
NSF Org: |
CMMI Division of Civil, Mechanical, and Manufacturing Innovation |
Recipient: |
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Initial Amendment Date: | August 20, 2019 |
Latest Amendment Date: | August 20, 2019 |
Award Number: | 1925371 |
Award Instrument: | Standard Grant |
Program Manager: |
Alex Leonessa
CMMI Division of Civil, Mechanical, and Manufacturing Innovation ENG Directorate for Engineering |
Start Date: | September 1, 2019 |
End Date: | August 31, 2024 (Estimated) |
Total Intended Award Amount: | $600,987.00 |
Total Awarded Amount to Date: | $600,987.00 |
Funds Obligated to Date: |
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History of Investigator: |
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Recipient Sponsored Research Office: |
201 PRESIDENTS CIR SALT LAKE CITY UT US 84112-9049 (801)581-6903 |
Sponsor Congressional District: |
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Primary Place of Performance: |
1550 MEK (1495 E 100 S.), office Salt Lake City UT US 84112-0030 |
Primary Place of
Performance Congressional District: |
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Unique Entity Identifier (UEI): |
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Parent UEI: |
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NSF Program(s): | NRI-National Robotics Initiati |
Primary Program Source: |
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Program Reference Code(s): |
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Program Element Code(s): |
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Award Agency Code: | 4900 |
Fund Agency Code: | 4900 |
Assistance Listing Number(s): | 47.041 |
ABSTRACT
The research objective of this project is to enable volitional control over lower-limb prostheses through the integration of sonomyographic sensing - the ultrasound imaging of amputated (i.e., residual) limb muscle morphology - to control the Utah Lightweight Leg. This powered prosthetic leg is comprised of powered ankle and knee modules, and is roughly half the weight of contemporary technologies. The project team will use sonomyographic sensors in combination with mechanical sensors to infer the user's intent in anticipation of ambulation mode or joint motion, for example locomotor transitions from walking over level ground to ramps or stairs. The team will then perform human subject experiments comparing the ability of participants with transfemoral amputation to ambulate with and without various sonomyographic control algorithms enabled. If successful, the project will have positive impact on national health and welfare by improving the lives of individuals with amputation in terms of their independence and ambulation abilities, and by mitigating undesirable secondary effects of amputation such as a fear of falling and long-term joint health. Additional broader impacts of the work include enhanced undergraduate and graduate research experiences for veterans and underrepresented minorities, as well as outreach activities to K-12 students.
Robotic leg prostheses can overcome the limitations of conventional passive prostheses by generating net-positive energy during the gait cycle and actively regulating joint motion. However, scientific barriers must be overcome for robotic leg prosthesis to safely and effectively operate in real-world settings. The goal of this project is to fill the knowledge gap regarding the integration of the user's volition in the control of lightweight robotic ankle and knee prostheses. The research team will measure muscle contractions of the user's residual limb using wearable ultrasound probes. Specific objectives of this project are: 1) to identify optimal design guidelines to integrate sonomyographic sensing into state-of-the-art powered knee-ankle prostheses; 2) to determine specific algorithms that best anticipate the user's intention to perform different ambulation modes in a timely, accurate, and reliable manner; and 3) to understand how to optimally combine information gathered from sonomyography and mechanical sensors to control a robotic leg prosthesis within specific ambulation modes. Algorithms will be implemented on a lightweight robotic ankle and knee prosthesis to evaluate the hypothesis that providing users with anticipatory volitional control will lead to enhanced performance in complex and uncertain environments, thereby fostering seamless integration of robotic prostheses with human users.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
This project improved robotic prosthetic legs by allowing users to control them more naturally using signals from their own muscles and nerves. Our work focused on people with above-knee amputations, helping them move more smoothly and intuitively improving their mobilty and quality of life.
To achieve this, we created a new type of controller that blends the user’s own movement intentions with robotic assistance. This controller was tested using electromyography (EMG), a method that measures tiny electrical signals in muscles. Initially, the system allowed users to adjust the robotic leg’s support only when their foot was on the ground—such as when standing up from a chair or squatting. Over time, we improved the technology so users could also control movements while their leg was in the air, like during walking. Our research showed that this controller helped people with amputations climb stairs more naturally, even while carrying a backpack. It also allowed them to smoothly switch between activities like walking, stair climbing, and sitting down, providing better functionality than previous prosthetic controllers.
To further improve how well the system detects user intentions, we explored sonomyography, a technique that uses ultrasound (sound waves) to monitor muscle movements. We tested two types of ultrasound technology—A-mode and B-mode—and found that while B-mode provides more detailed images, but it is also more complex and expensive. Since our goal was to create a practical system that can be used in real life, we developed a lightweight, battery-powered A-mode ultrasound device that works in real-time. With this system, we collected extensive data from individuals with above-knee amputations as they performed tasks like walking and climbing stairs using passive prosthetic legs. Using this data, we trained artificial intelligence (AI) models to predict how the robotic knee and ankle should move based on the user’s muscle activity. Our results showed that a person with an above-knee amputation could successfully control the speed of a robotic knee just by using muscle signals detected through ultrasound—without needing additional sensors or external controls. This was the first successful demonstration of direct neural control of a robotic prosthesis using AI, paving the way for more natural and intuitive prosthetic limb technology.
This project also played a role in education and workforce development. It supported three PhD students and two master’s students, all U.S. citizens. Additionally, it provided research opportunities for undergraduate students through senior design projects and summer internships focused on prosthetics and robotics.
Last Modified: 03/11/2025
Modified by: Tommaso Lenzi
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