1. Artificial Intelligence Enables Real-Time and Intuitive Control of Prostheses via Nerve Interface
- Author
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Luu, Diu Khue, Nguyen, Anh Tuan, Jiang, Ming, Drealan, Markus W., Xu, Jian, Wu, Tong, Tam, Wing-kin, Zhao, Wenfeng, Lim, Brian Z. H., Overstreet, Cynthia K., Zhao, Qi, Cheng, Jonathan, Keefer, Edward W., and Yang, Zhi
- Subjects
Computer Science - Robotics ,Computer Science - Artificial Intelligence ,Computer Science - Human-Computer Interaction ,Computer Science - Machine Learning ,Quantitative Biology - Neurons and Cognition - Abstract
Objective: The next generation prosthetic hand that moves and feels like a real hand requires a robust neural interconnection between the human minds and machines. Methods: Here we present a neuroprosthetic system to demonstrate that principle by employing an artificial intelligence (AI) agent to translate the amputee's movement intent through a peripheral nerve interface. The AI agent is designed based on the recurrent neural network (RNN) and could simultaneously decode six degree-of-freedom (DOF) from multichannel nerve data in real-time. The decoder's performance is characterized in motor decoding experiments with three human amputees. Results: First, we show the AI agent enables amputees to intuitively control a prosthetic hand with individual finger and wrist movements up to 97-98% accuracy. Second, we demonstrate the AI agent's real-time performance by measuring the reaction time and information throughput in a hand gesture matching task. Third, we investigate the AI agent's long-term uses and show the decoder's robust predictive performance over a 16-month implant duration. Conclusion & significance: Our study demonstrates the potential of AI-enabled nerve technology, underling the next generation of dexterous and intuitive prosthetic hands.
- Published
- 2022