Wearable sensors enable health researchers to continuously collect data pertaining to the physiological state of individuals in real-world settings. However, such data can be subject to extensive missingness due to a complex combination of factors. In this work, we study the problem of imputation of missing step count data, one of the most ubiquitous forms of wearable sensor data. We construct a novel and large scale data set consisting of a training set with over 3 million hourly step count observations and a test set with over 2.5 million hourly step count observations. We propose a domain knowledge-informed sparse self-attention model for this task that captures the temporal multi-scale nature of step-count data. We assess the performance of the model relative to baselines and conduct ablation studies to verify our specific model designs.
@article{arxiv.2406.18848,
title = {Temporally Multi-Scale Sparse Self-Attention for Physical Activity Data Imputation},
author = {Hui Wei and Maxwell A. Xu and Colin Samplawski and James M. Rehg and Santosh Kumar and Benjamin M. Marlin},
journal= {arXiv preprint arXiv:2406.18848},
year = {2024}
}
Comments
Accepted by Conference on Health, Inference, and Learning (CHIL) 2024