# Machine-learning based control of functional electrical stimulation

> **NIH NIH R01** · UNIVERSITY OF ARIZONA · 2020 · $301,922

## Abstract

PROJECT SUMMARY/ABSTRACT
Functional electrical stimulation involves artificial activation of paralyzed muscles with implanted electrodes
and has been used successfully to improve the ability of tetraplegics to perform movements important for daily
activities. The range of motor behaviors that can be generated by functional electrical stimulation, however, is
limited to a relatively small set of preprogrammed movements such as hand grasp and release. A broader
range of movements has not been implemented because of the substantial challenge associated with
identifying the patterns of muscle stimulation needed to elicit specified movements. To address this limitation,
we have developed machine-learning based algorithms that can predict patterns of muscle activity associated
with a wide range of complex limb movements. In addition, we have devised a method whereby predicted
patterns of muscle activity can then be transformed into stimulus pulse patterns needed to evoke movements
in paralyzed limbs. Our goal for this project is to determine whether these approaches, when applied to
temporarily paralyzed non-human primates, can be used to produce: 1) a wide range of movements of the
hand throughout peri-personal reach space, and 2) configuration of the hand and fingers into a variety of
shapes needed to interact with diverse objects in the environment. If successful, this approach would greatly
expand the repertoire of motor behaviors available to individuals paralyzed because of spinal cord injury or
stroke. Furthermore, this system ultimately might serve as the requisite interface between brain-derived
trajectory information and functional electrical stimulation systems needed to realize a self-contained and self-
controlled upper limb neuroprosthetic.

## Key facts

- **NIH application ID:** 9878937
- **Project number:** 5R01NS102259-03
- **Recipient organization:** UNIVERSITY OF ARIZONA
- **Principal Investigator:** ANDREW J FUGLEVAND
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $301,922
- **Award type:** 5
- **Project period:** 2018-06-01 → 2022-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9878937

## Citation

> US National Institutes of Health, RePORTER application 9878937, Machine-learning based control of functional electrical stimulation (5R01NS102259-03). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/9878937. Licensed CC0.

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