Image-based Stability Quantification

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 (lab-based). We propose a novel imagebased 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 against the ones generated directly from labbased sensory output (ground truth) using a publicly available multi-modality (mocap, foot pressure, 2-view videos), tensubject human motion dataset. Using leave-one-subject-out cross validation, our experimental results show: 1) our CoM estimation method (CoMNet) consistently outperforms state-of-theart inertial sensor-based CoM estimation techniques; 2) our image-based method combined with insole foot-pressure alone produces consistent 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 stability metric estimation produces consistent, positive, and statistically significant correlation on the two stability metrics (CoMtoCoP R=0.31 P<0.001, CoMtoBoS R=0.22 P<0.001). Our study provides promising quantitative evidence for stability computations and monitoring in natural environments.Index Terms-image-based, stability, base of support, center of mass, center of pressure, deep learning.