PERSPECTIVE article
Front. Digit. Health
Sec. Health Informatics
Volume 6 – 2024 |
doi: 10.3389/fdgth.2024.1467424
Provisionally accepted
- 1
Duke University, Durham, North Carolina, United States - 2
Northeastern University, Boston, United States
The final, formatted version of the article will be published soon.
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.
Keywords:
physiological signals, Signal processing, autonomic signals, open-source, Psychophysiology, Digital phenotyping
Received:
19 Jul 2024;
Accepted:
16 Dec 2024.
Copyright:
© 2024 Dunn, Mishra, Shandhi, Jeong, Yamane, Watanabe, Chen and Goodwin. This is an
open-access article distributed under the terms of the
Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted,
provided the original author(s) or licensor are credited and that the
original publication in this journal is cited, in accordance with accepted
academic practice. No use, distribution or reproduction is permitted which
does not comply with these terms.
* Correspondence:
Varun Mishra, Northeastern University, Boston, United States
Disclaimer:
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