Better control of below-elbow prosthetic hands using cloud tools and AI
Official title: Improved function performance in individuals with transradial limb difference using cloud based health system and deep learning
Funded at Rehabilitation Institute of Chicago D/b/a Shirley Ryan Abilitylab · NIH-11225165 · Data updated 2026-11-12
This project uses cloud-connected health tools and deep learning to help people with below-elbow amputations control myoelectric prosthetic hands more reliably.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Rehabilitation Institute of Chicago D/b/a Shirley Ryan Abilitylab NIH-funded |
| Lab location | 1 site (Chicago, United States) |
| Project start | September 20, 2014 |
| Project end | August 31, 2028 |
| Funding year | 2025 |
| Award (this year) | $1,978,028 |
| NIH project number | 2R01HD081525-06A1 |
| Project ID | NIH-11225165 on NIH RePORTER |
Who could benefit from this research
✓Could help
Adults with transradial (below-elbow) limb loss who use or plan to use myoelectric prostheses and can attend clinic testing in Chicago are ideal candidates.
✕Less likely to help
People with amputations above the elbow, those who use only body-powered or purely cosmetic prostheses, or those unable to wear EMG sensors are unlikely to benefit.
What this research studies
If you have a below-elbow (transradial) amputation, this project aims to improve how your prosthetic reads muscle signals and responds to your intended movements. Researchers will compare a standard pattern-recognition controller to one enhanced with deep learning and data augmentation that can compensate for signal loss from loose electrodes or broken leads. A cloud-based system will collect device data, support recalibration, and enable controller updates during clinic and real-world use. The goal is fewer dropouts, easier recalibration, and better everyday function so people keep using their prostheses.
Why it matters
Potential benefit: Could make myoelectric prostheses more reliable and easier to use, improving daily function and reducing device abandonment.
How similar studies have gone: Prior studies show machine-learning methods can improve EMG control in lab settings, but combining deep learning with cloud-based augmentation for real-world prosthesis reliability is relatively new.
Where this research is happening
-
Rehabilitation Institute of Chicago D/b/a Shirley Ryan Abilitylab
Chicago, United States Active
Researchers
- Principal investigator: Hargrove, Levi John — Rehabilitation Institute of Chicago D/b/a Shirley Ryan Abilitylab
- Study coordinator: Hargrove, Levi John
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About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page.