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