Physical exercise is an essential component of rehabilitation programs that improve quality of life and reduce mortality and re-hospitalization rates. In AI-driven virtual rehabilitation programs, patients complete their exercises independently at home, while AI algorithms analyze the exercise data to provide feedback to patients and report their progress to clinicians. To analyze exercise data, the first step is to segment it into consecutive repetitions. There has been a significant amount of research performed on segmenting and counting the repetitive activities of healthy individuals using raw video data, which raises concerns regarding privacy and is computationally intensive. Previous research on patients' rehabilitation exercise segmentation relied on data collected by multiple wearable sensors, which are difficult to use at home by rehabilitation patients. Compared to healthy individuals, segmenting and counting exercise repetitions in patients is more challenging because of the irregular repetition duration and the variation between repetitions. This paper presents a novel approach for segmenting and counting the repetitions of rehabilitation exercises performed by patients, based on their skeletal body joints. Skeletal body joints can be acquired through depth cameras or computer vision techniques applied to RGB videos of patients. Various sequential neural networks are designed to analyze the sequences of skeletal body joints and perform repetition segmentation and counting. Extensive experiments on three publicly available rehabilitation exercise datasets, KIMORE, UI-PRMD, and IntelliRehabDS, demonstrate the superiority of the proposed method compared to previous methods. The proposed method enables accurate exercise analysis while preserving privacy, facilitating the effective delivery of virtual rehabilitation programs.

翻译：体育锻炼是康复计划的关键组成部分，可提升生活质量并降低死亡率与再住院率。在人工智能驱动的虚拟康复计划中，患者居家自主完成训练动作，同时AI算法通过分析运动数据为患者提供反馈，并向临床医生报告其康复进展。运动数据分析的首要步骤是将其分割为连续重复动作。已有大量研究利用原始视频数据对健康个体的重复性动作进行分割与计数，但该方法存在隐私隐患且计算成本高昂。针对患者康复训练动作分割的既往研究多依赖多组可穿戴传感器采集的数据，而这类设备对居家康复患者而言操作困难。与健康个体相比，患者重复动作的不规则时长与动作间差异使得动作分割与计数更具挑战性。本文提出一种基于骨骼关节点的新型方法，用于分割与计数患者康复训练中的重复动作。骨骼关节点可通过深度相机或基于RGB患者视频的计算机视觉技术获取。我们设计了多种序列神经网络模型，对骨骼关节点序列进行分析以实现动作分割与计数。在KIMORE、UI-PRMD和IntelliRehabDS三个公开康复训练数据集上的大量实验表明，本方法相较于既往方法具有显著优越性。该方法在保障隐私的前提下实现精确动作分析，有效推动虚拟康复计划的落地实施。