Invariant Feature Learning for Sensor-based Human Activity Recognition

Abstract

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.

References in corpus (9)

• Overcoming catastrophic forgetting in neural networks
• Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
• An Overview of Multi-Task Learning in Deep Neural Networks
• Deep Learning for Sensor-based Activity Recognition: A Survey
• A review of domain adaptation without target labels
• Multi-task Self-Supervised Learning for Human Activity Detection
• Cross-Subject Transfer Learning in Human Activity Recognition Systems using Generative Adversarial Networks
• One-Shot Imitation from Observing Humans via Domain-Adaptive Meta-Learning
• Few-Shot Learning via Learning the Representation, Provably