  Exercise-based rehabilitation programs have proven to be effective in
enhancing the quality of life and reducing mortality and rehospitalization
rates. AI-driven virtual rehabilitation, which allows patients to independently
complete exercises at home, utilizes AI algorithms to analyze exercise data,
providing feedback to patients and updating clinicians on their progress. These
programs commonly prescribe a variety of exercise types, leading to a distinct
challenge in rehabilitation exercise assessment datasets: while abundant in
overall training samples, these datasets often have a limited number of samples
for each individual exercise type. This disparity hampers the ability of
existing approaches to train generalizable models with such a small sample size
per exercise. Addressing this issue, our paper introduces a novel supervised
contrastive learning framework with hard and soft negative samples that
effectively utilizes the entire dataset to train a single model applicable to
all exercise types. This model, with a Spatial-Temporal Graph Convolutional
Network (ST-GCN) architecture, demonstrated enhanced generalizability across
exercises and a decrease in overall complexity. Through extensive experiments
on three publicly available rehabilitation exercise assessment datasets, the
University of Idaho-Physical Rehabilitation Movement Data (UI-PRMD),
IntelliRehabDS (IRDS), and KInematic assessment of MOvement and clinical scores
for remote monitoring of physical REhabilitation (KIMORE), our method has shown
to surpass existing methods, setting a new benchmark in rehabilitation exercise
assessment accuracy.
