A Portable, Self-Contained Neuroprosthetic Hand with Deep Learning-Based Finger Control
Objective: Deep learning-based neural decoders have emerged as the prominent
approach to enable dexterous and intuitive control of neuroprosthetic hands.
Yet few studies have materialized the use of deep learning in clinical settings
due to its high computational requirements. Methods: Recent advancements of
edge computing devices bring the potential to alleviate this problem. Here we
present the implementation of a neuroprosthetic hand with embedded deep
learning-based control. The neural decoder is designed based on the recurrent
neural network (RNN) architecture and deployed on the NVIDIA Jetson Nano - a
compacted yet powerful edge computing platform for deep learning inference.
This enables the implementation of the neuroprosthetic hand as a portable and
self-contained unit with real-time control of individual finger movements.
Results: The proposed system is evaluated on a transradial amputee using
peripheral nerve signals (ENG) with implanted intrafascicular microelectrodes.
The experiment results demonstrate the system's capabilities of providing
robust, high-accuracy (95-99%) and low-latency (50-120 msec) control of
individual finger movements in various laboratory and real-world environments.
Conclusion: Modern edge computing platforms enable the effective use of deep
learning-based neural decoders for neuroprosthesis control as an autonomous
system. Significance: This work helps pioneer the deployment of deep neural
networks in clinical applications underlying a new class of wearable biomedical
devices with embedded artificial intelligence.
Anh Tuan Nguyen, Markus W. Drealan, Diu Khue Luu, Ming Jiang, Jian Xu, Jonathan Cheng, Qi Zhao, Edward W. Keefer, Zhi Yang