Detection of Short-Form Video Addiction with Wearable Sensors via Temporally-Coherent Domain Adaptation

Presentation Time: 04:30 PM - 04:45 PM

Abstract Keywords: Mobile Health, Deep Learning, Ubiquitous Computing and Sensors
Primary Track: Applications
Programmatic Theme: Translational Bioinformatics

Short-form Video Addiction (SVA), a novel digital addiction of the modern world, proliferates among young adults and is not formally diagnosable. SVA detection from resulting bio-signals is crucial to prevent its adverse impacts. Existing formal methods involve large and expensive neuro-imaging devices in laboratory setups that are intrusive and not feasible to use in daily life. A possible non-intrusive solution can be using wearable sensors which is challenging due to the resulting noisy and faint signals. To address this problem, we investigate multi-modal wearable sensing technology to detect SVA in a non-intrusive fashion. However, fusing multi-modal sensors effectively presents different challenges due to the presence of signal heterogeneity. In this study, we propose a novel multi-modal temporally coherent domain adaptation method to effectively detect SVA using Electroencephalogram (EEG) and Electrodermal Activity (EDA) sensors. We also investigate the nature and properties of SVA with the help of different components of EEG and EDA signals. We evaluate our proposed method for SVA detection and fatigue assessment tasks. Experimental evaluation posits the proposed model's superior performance (10% accuracy) over state-of-art domain adaptation models.

Speaker(s):
Mahmudur Rahman, PhD
University of Wisconsin-Madison

Author(s):
Mahmudur Rahman, PhD - University of Wisconsin-Madison; Atqiya Munawara Mahi, MS - University of Massachusetts Lowell; Sharmin Sultana, Ph.D. Student - University of Massachusetts Lowell; Matthew Churpek, MD, MPH, PhD - University of Wisconsin-Madison; Mohammad Arif Ul Alam, Assistant Professor/PhD - University of Massachusetts Lowell;