Dunn Jessilyn, Mishra Varun, Shandhi Md Mobashir Hasan, Jeong Hayoung, Yamane Natasha, Watanabe Yuna, Chen Bill, Goodwin Matthew S
Department of Biomedical Engineering, Duke University, Durham, NC, United States.
Khoury College of Computer Sciences and Bouvé College of Health Sciences, Northeastern University, Boston, MA, United States.
Front Digit Health. 2025 Jan 9;6:1467424. doi: 10.3389/fdgth.2024.1467424. eCollection 2024.
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.
智能手机和可穿戴传感器提供了前所未有的能力，可在不同的时间尺度、环境、人群和模式下收集外周心理生理信号。然而，开源软件开发尚未跟上硬件技术和可用性的快速发展，造成了一个分析障碍，限制了所采集数据的科学实用性。我们提出了一个由社区驱动的开源外周心理生理信号预处理和分析软件框架，该框架可通过实现涉及自主神经系统数据的更稳健、透明和可重复的推断，推动生物行为健康发展。
