Recent advances in data analytics and computer-aided diagnostics stimulate the vision of patient-centric precision healthcare, where treatment plans are customized based on the health records and needs of every patient. In physical rehabilitation, the progress in machine learning and the advent of affordable and reliable motion capture sensors have been conducive to the development of approaches for automated assessment of patient performance and progress toward functional recovery. The presented study reviews computational approaches for evaluating patient performance in rehabilitation programs using motion capture systems. Such approaches will play an important role in supplementing traditional rehabilitation assessment performed by trained clinicians, and in assisting patients participating in home-based rehabilitation. The reviewed computational methods for exercise evaluation are grouped into three main categories: discrete movement score, rule-based, and template-based approaches. The review places an emphasis on the application of machine learning methods for movement evaluation in rehabilitation. Related work in the literature on data representation, feature engineering, movement segmentation, and scoring functions is presented. The study also reviews existing sensors for capturing rehabilitation movements and provides an informative listing of pertinent benchmark datasets. The significance of this paper is in being the first to provide a comprehensive review of computational methods for evaluation of patient performance in rehabilitation programs.
Vogtareuth Rehab Depth Datasets: Benchmark for Marker-less Posture Estimation in Rehabilitation
Technical University of Munich（慕尼黑工业大学）；reFit Systems（reFit Systems公司）；Ludwig Maximilian University of Munich（慕尼黑路德维希-马克西米利安大学）；Schön Klinik Vogtareuth（福格塔罗伊特舍恩医院）
GAITEX: Human motion dataset of impaired gait and rehabilitation exercises using inertial and optical sensors
AI for Sensor Data Analytics Research Group（人工智能传感器数据解析研究组）；Ulm University of Applied Sciences（乌尔姆应用科学大学）；Biomechatronic Research Group（生物机械研究组）；Institute of Computer Science（计算机科学研究所）
HRTR: A Single-stage Transformer for Fine-grained Sub-second Action Segmentation in Stroke Rehabilitation
Department of Electrical and Computer Engineering, University of Kentucky（电气与计算机工程系，肯塔基大学）；College of Medicine, University of Kentucky（医学学院，肯塔基大学）；Department of Electrical and Computer Engineering, University of California, Davis（电气与计算机工程系，加州大学戴维斯分校）
提出单阶段 High Resolution Temporal Transformer（HRTR），用于中风康复中细粒度亚秒级动作的时间定位与分类，无需多阶段流程或后处理，并在多个数据集上取得最优 Edit Score。
Rehabilitation Exercise Quality Assessment and Feedback Generation Using Large Language Models with Prompt Engineering
KITE Research Institute（KITE研究 institute）；Toronto Rehabilitation Institute（多伦多康复研究所）；University Health Network（大学健康网络）；Faculty of Applied Science and Engineering（应用科学与工程学院）；University of Toronto（多伦多大学）；College of Engineering and Technology（工程与技术学院）；American University of the Middle East（中东美国大学）
Towards nation-wide analytical healthcare infrastructures: A privacy-preserving augmented knee rehabilitation case study
AUT University（奥克兰理工大学）；Institute of Biomedical Technologies (IBTec)（生物医学技术研究所（IBTec））；Sports Performance Research Institute New Zealand (SPRINZ)（新西兰运动表现研究所（SPRINZ））；Ovidius University of Constanta（康斯坦察奥维迪乌斯大学）
本文提出一种隐私保护的膝关节康复分析方案，利用 Google MediaPipe 姿态估计将视频转为时间序列数据，并通过自适应算法以91.67%-100%准确率识别运动，为全国性可解释医疗平台提供开源基础。
RePose: A Real-Time 3D Human Pose Estimation and Biomechanical Analysis Framework for Rehabilitation
Zhejiang Lab（浙江实验室）；Northeastern University（东北大学）；Xi’an Jiaotong University（西安交通大学）；Zhengzhou University（郑州大学）；Dalian Minzu University（大连民族大学）；Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；Fuyao University of Science and Technology（福耀科技大学）；Xianghu Lab（翔虎实验室）
Video-Based Markerless Motion Capture for Clinical and Rehabilitation Biomechanics: A PRISMA-ScR Scoping Review of Validated Architectures, Clinical Readiness, and Emerging Methods
Université Côte d'Azur（蔚蓝海岸大学）；Centre Hospitalier Universitaire de Nice（尼斯大学附属医院）；Institut Universitaire de France（法国大学研究院）
