Video and movement system to monitor low back pain
Official title: Efficient and Cost-Effective Multimodal System for Pain Management in Low Back Pain
Funded at Carnegie-Mellon University · NIH-11135600 · Data updated 2026-11-12
This project uses camera and movement data to detect and track pain in people with acute or chronic low back pain.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Carnegie-Mellon University NIH-funded |
| Lab location | 1 site (Pittsburgh, United States) |
| Project start | March 16, 2020 |
| Project end | June 30, 2026 |
| Funding year | 2025 |
| Award (this year) | $449,960 |
| NIH project number | 5R01NR018451-05 |
| Project ID | NIH-11135600 on NIH RePORTER |
Who could benefit from this research
✓Could help
Adults with acute or chronic low back pain who can perform extension, flexion, and rotation movements and can attend an initial clinic visit plus three follow-ups are the ideal candidates.
✕Less likely to help
People without low back pain, those unable to safely perform the movement tests, or those with conditions that block accurate face/body tracking (for example severe facial paralysis) are unlikely to benefit.
What this research studies
You will have high-definition video taken of your face, head, and body while you do bending, twisting, and stretching movements during a clinic visit and three follow-ups. The team will use automated tracking to measure how you move and feed those signals into deep-learning computer models that learn when pain happens and how strong it is. They will train and test the system on five different types of low back pain to make the measurements work across common causes. The goal is to create reliable, repeatable pain signals from video that could help guide care.
Why it matters
Potential benefit: If successful, this could give clinicians objective, continuous measures of low back pain to help guide treatment decisions and track improvement.
How similar studies have gone: Previous small studies using facial expression and movement to detect pain have shown promise, but applying end-to-end deep learning across multiple low-back pain types is relatively new.
Where this research is happening
-
Carnegie-Mellon University
Pittsburgh, United States Active
Researchers
- Principal investigator: Hammal, Zakia — Carnegie-Mellon University
- Study coordinator: Hammal, Zakia
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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.