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.

翻译：以运动为基础的康复训练计划已被证实能有效提升患者生活质量并降低死亡率及再入院率。人工智能驱动的虚拟康复系统允许患者在家独立完成训练，该系统利用AI算法分析运动数据，为患者提供实时反馈并将进展同步更新给临床医生。此类训练方案通常包含多种运动类型，这给康复动作评估数据集带来独特挑战：尽管整体训练样本充足，但针对每个单一运动类型的样本数量往往十分有限。这种样本分布不均导致现有方法难以在每类运动样本量不足的情况下训练出具有泛化能力的模型。针对这一问题，本文提出了一种创新的监督对比学习框架，通过引入硬负样本与软负样本策略，充分利用整个数据集训练适用于所有运动类型的单一模型。该模型采用时空图卷积网络（ST-GCN）架构，在提升跨运动泛化能力的同时降低了整体复杂度。通过在三个公开康复动作评估数据集（爱达荷大学物理康复运动数据集UI-PRMD、IntelliRehabDS（IRDS）及远程物理康复运动临床评分运动学评估数据集KIMORE）上的广泛实验，本方法在康复动作评估精度上超越了现有方法，树立了新的性能基准。