Promoting Digital Wellbeing Through Real-Time State Classification of Psychophysiological Sensor Networks
The Second Neuroadaptive Technology Conference, July 2019
Matthew Pike, Christopher Roadknight. 2019. Promoting Digital Wellbeing Through Real-Time State Classification of Psychophysiological Sensor Networks. In The Second Neuroadaptive Technology Conference.
Matthew Pike and Christopher Roadknight. (2019). Promoting Digital Wellbeing Through Real-Time State Classification of Psychophysiological Sensor Networks. The Second Neuroadaptive Technology Conference.
Matthew Pike and Christopher Roadknight. "Promoting Digital Wellbeing Through Real-Time State Classification of Psychophysiological Sensor Networks." The Second Neuroadaptive Technology Conference, 2019.
Matthew Pike, Christopher Roadknight. 2019. Promoting Digital Wellbeing Through Real-Time State Classification of Psychophysiological Sensor Networks. The Second Neuroadaptive Technology Conference.
Matthew Pike and Christopher Roadknight, "Promoting Digital Wellbeing Through Real-Time State Classification of Psychophysiological Sensor Networks," The Second Neuroadaptive Technology Conference, 2019.
@inproceedings{nat-2019,
title={Promoting Digital Wellbeing Through Real-Time State Classification of Psychophysiological Sensor Networks},
author={Matthew Pike and Christopher Roadknight},
booktitle={The Second Neuroadaptive Technology Conference},
year={2019}
}
Digital wellbeing, Psychophysiological sensor networks, Deep learning, Real-time state classification, User state prediction, Neuroadaptive interfaces
Abstract
Boundaries between digital experiences and daily activities have blurred as technology becomes more pervasive. This study explores the application of psychophysiological sensor networks for real-time user state classification to promote digital wellbeing. Using deep learning (DL), we propose a novel pruning algorithm to enhance operational efficiency and classification accuracy in constrained mobile environments. By leveraging physiological data from sources such as ECG, fNIRS, EEG, and EDA, DL models can predict cognitive states like mental workload, fatigue, and task engagement. Neuroadaptive interfaces can then mediate user interactions with digital systems, improving focus, reducing interruptions (e.g., mobile notifications), and enhancing productivity. The study demonstrates the impact of correlated activity pruning (CAP) in refining multi-layer networks for efficient real-time classification of user states.