Can a Camera Measure Movement Like a Motion Lab?

Can a single camera, with no markers and no sensors on the body, measure movement as well as a motion lab? This episode looks at markerless motion capture in rehabilitation: how it works, how its accuracy is judged, and what three systematic reviews and two 2026 studies tested and found. We explain the terms as we go (pose estimation, colour vs depth cameras, concurrent validity, reliability, ICC, limits of agreement, sagittal plane, weighted kappa) and cover walking, shoulder range of motion, automated scoring of arm tasks after stroke, and how often recordings fail.

Key findings

• Gait (22 studies vs marker-based labs): walking speed, step time and step length showed good to excellent agreement (pooled ICC 0.81 to 0.98). Hip and knee angles in the sagittal plane were moderate to excellent; the ankle and rotations were poor. The validity studies included 402 healthy people and only 19 patients, all with Parkinson’s disease.
• Shoulder (14 studies, single depth camera): good to excellent validity for flexion and abduction, lower for rotations, and consistently poor for complex movements such as hair combing or wheelchair transfers. Reliability was inconsistent, and limits of agreement often went beyond 10 degrees.
• Scoring arm tasks after stroke (39 people with stroke, 19 healthy volunteers, 9 tasks, 1 depth camera): the best model matched occupational therapists’ ratings 83.3% of the time on average (75.0% to 91.7% by task), weighted kappa 0.67 to 0.88. It is a development study, and the authors state that external validation is needed before clinical use.
• Recording failures (colour camera, 7 physical performance tests, 15 healthy adults): 96.7% of recordings could be analysed under standard conditions (ICC 0.78 to 0.97), but only 79.6% under deliberately altered conditions.
• Most validation is in healthy adults; fine finger movement, severe impairment and change over time are poorly covered.

Sources

1. Lam WWT, Tang YM, Fong KNK. A systematic review of the applications of markerless motion capture (MMC) technology for clinical measurement in rehabilitation. J Neuroeng Rehabil 2023;20(1):57. DOI: 10.1186/s12984-023-01186-9
2. Scataglini S, Abts E, Van Bocxlaer C, Van den Bussche M, Meletani S, Truijen S. Accuracy, validity, and reliability of markerless camera-based 3D motion capture systems versus marker-based 3D motion capture systems in gait analysis: a systematic review and meta-analysis. Sensors (Basel) 2024;24(11):3686. DOI: 10.3390/s24113686
3. Lee U, Lee S, Kim SA, Kim Y, Lee S. Validity and reliability of single camera markerless motion capture systems with RGB-D sensors for measuring shoulder range-of-motion: a systematic review. Front Bioeng Biotechnol 2025;13:1570637. DOI: 10.3389/fbioe.2025.1570637
4. Fisher J, Bunn C, Li W, Hurden A, Barnhart M, Black R, Anderson C, Chen X. Development of a single-camera markerless motion capture system for assessing upper limb function post-stroke (FULMA). Neurorehabil Neural Repair 2026. DOI: 10.1177/15459683261493918
5. Yue X, Yu B. A reproducible workflow for validating two-dimensional color video-based markerless motion capture for automated physical performance scoring in adults. J Vis Exp 2026;(237). DOI: 10.3791/73504

Further reading

• Full written guide: Markerless motion capture: how a camera measures movement
• Previous episode: Do Exercise Apps That Watch You Move Actually Help?

This video is for educational purposes only and is not medical advice. Narration voice is AI-generated.