Invariant Feature Learning for Sensor-based Human Activity Recognition
This work tackles the problem of poor generalization in wearable sensor-based human activity recognition for new users and devices, which is an incremental improvement for the HAR community.
This paper addresses the challenge of generalizing pre-trained human activity recognition (HAR) models to unseen subjects and sensor devices, which is hindered by significant data variances. The proposed Invariant Feature Learning Framework (IFLF) extracts common information across subjects and devices, achieving up to a 40% improvement in test accuracy over baseline models.
Wearable sensor-based human activity recognition (HAR) has been a research focus in the field of ubiquitous and mobile computing for years. In recent years, many deep models have been applied to HAR problems. However, deep learning methods typically require a large amount of data for models to generalize well. Significant variances caused by different participants or diverse sensor devices limit the direct application of a pre-trained model to a subject or device that has not been seen before. To address these problems, we present an invariant feature learning framework (IFLF) that extracts common information shared across subjects and devices. IFLF incorporates two learning paradigms: 1) meta-learning to capture robust features across seen domains and adapt to an unseen one with similarity-based data selection; 2) multi-task learning to deal with data shortage and enhance overall performance via knowledge sharing among different subjects. Experiments demonstrated that IFLF is effective in handling both subject and device diversion across popular open datasets and an in-house dataset. It outperforms a baseline model of up to 40% in test accuracy.