Smartphones and wearable sensors offer an unprecedented ability to collect peripheral psychophysiological signals across diverse timescales, settings, populations, and modalities. However, open-source software development has yet to keep pace with rapid advancements in hardware technology and availability, creating an analytical barrier that limits the scientific usefulness of acquired data. We propose a community-driven, open-source peripheral psychophysiological signal pre-processing and analysis software framework that could advance biobehavioral health by enabling more robust, transparent, and reproducible inferences involving autonomic nervous system data.
@article{arxiv.2403.17165,
title = {Building an Open-Source Community to Enhance Autonomic Nervous System Signal Analysis: DBDP-Autonomic},
author = {Jessilyn Dunn and Varun Mishra and Md Mobashir Hasan Shandhi and Hayoung Jeong and Natasha Yamane and Yuna Watanabe and Bill Chen and Matthew S. Goodwin},
journal= {arXiv preprint arXiv:2403.17165},
year = {2024}
}