Year: 2025
Type: article
Abstract: Wearable sensors are increasingly used to monitor physical activity, yet low-power devices often rely on data aggregation to conserve battery life, potentially impacting measurement accuracy. This study evaluates the performance of a new low-power wearable (LPW), designed for monitoring steps across multiple months in a free-living environment, compared to a research-grade sensor (RGS) that collects raw acceleration data, with a focus on how different aggregation intervals impact step count accuracy. Thirty-two participants wore both sensors over two days, with LPW data collected in 10 min, 1 min, or 10 s aggregation periods (APs). Sensitivity and specificity of wear time detection were high across all APs (0.96 and 0.98, respectively). While total daily step count error did not differ significantly between APs, the 10 min AP exhibited greater undercounting and wider limits of agreement, especially in APs containing more than 40 steps. These findings suggest that although AP does not affect total daily step count, it influences the accuracy and variability of more granular data windows. Aggregating step counts over longer intervals may obscure short, fragmented bouts common in daily activity, leading to underestimation of steps. Optimizing APs and sensor settings is critical for improving accuracy in low-power wearables used outside laboratory settings.
Source: Sensors
DOI: 10.3390/s25226998
Authors: Sydney Lundell, Kenton R. Kaufman
Institution: Mayo Clinic
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
OpenAlex ID: W4416393081