Chen Jong-Chen, Cheng Hao-Ming
Information Management Department, National Yunlin University of Science and Technology, Douliu 640, Taiwan.
Biomimetics (Basel). 2023 Aug 24;8(5):385. doi: 10.3390/biomimetics8050385.
Stroke patients cannot use their hands as freely as usual. However, recovery after a stroke is a long road for many patients. If artificial intelligence can assist human arm movement, it is believed that the possibility of stroke patients returning to normal hand movement can be significantly increased. In this study, the artificial neuromolecular system (ANM system) developed by our laboratory is used as the core motion control system to learn to control the mechanical arm, produce similar human rehabilitation actions, and assist patients in transiting between different activities. The strength of the ANM system lies in its ability to capture and process spatiotemporal information by exploiting the dynamic information processing inside neurons. Five experiments are conducted in this research: continuous learning, dimensionality reduction, moving problem domains, transfer learning, and fault tolerance. The results show that the ANM system can find out the arm movement trajectory when people perform different rehabilitation actions through the ability of continuous learning and reduce the activation of multiple muscle groups in stroke patients through the learning method of reducing dimensions. Finally, using the ANM system can reduce the learning time and performance required to switch between different actions through transfer learning.
中风患者无法像往常一样自如地使用双手。然而，对许多患者来说，中风后的恢复是一条漫长的道路。如果人工智能能够辅助人类手臂运动，相信中风患者恢复正常手部运动的可能性会显著增加。在本研究中，我们实验室开发的人工神经分子系统（ANM系统）被用作核心运动控制系统，以学习控制机械臂，产生类似人类的康复动作，并协助患者在不同活动之间过渡。ANM系统的优势在于它能够通过利用神经元内部的动态信息处理来捕获和处理时空信息。本研究进行了五个实验：持续学习、降维、移动问题域、迁移学习和容错。结果表明，ANM系统能够通过持续学习能力找出人们执行不同康复动作时的手臂运动轨迹，并通过降维学习方法减少中风患者多个肌肉群的激活。最后，使用ANM系统可以通过迁移学习减少在不同动作之间切换所需的学习时间和表现。
