Quantifying the Robustness of Accelerometer-Derived Gait Features for Step Counting Across Sensor Locations and Sampling Frequencies

Abstract: Accurate and robust step counting using wearable accelerometers is essential for health monitoring, yet the influence of sensor placement and data resolution on algorithm performance remains underexplored. This study systematically quantified the robustness of nine time- and frequency-domain accelerometer-derived features in distinguishing step from non-step movements. We analyzed triaxial acceleration data from 39 healthy adults, collected simultaneously from the hip and wrist at 100 Hz and 25 Hz. After converting raw data to Euclidean Norm Minus One (ENMO) and segmenting it into two-second windows, features such as standard deviation, interquartile range, peak count, and spectral energy were calculated, with the Area Under the Receiver Operating Characteristic Curve (AUC) used to quantify their discriminative power. Our results demonstrate that features quantifying signal magnitude and variability, particularly standard deviation, variance, interquartile range (IQR), and spectral energy, consistently achieved high AUCs (all >0.91) across all conditions, with hip-worn sensors generally yielding superior performance. Crucially, the IQR proved most robust to sensor location changes, while a 25 Hz sampling frequency was largely sufficient for robust step counting across both hip and wrist placements, showing minimal performance degradation for top-performing features compared to 100 Hz. Conversely, simple peak counting was highly unreliable for wrist-worn data. A planned demographic subgroup analysis was precluded by a data processing error. These findings offer critical insights for designing resource-efficient and reliable step-counting algorithms, highlighting the suitability of specific features and lower sampling rates for diverse wearable applications. \

Subjects:	eess.SP; cs.LG
Cite as:	PX:2508.00023