Deep Learning and Human-machine Integration in Smart Prosthetic Control: Process and Perspectives

Abstract

With the rapid development of science and technology and the maturity of deep learning technology, intelligent prosthetic limb control has made significant progress in human-machine integration. While traditional prosthetic technology has seen improvements in form and function, it still faces challenges in user experience and physiological adaptability. Deep learning, with its powerful pattern recognition and decision-making capabilities, offers new possibilities to address these issues. This paper reviews the current status and future development of the application of deep learning in intelligent prosthetic limb control, emphasizing its close relationship with the concept of human-machine integration. Intelligent prosthetic control involves not only mechanical engineering and bioengineering, but also interdisciplinary integration of the fields of neural engineering, electrical engineering, motion control and computer science. This paper reviews the limitations in traditional prosthetic technology, such as accuracy and user adaptability, introduces the successful cases and latest research results of deep learning in the medical field, and explores the potential of deep reinforcement learning in optimizing complex movements and adapting to different environments. Finally, the future direction of deep learning technology in smart prosthetic limb control is envisioned, and research challenges and recommendations are presented in the hope of providing theoretical support for the innovative development of smart prosthetic limb technology and improving the quality of life of people with disabilities.