A multi-task self-supervised approach trains a temporal CNN to detect transformations on sensory data, yielding features that match or exceed fully supervised performance in semi-supervised and transfer settings for smartphone-based HAR.
Personalized Driver Stress Detection with Multi-task Neural Networks using Physiological Signals
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abstract
Stress can be seen as a physiological response to everyday emotional, mental and physical challenges. A long-term exposure to stressful situations can have negative health consequences, such as increased risk of cardiovascular diseases and immune system disorder. Therefore, a timely stress detection can lead to systems for better management and prevention in future circumstances. In this paper, we suggest a multi-task learning based neural network approach (with hard parameter sharing of mutual representation and task-specific layers) for personalized stress recognition using skin conductance and heart rate from wearable devices. The proposed method is tested on multi-modal physiological responses collected during real-world and simulator driving tasks.
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cs.LG 1years
2019 1verdicts
UNVERDICTED 1representative citing papers
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Multi-task Self-Supervised Learning for Human Activity Detection
A multi-task self-supervised approach trains a temporal CNN to detect transformations on sensory data, yielding features that match or exceed fully supervised performance in semi-supervised and transfer settings for smartphone-based HAR.