A Portable, Self-Contained Neuroprosthetic Hand with Deep Learning-Based Finger Control

TL;DR

This paper presents a portable neuroprosthetic hand with embedded deep learning control using an RNN on an edge device, enabling real-time, high-accuracy finger movement control for amputees in clinical and real-world settings.

Contribution

It demonstrates the deployment of a deep learning neural decoder on an edge platform for a self-contained, portable neuroprosthetic hand with real-time control capabilities.

Findings

Achieved 95-99% control accuracy

Low latency of 50-120 milliseconds

Validated on a transradial amputee in real-world environments

Abstract

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.