肩关节仿生运动传感服的设计和原型制作：Multi-DoF肩关节运动服的前身
Design and Prototyping of a Bio-inspired Kinematic Sensing Suit for the Shoulder Joint: Precursor to a Multi-DoF Shoulder Exosuit
摘要：
软可穿戴机器人是康复和主动辅助应用的一种有前途的新设计范例。它们的柔顺性使其成为肩部等复杂关节的理想选择，但这些机器人的直观控制需要强大且柔顺的传感机制。在这项工作中，我们介绍了多自由度肩外装能够感测肩关节的运动学的传感框架。所提出的基于肌腱的传感系统的灵感来自肌肉协同作用的概念，身体的本体感觉，并在负责肩部运动的肌肉组织中找到其基础。基于运动捕捉的评估开发的传感系统显示符合肌肉所表现出的行为，启发其路由和验证肌腱路由的假设扩展到驱动框架的机器护甲在未来。从多传感器空间到关节空间的映射是一个多变量的多元回归问题，并使用人工神经网络（ANN）导出。传感框架进行了测试与运动跟踪系统，并实现性能与均方根误差（RMSE）约为5.43度和3.65度的方位角和仰角关节角，分别测量超过29000帧（4+分钟）的运动捕捉数据。
英文摘要：
Soft wearable robots are a promising new design paradigm for rehabilitation and active assistance applications. Their compliant nature makes them ideal for complex joints like the shoulder, but intuitive control of these robots require robust and compliant sensing mechanisms. In this work, we introduce the sensing framework for a multi-DoF shoulder exosuit capable of sensing the kinematics of the shoulder joint. The proposed tendon-based sensing system is inspired by the concept of muscle synergies, the body's sense of proprioception, and finds its basis in the organization of the muscles responsible for shoulder movements. A motion-capture-based evaluation of the developed sensing system showed conformance to the behaviour exhibited by the muscles that inspired its routing and validates the hypothesis of the tendon-routing to be extended to the actuation framework of the exosuit in the future. The mapping from multi-sensor space to joint space is a multivariate multiple regression problem and was derived using an Artificial Neural Network (ANN). The sensing framework was tested with a motion-tracking system and achieved performance with root mean square error (RMSE) of approximately 5.43 degrees and 3.65 degrees for the azimuth and elevation joint angles, respectively, measured over 29000 frames (4+ minutes) of motion-capture data.
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