Enabling Automated and Personalized Motor Assessment in Neurorehabilitation: Generating Patient-Specific Reference Movements with a Virtual Humanoid Twin

Abstract

Recovering upper-limb motor functions impaired by trauma or neurological disease is a long and challenging process. To monitor a patient’s progress through the various stages of rehabilitation and guide therapy, regular movement assessment is essential. However, such evaluations are rarely conducted in clinical practice due to time constraints and the need for cumbersome equipment. A key limitation is the access to reference motion data, typically derived from averaged movements of unimpaired individuals, which requires new data collection for each task and lacks personalization (e.g., accounting for individual morphology or motor abilities). We present a novel method to generate patient-specific reference motions directly from the patient’s hand pose using a personalized model of the patient, the Virtual Humanoid Twin (VHT). By solving an ergonomic-based optimal control problem, our approach produces tailored reference motions without prior task-specific data. We validated this method on two motor tasks (reaching and pouring) using data from seven unimpaired participants, with and without an elbow orthosis restricting motion. Analysis of joint trajectories, range of motion, and normalized multi-dimensional Dynamic Time Warping confirmed that VHT-generated motions were more ergonomic than those with the orthosis and closely matched natural movements. The method’s rapid generation time can also enable real-time reference motion estimation, parallel to the patient’s movements. This innovation simplifies access to reference motions while providing personalization. It creates opportunities for automated motor assessment in neurorehabilitation, enhancing patient recovery tracking through regular evaluations.