Independent AI pipelines for rehabilitation exercise assessment and cognitive status classification: a retrospective proof of concept study

Digital rehabilitation can extend remote exercise monitoring and cognitive health support, but available public datasets often capture these functions separately. This study evaluates two independent machine learning pipelines within a conceptual telerehabilitation architecture rather than a clinically validated integrated system.

The physical pipeline used the WLU Rehabilitation Posture dataset. After quality control and removal of exact duplicates, we retained 315 unique videos of arm raise, knee extension, and sit-to-stand with dataset-defined correct or incorrect labels. We converted 24 video frames into 182 privacy-preserving features: appearance, shape, gradient, optical flow, temporal, and metadata. We compared candidate algorithms using stratified five-fold cross-validation within the training data, reserving a 20% holdout set for final evaluation. The cognitive pipeline analyzed 5,000 records from the Community Elderly Cognitive Health dataset using 14 demographic, clinical, lifestyle, psychological, and cognitive-assessment predictors. Because target generation is not independently documented and MMSE and GDS are target-proximal, this module is interpreted as concurrent cognitive status classification rather than as independent risk prediction.

Random forest achieved video-level physical six-class accuracy of 0.857, balanced accuracy of 0.846, macro F1 of 0.849, and Matthews correlation coefficient of 0.829; because participant and session identifiers were unavailable, these estimates do not represent participant-independent validation. Exercise identity accuracy was 0.984, whereas correct versus incorrect execution accuracy was 0.857. Extra Trees achieved cognitive status accuracy of 0.936, balanced accuracy of 0.918, macro F1 of 0.923, and ROC AUC of 0.978. MMSE, GDS, physical activity, and glucose level were influential cognitive predictors.

The two pipelines produced strong technical discrimination and interpretable outputs, but we evaluated them on independent, unlinked datasets. The physical cohort was not confirmed as geriatric; participant- or session-level groupings were unavailable, and the provenance of the cognitive data and the target construction remain uncertain. The findings therefore support a retrospective proof of concept for separate clinician-supervised decision-support components, not for patient-level multimodal fusion, diagnosis, treatment effectiveness, recovery, or autonomous care.