Related Experiment Video
Updated: Oct 27, 2026
A Human-machine-interface Integrating Low-cost Sensors with a Neuromuscular Electrical Stimulation System for Post-stroke Balance Rehabilitation
Published on: April 12, 2016
Point-of-Care Computer Vision for Postural Assessment in Brain Injury Rehabilitation
Elise Kirkby1, Helen Burke1, Stephanie Cline1
1Virginia C. Crawford Research Institute, Shepherd Center, 2020 Peachtree Street Atlanta, GA.
Objective:
To develop and preliminarily evaluate a clinician-led, computer vision (CV)-based system for point-of-care postural assessment in people with acquired brain injury (ABI) undergoing inpatient rehabilitation.
Design:
Cross-sectional and longitudinal observational study incorporating clinician-led co-innovation, usability testing, feasibility assessment, clinical utility analysis, and analytical validation against three-dimensional motion capture.
Setting:
Tertiary inpatient neurorehabilitation hospital.
Participants:
Rehabilitation clinicians (n=10), individuals with ABI (n=20; 60% female; age 45.0 ± 17.7 years), and neurologically intact controls (n=12).
Methods:
Occupational therapists, physical therapists, engineers, and rehabilitation scientists co-developed a markerless CV workflow for seated and standing postural assessment. Usability, feasibility, therapy-related change, analytical validity, and associations with clinician-reported measures were evaluated.
Results:
Clinicians reported excellent usability (System Usability Scale: 85.3 ± 12.3). All participants with ABI completed testing without adverse events, and all posture metrics were successfully acquired. During inpatient neurorehabilitation, seated cervical lateral flexion (β = -0.084°/day, 95% CI: -0.163 to -0.005, p = 0.037) and standing trunk lateral flexion (β = -0.096°/day, 95% CI: -0.152 to -0.040, p = 0.0008) decreased significantly over time, indicating improvements in postural alignment. CV metrics correlated with clinician-rated postural abnormalities, including unsupported sitting trunk displacement (ρ = 0.531, p = 0.023), pelvic displacement (ρ = 0.501, p = 0.034), and head/neck displacement (ρ = 0.482, p = 0.043). Agreement with motion capture was strong for cervical and trunk lateral flexion (ICC[A,1] = 0.83 for both).
Conclusions:
Clinician-led co-innovation produced a usable, feasible, and clinically meaningful CV tool that captured therapy-related postural change and aligned with clinician-reported impairments in ABI.
