Research Proposal: Federated Learning to Understand Human Emotions Via Smart Clothing

This paper contains the research proposal of Mary Pidgeon that was presented at the MMSys 2022 doctoral symposium. Emotion recognition from physiological signals has seen a huge growth in recent decades. Wearables such as smart watches now have sensors to accurately measure physiological signals such as electrocardiography (ECG), blood volume pressure (BVP), galvanic skin response (GSR), and skin temperature (ST). These sensors have also been embedded in textiles. Collaborative body sensor networks (CBSN) have been used to analyse emotion reactions in a social setting from heart rate sensors. Federated learning, a recently proposed machine learning paradigm, protects user's private information while using information from several users to train a global machine learning model. Federated learning has several categorisations based on data partitioning, the privacy mechanisms, machine learning models and methods for solving heterogeneity. In this doctoral thesis, we propose using a smart clothing body sensor network to collect peripheral physiological data while protecting the user's privacy using federated machine learning. We present three primary research questions to address the challenges in emotion prediction, data collection from e-textile sensors and federated (FL) learning.