Transferring Activity Recognition Models for New Wearable Sensors with Deep Generative Domain Adaptation

Wearable sensors provide enormous opportunities to identify activities and events of interest for various applications. However, a major limitation of the current systems is the fact that machine learning algorithms trained on particular sensors need to be retrained upon any changes in configuration of the system, such as adding a new sensor. In this paper, we aim to seamlessly train machine learning algorithms for the new sensors to identify activities and observations that are detectable by the pre-existing sensors. We create a domain adaptation method to expand training algorithms from known wearable sensors to new sensors, eliminating the need for manual training of machine learning algorithms. Specifically, our proposed approach eliminates the need for capturing substantial amount of data on new sensors. We propose the concept of stochastic features for human activity recognition, and design a new architecture of deep neural network to approximate the posterior distribution of the features. This approximation aligns the feature space of the new and old sensors by using limited, unlabeled data from the new sensor so that the previously defined classifier can be used with the new sensor. The experimental results show that (i) stochastic features are more robust against additive noise compared to typical convolutional neural networks based on deterministic features (ii) our framework outperforms the state-of-the-art domain adaptation algorithms. It can also achieve 10% improvement when training new sensors with limited unlabeled training data compared to training a model from scratch for the new sensor.