  Human Activity Recognition (HAR) models often suffer from performance
degradation in real-world applications due to distribution shifts in activity
patterns across individuals. Test-Time Adaptation (TTA) is an emerging learning
paradigm that aims to utilize the test stream to adjust predictions in
real-time inference, which has not been explored in HAR before. However, the
high computational cost of optimization-based TTA algorithms makes it
intractable to run on resource-constrained edge devices. In this paper, we
propose an Optimization-Free Test-Time Adaptation (OFTTA) framework for
sensor-based HAR. OFTTA adjusts the feature extractor and linear classifier
simultaneously in an optimization-free manner. For the feature extractor, we
propose Exponential DecayTest-time Normalization (EDTN) to replace the
conventional batch normalization (CBN) layers. EDTN combines CBN and Test-time
batch Normalization (TBN) to extract reliable features against domain shifts
with TBN's influence decreasing exponentially in deeper layers. For the
classifier, we adjust the prediction by computing the distance between the
feature and the prototype, which is calculated by a maintained support set. In
addition, the update of the support set is based on the pseudo label, which can
benefit from reliable features extracted by EDTN. Extensive experiments on
three public cross-person HAR datasets and two different TTA settings
demonstrate that OFTTA outperforms the state-of-the-art TTA approaches in both
classification performance and computational efficiency. Finally, we verify the
superiority of our proposed OFTTA on edge devices, indicating possible
deployment in real applications. Our code is available at
https://github.com/Claydon-Wang/OFTTA.
