人-假肢界面运动协同的个性化在线自适应
Personalized On-line Adaptation of Kinematic Synergies for Human-Prosthesis Interfaces
摘要：
在假肢应用中已经采用协同作用来降低设计的复杂性，但通常涉及针对群体的单一协同作用设置，而忽略了个体偏好或适应能力。然而，个性化的协同设置是必要的假体装置的有效操作。两个主要的挑战阻碍了人体-假体界面协同作用的个性化。第一个与人类运动适应过程有关，第二个与个体运动学习动力学的变化有关。在本文中，提出了一个系统的个性化的人体假肢接口使用在线测量从每个人的运动协同作用。使用上肢伸手的任务由目标函数描述，界面由运动协同作用参数化。因此，针对给定个体的界面的个性化可以被公式化为找到最佳个性化参数。提出了一种结构来模拟所观察到的运动行为，允许运动偏好和运动学习的个性化特征，并随后用于在线优化方案，以确定个人的协同作用。包含在模型中的共同特征的知识，使在线适应的人体假肢接口同时发生的人体运动适应，而不需要重新调整个性化算法为每个人。在虚拟现实环境中模拟截肢和假肢使用的健全受试者的人在回路实验结果表明，所提出的个性化算法是有效的，在获得最佳的协同作用与快速统一的收敛速度在一组个人。
英文摘要：
Synergies have been adopted in prosthetic limb applications to reduce complexity of design, but typically involve a single synergy setting for a population and ignore individual preference or adaptation capacity. However, personalization of the synergy setting is necessary for the effective operation of the prosthetic device. Two major challenges hinder the personalization of synergies in human-prosthesis interfaces. The first is related to the process of human motor adaptation and the second to the variation in motor learning dynamics of individuals. In this paper, a systematic personalization of kinematic synergies for human-prosthesis interfaces using on-line measurements from each individual is proposed. The task of reaching using the upper-limb is described by an objective function and the interface is parameterized by a kinematic synergy. Consequently, personalizing the interface for a given individual can be formulated as finding an optimal personalized parameter. A structure to model the observed motor behavior that allows for the personalized traits of motor preference and motor learning is proposed, and subsequently used in an on-line optimization scheme to identify the synergies for an individual. The knowledge of the common features contained in the model enables on-line adaptation of the human-prosthesis interface to happen concurrently to human motor adaptation without the need to re-tune the personalization algorithm for each individual. Human-in-the-loop experimental results with able-bodied subjects, performed in a virtual reality environment to emulate amputation and prosthesis use, show that the proposed personalization algorithm was effective in obtaining optimal synergies with a fast uniform convergence speed across a group of individuals.
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