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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