Rahman Mahmudur, Mahi Atqiya Munawara, Sultana Sharmin, Churpek Matthew M, Ul Alam Mohammad Arif
University of Massachusetts Lowell.
University of Wisconsin-Madison.
AMIA Annu Symp Proc. 2025 May 22;2024:940-949. eCollection 2024.
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 investigated multi-modal wearable sensing technology to detect SVA in a non-intrusive fashion. However, fusing multi-modal sensors effectively presented different challenges due to the presence of signal heterogeneity. In this study, we proposed a novel multi-modal temporally coherent domain adaptation method to effectively detect SVA using Electroencephalogram (EEG) and Electrodermal Activity (EDA) sensors. We also investigated the nature and properties of SVA with the help of different components of EEG and EDA signals. We evaluated our proposed method for SVA detection and fatigue assessment tasks. Experimental evaluation shown the proposed model's superior performance (10% accuracy) over state-of-art domain adaptation models.
短视频成瘾（SVA）是现代社会一种新型的数字成瘾现象，在年轻人中迅速蔓延，且尚无正式的诊断方法。从生物信号中检测SVA对于预防其不良影响至关重要。现有的正式检测方法需要在实验室环境中使用大型且昂贵的神经成像设备，这些设备具有侵入性，无法在日常生活中使用。一种可能的非侵入性解决方案是使用可穿戴传感器，但由于信号噪声大且微弱，这具有挑战性。为了解决这个问题，我们研究了多模态可穿戴传感技术，以非侵入方式检测SVA。然而，由于信号异质性的存在，有效融合多模态传感器带来了不同的挑战。在本研究中，我们提出了一种新颖的多模态时间相干域适应方法，利用脑电图（EEG）和皮肤电活动（EDA）传感器有效检测SVA。我们还借助EEG和EDA信号的不同成分研究了SVA的本质和特性。我们评估了所提出的SVA检测和疲劳评估方法。实验评估表明，所提出的模型比现有域适应模型具有更高的性能（准确率提高10%）。
