A New Approach for Medical Assessment of Patient’s Injured Shoulder

A. Vitali, D. Regazzoni, C. Rizzi, F. Maffioletti
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引用次数: 4

Abstract

Low cost marker-less motion capture (Mocap) systems can be considered an interesting technology for the objective assessment of rehabilitation processes. In particular, this paper presents a feasibility study to introduce a Mocap system as a tool to assess shoulder rehabilitation. The movements of a shoulder are complex and challenging to be captured with a marker-less system because the skeleton avatar usually oversimplifies shoulder articulation with a single virtual joint. The designed solution integrates a low-cost Mocap system with image processing techniques and convolutional neural networks to automatically detect and measure potential compensatory movements executed during an abduction, which is one of the first post-surgery exercises for shoulder rehabilitation. First, we introduce the main steps of a reference roadmap that guided the development of the Mocap solution for rehab assessment of injured shoulder. Then, the acquisition of medical knowledge is presented as well as the new Mocap solution based on the integration of convolutional neural networks and 2D motion tracking techniques. Finally, the application which automatically evaluates abductions and makes available the measurements of the scapular elevations is described. Preliminary study and future works are also presented and discussed.
肩部损伤医学评估的新方法
低成本无标记运动捕捉(Mocap)系统可以被认为是一种有趣的技术,用于客观评估康复过程。特别地,本文提出了一项可行性研究,将动作捕捉系统作为评估肩部康复的工具。肩膀的运动是复杂的,具有挑战性的捕捉与无标记系统,因为骨骼化身通常过度简化肩关节与单个虚拟关节。设计的解决方案将低成本的动作捕捉系统与图像处理技术和卷积神经网络集成在一起,自动检测和测量外展过程中执行的潜在代偿运动,这是肩膀康复的第一个术后练习之一。首先,我们介绍了参考路线图的主要步骤,该路线图指导了用于受伤肩膀康复评估的动作捕捉解决方案的开发。然后,介绍了医学知识的获取以及基于卷积神经网络和二维运动跟踪技术相结合的新的动作捕捉解决方案。最后,描述了自动评估外展并提供肩胛骨高度测量的应用。并对前期的研究和今后的工作进行了展望和讨论。
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