Privacy Preserving Stress Detection System Using Physiological Data from Wearable Device

Abstract

Stress is considered to be an emotional state deserving of special attention, as it brings about harmful effects on human health when exposed to in the long term. Stress may also induce general health risks, including headaches, sleep disorders, and cardiovascular diseases. Continuous monitoring of emotion can help patients suffering from psychiatric disorders better understand themselves and promote the emotional well-being of the public in general. Recent advancements in wearable technologies and biosensors enable a decent level of emotion and stress detection through multimodal machine learning analysis and measurement outside of lab conditions. As machine learning solutions demand a large amount of training data, collecting and combining personal data is a prerequisite for accurate analysis. However, due to the highly sensitive nature of medical data, the additional implementation of measures for the preservation of user privacy is a non-trivial task when developing an AI-based stress detection solution. We propose a novel machine learning stress detection system that facilitates privacy-preserving data exploitation based on FedAvg, a renowned federated learning algorithm. We evaluated our system design on a standard multimodal dataset for the detection of stress. Experiment results demonstrate that our system may achieve a detection accuracy of 75% without jeopardizing the privacy of user data.