Identifying Subject Bias in WiFi-based Human Activity Recognition Evaluation Methods

WiFi-based Human Activity Recognition (HAR) has emerged as a promising approach for monitoring and analysing human activities in a non-intrusive manner, leveraging WiFi signals for activity classification. Despite advancements, existing WiFi-based HAR research lacks consideration of subject (human) bias. This results in learning models performing well on individuals used in the training samples but failing to generalise to new/unseen subjects, in contrast to known good practices in machine learning. In this paper, we address this oversight directly by systematically examining the evaluation methodology for the WiFi-based HAR context. Specifically, we investigate the impact of Leave-One-Subject-Out Cross-Validation (LOSOCV) in a hybrid architecture combining Convolutional Neural Networks (CNN) and Attention-based Bidirectional Long Short-Term Memory networks (ABiLSTM), designed to capture both spatial and temporal patterns in WiFi signals. However, our emphasis remains on the application of LOSOCV as a method for improving generalization and reducing subject bias, rather than on the architecture itself. The model’s effectiveness is evaluated using LOSOCV, and we compare its performance against conventional hold-out validation and k-fold validation. Additionally, we utilize weighted metrics for model evaluation to address class imbalance, ensuring a fair assessment across all activity categories. Our results demonstrate the importance of LOSOCV in providing a realistic assessment of HAR model performance and underscore that addressing subject bias is essential for the deployment of these systems in practical scenarios such as healthcare monitoring, smart homes, and security applications.