# HEALing LB3P: Profiling Biomechanical, Biological and Behavioral phenotypes

> **NIH NIH U19** · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2021 · $134,064

## Abstract

Project Summary
Identifying the optimal treatment for chronic low back pain (CLBP), the most prevalent of painful musculoskeletal
disorders, on a patient-specific basis is an important and unresolved challenge. Tailoring interventions according
to patient movement characteristics may improve clinical outcomes. Multi-modal studies are underway to
characterize CLBP patients and to provide insight into the phenotypes associated with the experience of CLBP
in relation to direct targeted and improved treatments. Comprehensive assessment of lumbar spine movement
CLBP patients may be an important facet of such treatments such that patient-specific spine biomechanics may
be included in predictive models to improve the ability to characterize them. To that end, the purpose of this
administrative supplement is to explore additional clinical tools for characterizing lumbopelvic kinematics during
functional tasks and daily activities. Specifically, this work aims to develop computer vision methods with which
to characterize motions of the lumbar spine, thereby providing an accurate and unobtrusive clinical tool that can
be used to supplement or replace currently considered tools such as wearable sensors, handheld sensors, and
complex marker-based motion capture systems.
This project will use video, marker-based 3D video motion capture data, and wearable inertial measurement data
to create and validate single-camera computer-vision algorithms that can be used to compute known clinical
metrics. During clinical assessments of patients in the Pitt LB3P Biomechanics Core study, participants are
asked to perform functional tasks (e.g., repeated flexion/extension, axial rotation, lateral bending, lifting, chair
rises) while wearing inertial measurement units (IMUs) attached to the upper back at T1, low back at L1 and L5,
and thigh. The functional performance exams are also recorded by video. The standard metrics determined from
these trials will include maximum and minimum lumbar spine and hip ROM, angular velocity at mid-excursion,
maximum rotation acceleration, and phase angles for lumbar and hip joint rotation. The goal of this supplemental
proposal is to use collected data to develop and train computer vision algorithms (so-called markerless motion
capture) to quantify the metrics of interest. Use of video is much simpler for clinicians as it avoids the setup
process required by wearable sensors. Recent developments in markerless motion capture have enabled simple
camera systems to provide quantitative information about human motions but few studies have assessed
computer vision for use in the clinical setting. The research plan in this proposal involves the use of existing
data to train and validate computer vision algorithms, and then to further investigate their use in the clinical
setting with single camera video to determine the extent to which video-based markerless motion capture may
be clinically useful in the assessment of CLBP patients.

## Key facts

- **NIH application ID:** 10415626
- **Project number:** 3U19AR076725-01S2
- **Recipient organization:** UNIVERSITY OF PITTSBURGH AT PITTSBURGH
- **Principal Investigator:** Gwendolyn A Sowa
- **Activity code:** U19 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $134,064
- **Award type:** 3
- **Project period:** 2021-09-01 → 2024-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10415626

## Citation

> US National Institutes of Health, RePORTER application 10415626, HEALing LB3P: Profiling Biomechanical, Biological and Behavioral phenotypes (3U19AR076725-01S2). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10415626. Licensed CC0.

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