Quantifying and Characterizing Step Counting Uncertainty in Wearable Accelerometer Data

Abstract: Traditional step counting accuracy metrics often fail to capture the critical aspects of measurement uncertainty and reliability, which are paramount for dependable health monitoring in free-living environments. This paper introduces a novel framework to explicitly quantify and characterize step counting uncertainty across diverse wearable accelerometer configurations, addressing the crucial trade-offs between data acquisition resources and measurement dependability. We developed a probabilistic 1D Convolutional Neural Network (CNN) that outputs the rate parameter of a Poisson distribution, allowing direct estimation of prediction confidence. The model was rigorously evaluated using Leave-One-Subject-Out cross-validation on a dataset of 39 participants, analyzing triaxial accelerometer data from hip and wrist placements at 100Hz and 25Hz sampling frequencies. Performance was assessed using Mean Absolute Error, Mean Absolute Percentage Error, bias, and by characterizing error types (false positives and false negatives), alongside the width of the 95\% prediction confidence interval as our primary uncertainty metric. Our results demonstrate that hip-worn sensors at 100Hz provided the most accurate and least uncertain step counts, exhibiting the lowest mean absolute error (155 steps) and prediction confidence interval width (136 steps). Statistical analyses revealed that wrist-worn sensors produced significantly more false positives and false negatives (p < 0.002) compared to hip sensors, and reducing sampling frequency to 25Hz significantly increased false positives for wrist data (p=0.0007) while hip-worn sensors showed no significant degradation. Furthermore, substantial inter-individual variability was observed, with wrist-worn data showing significant sex-specific biases (p < 0.02). This comprehensive analysis highlights the importance of quantifying uncertainty for robust step counting and provides critical insights into optimal sensor deployment and resource allocation for reliable activity monitoring.

Subjects:	eess.SP; cs.LG
Cite as:	PX:2508.00004