# Automatic mode-dependent and phase-varying prosthetic foot  stiffness modulation to improve balance control in individuals with lower-limb amputations

> **NIH NIH R01** · UNIVERSITY OF TEXAS AT AUSTIN · 2024 · $445,170

## Abstract

Project Summary
Individuals with lower limb amputations are at higher risk of falling compared to able-bodied and other clinical
populations and are more likely to sustain life-altering injuries. The higher fall risk is primarily due to the loss of
the muscles crossing the ankle, which are critical to maintaining balance control. Prosthetic devices are designed
to provide appropriate stiffness for needed stability and support. While research has shown the optimal stiffness
to maintain balance varies across ambulatory activities (e.g., straight walking versus turning), most clinically
prescribed prosthetic devices are passive and only provide a fixed stiffness level. The one commercially
available, powered prosthetic ankle-foot has not been shown to restore balance control. Thus, a prosthetic device
that actively adjusts ankle stiffness across different ambulatory activities is critically needed to advance the field
and improve balance control for those with lower-limb amputations. The goal of this project is to determine if
automatic stiffness modulation can improve the balance control of individuals with lower limb amputation as they
perform typical ambulatory activities of daily living. By matching the ankle stiffness to the task requirements, we
believe we will significantly improve balance control and decrease fall risk for those with lower-limb amputations.
In the proposed work, we will utilize an open source, lightweight, state-of-the-art hardware system (Open-Source
Ankle) that includes novel hardware, actuation, sensing, computation, and control software and pursue three
specific aims. In Aim 1, we will perform a human subject experiment to determine the influence of prosthetic
ankle stiffness on balance control during a wide range of ambulatory activities that will provide the basis for our
activity detection and stiffness modulation algorithms. In Aim 2, we will implement our activity detection and
phase-varying stiffness modulation algorithms into the Open-Source Ankle. We will use machine learning
techniques to predict different ambulatory activities and validate the ability of the Open-Source Ankle, fit with a
commonly prescribed low-profile prosthetic foot, to modulate the stiffness profile throughout the stance phase of
the different ambulatory activities. The outcome of this aim will be a semi-active prosthetic ankle-foot system
with activity-dependent, phase-varying, and user-specific mechanical stiffness profiles. In Aim 3, we will perform
a second human subject experiment to determine if automatic stiffness modulation improves balance control in
real-world environments. The outcomes of this research will provide insight into the relationships between
stiffness and balance and if a semi-active prosthetic system with automatic activity-dependent, phase-varying,
and user-specific stiffness modulation improves balance control for those with below-knee amputations. This
addresses a critical need in service member, veteran, and civil...

## Key facts

- **NIH application ID:** 10882682
- **Project number:** 1R01HD111514-01A1
- **Recipient organization:** UNIVERSITY OF TEXAS AT AUSTIN
- **Principal Investigator:** RICHARD R NEPTUNE
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $445,170
- **Award type:** 1
- **Project period:** 2024-08-01 → 2028-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10882682

## Citation

> US National Institutes of Health, RePORTER application 10882682, Automatic mode-dependent and phase-varying prosthetic foot  stiffness modulation to improve balance control in individuals with lower-limb amputations (1R01HD111514-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10882682. Licensed CC0.

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