Temporally Multi-Scale Sparse Self-Attention for Physical Activity Data Imputation
<|reference_start|>Temporally Multi-Scale Sparse Self-Attention for Physical Activity Data Imputation: 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.<|reference_end|>
@article{wei2024temporally,
    title={Temporally Multi-Scale Sparse Self-Attention for Physical Activity Data
  Imputation},
    author={Hui Wei, Maxwell A. Xu, Colin Samplawski, James M. Rehg, Santosh
  Kumar, Benjamin M. Marlin},
    journal={arXiv preprint arXiv:2406.18848},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.18848},
    primaryClass={cs.LG}
}