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关于使用RGB-D传感器进行肌肉骨骼健康监测的技术辅助方法的科学文献演变综述。

A review of the evolution of scientific literature on technology-assisted approaches using RGB-D sensors for musculoskeletal health monitoring.

作者信息

Mangal Naveen Kumar, Tiwari Anil Kumar

机构信息

Department of Electrical Engineering, IIT Jodhpur, Rajasthan, India.

Department of Electrical Engineering, IIT Jodhpur, Rajasthan, India.

出版信息

Comput Biol Med. 2021 May;132:104316. doi: 10.1016/j.compbiomed.2021.104316. Epub 2021 Mar 8.

Abstract

The human musculoskeletal (MSK) system (also known as the locomotor system) provides strength and assistance to perform functional tasks and daily life activities. The MSK health monitoring plays a vital role in maintaining the body mobility and quality of life. Manual approaches for musculoskeletal health monitoring are subjective and require a clinician's intervention. The evolution in motion tracking technology enables us to capture the fine details of body movements. The research community has proposed various approaches to help clinicians in diagnosis and monitor treatment sessions. This paper succinctly reviews the evolution of technology-assisted approaches for musculoskeletal health monitoring, using motion capture sensors. To streamline the search through the literature database, the PICOS framework and PRISMA method have been incorporated. The present study reviews methods to transform motion capture data into kinematics variables and factors that affect the tracking performance of RGB-D sensors. Furthermore, widely utilized time-series filters for skeletal data denoising and smoothing for kinematics analysis, stochastic models for movement modeling, rule-based and template-based approaches for rehabilitation exercises assessment, and telerehabilitation sessions for remote health monitoring are explored. This article analyzes skeletal tracking methods by providing advantages and drawbacks of the state of the art rehabilitation sessions assessment, skeletal joint kinematics analysis, and MSK Telerehabilitation approaches. It also discusses the possible future research avenues to improve musculoskeletal disorder diagnosis and treatment monitoring. Our review signifies that RGB-D sensor-based approaches are inexpensive and portable for disorder diagnosis and treatment monitoring. It can also be a viable option for clinicians to provide contactless healthcare access to patients in the current scenario of the COVID-19 pandemic.

摘要

人体肌肉骨骼(MSK)系统(也称为运动系统)为执行功能性任务和日常生活活动提供力量和辅助。MSK健康监测在维持身体活动能力和生活质量方面起着至关重要的作用。用于肌肉骨骼健康监测的手动方法具有主观性,需要临床医生的干预。运动跟踪技术的发展使我们能够捕捉身体运动的细微细节。研究界已经提出了各种方法来帮助临床医生进行诊断和监测治疗过程。本文简要回顾了使用运动捕捉传感器进行肌肉骨骼健康监测的技术辅助方法的发展历程。为了简化在文献数据库中的搜索,采用了PICOS框架和PRISMA方法。本研究回顾了将运动捕捉数据转换为运动学变量的方法以及影响RGB-D传感器跟踪性能的因素。此外,还探讨了广泛用于骨骼数据去噪和平滑以进行运动学分析的时间序列滤波器、用于运动建模的随机模型、用于康复锻炼评估的基于规则和基于模板的方法以及用于远程健康监测的远程康复治疗。本文通过分析现有康复治疗评估、骨骼关节运动学分析和MSK远程康复方法的优缺点,对骨骼跟踪方法进行了分析。它还讨论了未来可能改善肌肉骨骼疾病诊断和治疗监测的研究途径。我们的综述表明,基于RGB-D传感器的方法对于疾病诊断和治疗监测来说既便宜又便于携带。在当前新冠疫情的情况下,它也可以成为临床医生为患者提供非接触式医疗服务的可行选择。

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