Sardari Sara, Sharifzadeh Sara, Daneshkhah Alireza, Nakisa Bahareh, Loke Seng W, Palade Vasile, Duncan Michael J
Centre for Computational Science & Mathematical Modelling, Coventry University, Coventry, UK; School of Information Technology, Faculty of Science Engineering and Built Environment, Deakin University, Geelong, Vic, Australia.
Department of Computer Science, Swansea University, Swansea, UK.
Comput Biol Med. 2023 May;158:106835. doi: 10.1016/j.compbiomed.2023.106835. Epub 2023 Mar 31.
Performing prescribed physical exercises during home-based rehabilitation programs plays an important role in regaining muscle strength and improving balance for people with different physical disabilities. However, patients attending these programs are not able to assess their action performance in the absence of a medical expert. Recently, vision-based sensors have been deployed in the activity monitoring domain. They are capable of capturing accurate skeleton data. Furthermore, there have been significant advancements in Computer Vision (CV) and Deep Learning (DL) methodologies. These factors have promoted the solutions for designing automatic patient's activity monitoring models. Then, improving such systems' performance to assist patients and physiotherapists has attracted wide interest of the research community. This paper provides a comprehensive and up-to-date literature review on different stages of skeleton data acquisition processes for the aim of physio exercise monitoring. Then, the previously reported Artificial Intelligence (AI) - based methodologies for skeleton data analysis will be reviewed. In particular, feature learning from skeleton data, evaluation, and feedback generation for the purpose of rehabilitation monitoring will be studied. Furthermore, the associated challenges to these processes will be reviewed. Finally, the paper puts forward several suggestions for future research directions in this area.
在居家康复计划中进行规定的体育锻炼，对于不同身体残疾的人恢复肌肉力量和改善平衡起着重要作用。然而，参加这些计划的患者在没有医学专家的情况下无法评估自己的动作表现。最近，基于视觉的传感器已被应用于活动监测领域。它们能够捕捉准确的骨骼数据。此外，计算机视觉（CV）和深度学习（DL）方法也取得了重大进展。这些因素推动了自动患者活动监测模型设计方案的发展。那么，提高此类系统的性能以辅助患者和物理治疗师，已引起了研究界的广泛关注。本文针对物理治疗运动监测的目的，对骨骼数据采集过程的不同阶段进行了全面且最新的文献综述。然后，将回顾先前报道的基于人工智能（AI）的骨骼数据分析方法。特别地，将研究用于康复监测目的的从骨骼数据中进行特征学习、评估和反馈生成。此外，还将回顾这些过程所面临的相关挑战。最后，本文针对该领域未来的研究方向提出了若干建议。
