Exercise-based rehabilitation programs have been shown to enhance quality of life and reduce mortality and rehospitalizations. AI-driven virtual rehabilitation programs enable patients to complete exercises independently at home while AI algorithms can analyze exercise data to provide feedback to patients and report their progress to clinicians. This paper introduces a novel approach to assessing the quality of rehabilitation exercises using RGB video. Sequences of skeletal body joints are extracted from consecutive RGB video frames and analyzed by many-to-one sequential neural networks to evaluate exercise quality. Existing datasets for exercise rehabilitation lack adequate samples for training deep sequential neural networks to generalize effectively. A cross-modal data augmentation approach is proposed to resolve this problem. Visual augmentation techniques are applied to video data, and body joints extracted from the resulting augmented videos are used for training sequential neural networks. Extensive experiments conducted on the KInematic assessment of MOvement and clinical scores for remote monitoring of physical REhabilitation (KIMORE) dataset, demonstrate the superiority of the proposed method over previous baseline approaches. The ablation study highlights a significant enhancement in exercise quality assessment following cross-modal augmentation.

翻译：基于运动的康复计划已被证明能改善生活质量、降低死亡率和再住院率。AI驱动的虚拟康复计划使患者能够在家独立完成康复运动，同时AI算法可分析运动数据并向患者提供反馈，向临床医生报告其进展。本文提出了一种利用RGB视频评估康复运动质量的新方法。从连续RGB视频帧中提取人体骨骼关节序列，并通过多对一序列神经网络进行分析以评估运动质量。现有康复运动数据集缺乏足够样本，难以有效训练深度序列神经网络以达到良好泛化能力。为解决此问题，提出了一种跨模态数据增强方法：对视频数据应用视觉增强技术，并将增强后视频中提取的人体关节用于训练序列神经网络。在KInematic assessment of MOvement and clinical scores for remote monitoring of physical REhabilitation（KIMORE）数据集上进行的大量实验表明，所提方法优于以往基线方法。消融研究进一步凸显了跨模态增强后运动质量评估的显著提升。