Federated Learning for Wearable Sensor-Based Human Activity Recognition

Abstract

Computing devices that can identify various human behaviors or motions may be used to aid individuals in multiple contexts, including sports, healthcare, and interactions between humans and robots. Data readily accessible for this purpose may be collected from smartphones and wearable devices used daily. Therefore, efforts are made to classify real-time activity data effectively utilizing various machine learning models. However, current methods for human activity recognition do not sufficiently consider user data privacy. To mitigate privacy issues, federated learning can be employed to build generic activity classification model by aggregating a locally trained model at a user-edge device. This paper adopted a deep-learning neural network model called the transformer for motion signal time-series analysis. It uses the attention mechanism to provide context for each point in the time series. It also compares federated learning’s performance to centralized learning. Experimental results show that federated learning outperformed centralized training without com- promising user data privacy, with an accuracy of 96.87%. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.