Treffer: In shift and in variance: assessing the robustness of HAR deep learning models against variability

Deep Learning (DL) based Human Activity Recognition (HAR) using wearable inertial measurement unit (IMU) sensors can revolutionize continuous health monitoring and early disease prediction. However, most DL HAR models are untested in their robustness to real-world variability as they are trained on limited lab-controlled data. In this study, we isolate and analyze the effects of subject, device, position, and orientation variability on DL HAR models using the HARVAR and REALDISP datasets. Maximum Mean Discrepancy (MMD) is used to quantify shifts in data distribution caused by these variabilities, and the relationship between distribution shifts and model performance is drawn. Our HARVAR results show that different types of variability significantly degrade DL model performance, with an inverse relationship between data distribution shifts and performance. The compounding effect of multiple variabilities studied using REALDISP further underscores the challenges of generalizing DL HAR models to real-world conditions. Analyzing these impacts highlights the need for more robust models that generalize effectively to real-world settings. MMD proved valuable for explaining performance drops, emphasizing its utility in evaluating distribution shifts in HAR data.