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The proposed stress detection and mitigation system employs a machine learning model that leverages both physiological and psychological parameters as input for accurate stress detection. The physiological parameters include signals such as heart rate and blood oxygen level, while the psychological parameters are obtained from questionnaires that capture the user’s current state of mind. To ensure data privacy, we utilize Federated Learning and employ technologies like Flower and Android to develop a framework that can be deployed on a real network as a mobile application. This application provides fast and safe assessment of stress levels and offers personalized suggestions to reduce stress, thereby helping individuals achieve a less stressful life.

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Federated Learning-Based Stress Detection and Curative Recommendation System

  • 会议论文

  • 发表日期：

    2026年

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  • Priyanka Chawla,
  • Anik Das,
  • Naman Balai,
  • Joy Chhajed,
  • Jauwaad Shams

摘要

The proposed stress detection and mitigation system employs a machine learning model that leverages both physiological and psychological parameters as input for accurate stress detection. The physiological parameters include signals such as heart rate and blood oxygen level, while the psychological parameters are obtained from questionnaires that capture the user’s current state of mind. To ensure data privacy, we utilize Federated Learning and employ technologies like Flower and Android to develop a framework that can be deployed on a real network as a mobile application. This application provides fast and safe assessment of stress levels and offers personalized suggestions to reduce stress, thereby helping individuals achieve a less stressful life.