Human activity recognition (HAR) using IMU sensors, namely accelerometer and
gyroscope, has several applications in smart homes, healthcare and
human-machine interface systems. In practice, the IMU-based HAR system is
expected to encounter variations in measurement due to sensor degradation,
alien environment or sensor noise and will be subjected to unknown activities.
In view of practical deployment of the solution, analysis of statistical
confidence over the activity class score are important metrics. In this paper,
we therefore propose XAI-BayesHAR, an integrated Bayesian framework, that
improves the overall activity classification accuracy of IMU-based HAR
solutions by recursively tracking the feature embedding vector and its
associated uncertainty via Kalman filter. Additionally, XAI-BayesHAR acts as an
out of data distribution (OOD) detector using the predictive uncertainty which
help to evaluate and detect alien input data distribution. Furthermore, Shapley
value-based performance of the proposed framework is also evaluated to
understand the importance of the feature embedding vector and accordingly used
for model compressio