Personalized Activity Profiling in Post-Stroke Care via Explainable Evidential Clustering

Monitoring upper-limb activity in daily life is essential for personalizing rehabilitation and assessing functional recovery after stroke. Wearable accelerometers enable continuous and ecological measurement of arm use, but the resulting high-dimensional time-series data remain difficult to interpret in clinical practice. In this work, we propose an interpretable activity profiling framework for post-stroke care based on explainable evidential clustering. Accelerometer signals from both wrists are segmented into five-minute windows and transformed into clinically meaningful features describing functional arm use. Two clustering pipelines are investigated: (i) Evidential C-Means followed by an interpretable decision-tree approximation using Iterative Evidential Mistakeness Minimization (IEMM), and (ii) K-means followed by a Belief Rule-Based Classification System (BRBCS) inspired from Fuzzy Rule-Based Classification Systems (FRBCS). Both approaches produce transparent explanations of cluster assignments while explicitly modeling uncertainty. Clustering results are combined with patient-reported activity logs using evidence theory to derive activity-level profiles that characterize arm use patterns and associated uncertainty. Experiments on real-world post-stroke data show that the proposed framework provides clinically interpretable insights into patients’ behavior across daily activities, supporting individualized rehabilitation planning while preserving robustness to noisy and uncertain data.