# Improving hand kinematic predictions from implanted EMG in humans and monkeys

> **NIH NIH R01** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2024 · $577,880

## Abstract

Project Summary
There are millions of people worldwide with debilitating upper limb amputations. While electrical signals from
residual muscle can provide some function, every amputee is missing muscles, and therefore missing a variety
of important functions. Our group has demonstrated a novel method for obtaining signals from independent nerve
fascicles in humans, which we call the Regenerative Peripheral Nerve Interface (RPNI). The small muscle grafts
degenerate, regenerate, revascularize, and reinnervate utilizing natural biologic processes. They also introduce
a degree of conformity among prosthetic users, for example always having thumb muscles available for
electromyography (EMG). Our long-term goal is to achieve able bodied performance for prosthetic hand
movement. The objective of our current application, which represents the next step, is to develop reusable deep
learning architectures for controlling wrist and finger movements. We will achieve this with the following specific
aims. In Aim 1 we will utilize a range of deep learning techniques we developed for brain machine interfaces to
use with implantable EMG signals for truly continuous control of finger movement. This will be done in monkeys
and humans with similar implanted electrodes. In Aim 2 we will achieve simultaneous control of the wrist and
fingers by learning to segregate stabilization related EMG from wrist movement related EMG, again in both
humans and monkeys. Finally, in Aim 3, in humans, we will quantify the biomechanical efficiencies gained from
using our novel prosthetic decoders testing the likely clinical impact of this approach. We believe that the
demonstration of higher performance across the board will motivate widespread use of RPNI and implantable
EMG for prosthetic control after upper limb amputation.

## Key facts

- **NIH application ID:** 10979725
- **Project number:** 1R01NS134834-01A1
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** Cynthia Anne Chestek
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $577,880
- **Award type:** 1
- **Project period:** 2024-09-17 → 2029-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10979725

## Citation

> US National Institutes of Health, RePORTER application 10979725, Improving hand kinematic predictions from implanted EMG in humans and monkeys (1R01NS134834-01A1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10979725. Licensed CC0.

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