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 amputees 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.
|Original language||English (US)|
|Number of pages||13|
|Journal||IEEE Transactions on Biomedical Engineering|
|State||Published - Oct 1 2022|
Bibliographical notePublisher Copyright:
- Artificial intelligence
- Deep learning
- Prosthetic hand
- Real-time systems
- artificial intelligence
- deep learning
- information throughput
- information transfer rate
- motor decoding
- neural decoder
- peripheral nerve
- reaction time
PubMed: MeSH publication types
- Journal Article
- Research Support, N.I.H., Extramural
- Research Support, Non-U.S. Gov't