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: autonomic signals; digital phenotyping; open-source; physiological signals; psychophysiology; signal processing.
© 2025 Dunn, Mishra, Shandhi, Jeong, Yamane, Watanabe, Chen and Goodwin.