

Integrating phase-specific multimodal biomarkers with explainable ML yields an interpretable upper-limb dysfunction. By highlighting phase-specific kinetic and kinematic targets (e.g., trunk compensation and inter-joint coordination), the framework supports individualized, precision rehabilitation.