Artificial Intelligence Enables Real-Time and Intuitive Control of Prostheses via Nerve Interface
This addresses the need for more dexterous and intuitive prosthetic control for amputees, representing a strong specific gain rather than a broad paradigm shift.
The study tackled the problem of enabling intuitive, real-time control of prosthetic hands by developing an AI agent that decodes movement intent from nerve data, achieving 97-98% accuracy for individual finger and wrist movements in amputees and maintaining robust performance over 16 months.
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.