Category
Technology/equipment
Document Type
Paper
Abstract
This study presented a deep learning based markerless motion capture workflow and evaluated performance against marker-based motion capture during overground running. Multi-view high speed (200 Hz) image data were collected concurrently with marker-based motion capture (ground-truth data) permitting a direct comparison between methods. Lower limb kinematic data for six participants demonstrated high levels of agreement for lower limb joint angles with average RMSE ranging between 2.5° - 4.4° for hip sagittal and frontal plane motion, and 4.2° - 5.2° for knee and ankle motion. These differences generally fall within the known uncertainties of marker-based motion capture, suggesting that our markerless approach could be used for appropriate biomechanics applications. While there is a need for high quality open-access datasets to further facilitate performance improvements, markerless motion capture technology continues to improve; presenting exciting opportunities for biomechanics researchers and practitioners to capture large amounts of high quality, ecologically valid data both in and out of the laboratory setting.qual
Recommended Citation
Needham, Laurie
(2021)
"DEVELOPMENT AND EVALUATION OF A DEEP LEARNING BASED MARKERLESS MOTION CAPTURE SYSTEM,"
ISBS Proceedings Archive: Vol. 39:
Iss.
1, Article 32.
Available at:
https://commons.nmu.edu/isbs/vol39/iss1/32
