Concurrent assessment of gait kinematics using marker-based and markerless motion capture
By Robert M. Kanko, E. Laende, E. Davis, W. Selbie, K. Deluzio. bioRxiv, 2020
342 citations recorded by the source corpus.
Kinematic analysis is a useful and widespread tool used in research and clinical biomechanics for the estimation of human pose and the quantification of human movement. Common marker-based optical motion capture systems are expensive, time intensive, and require highly trained operators to obtain kinematic data. Markerless motion capture systems offer an alternative method for the measurement of kinematic data with several practical benefits. This work compared the kinematics of human gait measured using a deep learning algorithm-based markerless motion capture system to those of a common marker-based motion capture system. Thirty healthy adult participants walked on a treadmill while data were simultaneously recorded using eight video cameras (markerless) and seven infrared optical motion capture cameras (marker-based). Video data were processed using markerless motion capture software, marker-based data were processed using marker-based capture software, and both sets of data were compared. The average root mean square distance (RMSD) between corresponding joints was less than 2.5 cm for all joints except the hip, which was 3.6 cm. Lower limb segment angles indicated pose estimates from both systems were very similar, with RMSD of less than 5.5° for all segment angles except those that represent rotations about the long axis of the segment. Lower limb joint angles captured similar patterns for flexion/extension at all joints, ab/adduction at the knee and hip, and toe-in/toe-out at the ankle. These findings demonstrate markerless motion capture can measure similar 3D kinematics to those from marker-based systems.
Subfields: Physical Therapy, Sports Therapy and Rehabilitation, Psychiatry and Mental health, Rehabilitation
View Paper: https://www.semanticscholar.org/paper/5fa7b110fac92e56c8576f137c629c242f8eb7d0
Citations in the corpus
- OpenCap: Human movement dynamics from smartphone videos (2023, neutral): “…is similar to errors reported for inertial measurement unit–based approaches (RMSE: 2.0–12˚ for walking, running, and daily living activities [18,51–57]) and commercial and academic video-based systems with eight cameras (RMSE: 2.6–11˚ for walking, running, and cycling activities [31,58]).”