Incremental Real-Time Personalization in Human Activity Recognition Using Domain Adaptive Batch Normalization

TL;DR

This paper introduces an unsupervised online domain adaptation method for human activity recognition that personalizes models in real time without requiring user labels, handling evolving user patterns effectively.

Contribution

It proposes a novel incremental normalization technique that aligns feature distributions across users in neural networks for real-time, unsupervised personalization in HAR.

Findings

Effective real-time adaptation to new users

Handles changing user activity patterns

No prior user data needed for personalization

Abstract

Human Activity Recognition (HAR) from devices like smartphone accelerometers is a fundamental problem in ubiquitous computing. Machine learning based recognition models often perform poorly when applied to new users that were not part of the training data. Previous work has addressed this challenge by personalizing general recognition models to the unique motion pattern of a new user in a static batch setting. They require target user data to be available upfront. The more challenging online setting has received less attention. No samples from the target user are available in advance, but they arrive sequentially. Additionally, the motion pattern of users may change over time. Thus, adapting to new and forgetting old information must be traded off. Finally, the target user should not have to do any work to use the recognition system by, say, labeling any activities. Our work addresses all of these challenges by proposing an unsupervised online domain adaptation algorithm. Both classification and personalization happen continuously and incrementally in real time. Our solution works by aligning the feature distributions of all subjects, be they sources or the target, in hidden neural network layers. To this end, we normalize the input of a layer with user-specific mean and variance statistics. During training, these statistics are computed over user-specific batches. In the online phase, they are estimated incrementally for any new target user.