Ahmed Muhammad Hannan, Kutsuzawa Kyo, Hayashibe Mitsuhiro
Department of Robotics, Graduate School of Engineering, Tohoku University, Sendai 980-8579, Japan.
Biomimetics (Basel). 2023 Aug 15;8(4):367. doi: 10.3390/biomimetics8040367.
The lack of intuitive controllability remains a primary challenge in enabling transhumeral amputees to control a prosthesis for arm reaching with residual limb kinematics. Recent advancements in prosthetic arm control have focused on leveraging the predictive capabilities of artificial neural networks (ANNs) to automate elbow joint motion and wrist pronation-supination during target reaching tasks. However, large quantities of human motion data collected from different subjects for various activities of daily living (ADL) tasks are required to train these ANNs. For example, the reaching motion can be altered when the height of the desk is changed; however, it is cumbersome to conduct human experiments for all conditions. This paper proposes a framework for cloning motion datasets using deep reinforcement learning (DRL) to cater to training data requirements. DRL algorithms have been demonstrated to create human-like synergistic motion in humanoid agents to handle redundancy and optimize movements. In our study, we collected real motion data from six individuals performing multi-directional arm reaching tasks in the horizontal plane. We generated synthetic motion data that mimicked similar arm reaching tasks by utilizing a physics simulation and DRL-based arm manipulation. We then trained a CNN-LSTM network with different configurations of training motion data, including DRL, real, and hybrid datasets, to test the efficacy of the cloned motion data. The results of our evaluation showcase the effectiveness of the cloned motion data in training the ANN to predict natural elbow motion accurately across multiple subjects. Furthermore, motion data augmentation through combining real and cloned motion datasets has demonstrated the enhanced robustness of the ANN by supplementing and diversifying the limited training data. These findings have significant implications for creating synthetic dataset resources for various arm movements and fostering strategies for automatized prosthetic elbow motion.
对于经肱骨截肢者而言，要运用残肢运动学来控制假肢完成手臂伸展动作，缺乏直观的可控性仍是一项主要挑战。假肢手臂控制方面的最新进展主要集中在利用人工神经网络（ANN）的预测能力，以便在目标抓取任务中实现肘关节运动和手腕旋前 - 旋后动作的自动化。然而，为了训练这些人工神经网络，需要从不同受试者身上收集大量用于各种日常生活（ADL）任务的人体运动数据。例如，当桌子高度改变时，伸手动作可能会发生变化；然而，针对所有情况进行人体实验非常繁琐。本文提出了一个使用深度强化学习（DRL）来克隆运动数据集的框架，以满足训练数据的需求。深度强化学习算法已被证明能够在类人智能体中创建类似人类的协同运动，以处理冗余并优化动作。在我们的研究中，我们收集了六名个体在水平面内执行多方向手臂伸展任务的真实运动数据。我们利用物理模拟和基于深度强化学习的手臂操纵生成了模拟类似手臂伸展任务的合成运动数据。然后，我们使用包括深度强化学习、真实和混合数据集在内的不同训练运动数据配置来训练一个卷积神经网络 - 长短期记忆网络（CNN - LSTM），以测试克隆运动数据的有效性。我们的评估结果表明，克隆运动数据在训练人工神经网络以准确预测多个受试者的自然肘关节运动方面是有效的。此外，通过结合真实和克隆运动数据集进行运动数据增强，已证明通过补充和多样化有限的训练数据，提高了人工神经网络的鲁棒性。这些发现对于创建各种手臂运动的合成数据集资源以及促进假肢肘关节自动运动策略具有重要意义。
