Active Reinforcement Learning for Personalized Stress Monitoring in Everyday Settings

• URL: http://arxiv.org/abs/2305.00111v1
• Date: Fri, 28 Apr 2023 22:09:19 GMT
• Title: Active Reinforcement Learning for Personalized Stress Monitoring in Everyday Settings
• Authors: Ali Tazarv, Sina Labbaf, Amir Rahmani, Nikil Dutt, Marco Levorato
• Abstract summary: In this paper, we consider a fine-grain stress detection problem based on wearable sensors targeting everyday settings. We develop a multilayered sensor-edge-cloud platform to periodically capture physiological signals and process them in real-time. We show that the context-aware active learning technique we propose achieves a desirable detection performance using 88% and 32% fewer queries from users.
• Score: 4.4353357514621745
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Most existing sensor-based monitoring frameworks presume that a large available labeled dataset is processed to train accurate detection models. However, in settings where personalization is necessary at deployment time to fine-tune the model, a person-specific dataset needs to be collected online by interacting with the users. Optimizing the collection of labels in such phase is instrumental to impose a tolerable burden on the users while maximizing personal improvement. In this paper, we consider a fine-grain stress detection problem based on wearable sensors targeting everyday settings, and propose a novel context-aware active learning strategy capable of jointly maximizing the meaningfulness of the signal samples we request the user to label and the response rate. We develop a multilayered sensor-edge-cloud platform to periodically capture physiological signals and process them in real-time, as well as to collect labels and retrain the detection model. We collect a large dataset and show that the context-aware active learning technique we propose achieves a desirable detection performance using 88\% and 32\% fewer queries from users compared to a randomized strategy and a traditional active learning strategy, respectively.

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