Back to Search
Start Over
Image-Based Stability Quantification
- Source :
- IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol 31, Pp 564-573 (2023)
- Publication Year :
- 2023
- Publisher :
- IEEE, 2023.
-
Abstract
- Quantitative evaluation of human stability using foot pressure/force measurement hardware and motion capture (mocap) technology is expensive, time consuming, and restricted to the laboratory. We propose a novel image-based method to estimate three key components for stability computation: Center of Mass (CoM), Base of Support (BoS), and Center of Pressure (CoP). Furthermore, we quantitatively validate our image-based methods for computing two classic stability measures, CoMtoCoP and CoMtoBoS distances, against values generated directly from laboratory-based sensor output (ground truth) using a publicly available, multi-modality (mocap, foot pressure, two-view videos), ten-subject human motion dataset. Using Leave One Subject Out (LOSO) cross-validation, experimental results show: 1) our image-based CoM estimation method (CoMNet) consistently outperforms state-of-the-art inertial sensor-based CoM estimation techniques; 2) stability computed by our image-based method combined with insole foot pressure sensor data produces consistent, strong, and statistically significant correlation with ground truth stability measures (CoMtoCoP r = 0.79 p < 0.001, CoMtoBoS r = 0.75 p < 0.001); 3) our fully image-based estimation of stability produces consistent, positive, and statistically significant correlation on the two stability metrics (CoMtoCoP r = 0.31 p < 0.001, CoMtoBoS r = 0.22 p < 0.043). Our study provides promising quantitative evidence for the feasibility of image-based stability evaluation in natural environments.
Details
- Language :
- English
- ISSN :
- 15580210
- Volume :
- 31
- Database :
- Directory of Open Access Journals
- Journal :
- IEEE Transactions on Neural Systems and Rehabilitation Engineering
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.2d08c6720ee34b3fb866a863764ac46f
- Document Type :
- article
- Full Text :
- https://doi.org/10.1109/TNSRE.2022.3226191