EarDA: Towards Accurate and Data-Efficient Earable Activity Sensing

TL;DR

EarDA is a novel adversarial domain adaptation system that significantly improves the accuracy and data efficiency of activity recognition using earable devices, even with head movement challenges, by leveraging domain-independent features.

Contribution

The paper introduces EarDA, a new adversarial domain adaptation approach that enhances activity recognition accuracy and data efficiency for earable devices, addressing head movement variability.

Findings

Achieves 88.8% accuracy on HAR task.

Provides a 43% improvement over non-domain-adaptive methods.

Demonstrates effectiveness in mitigating domain gaps and head movement effects.

Abstract

In the realm of smart sensing with the Internet of Things, earable devices are empowered with the capability of multi-modality sensing and intelligence of context-aware computing, leading to its wide usage in Human Activity Recognition (HAR). Nonetheless, unlike the movements captured by Inertial Measurement Unit (IMU) sensors placed on the upper or lower body, those motion signals obtained from earable devices show significant changes in amplitudes and patterns, especially in the presence of dynamic and unpredictable head movements, posing a significant challenge for activity classification. In this work, we present EarDA, an adversarial-based domain adaptation system to extract the domain-independent features across different sensor locations. Moreover, while most deep learning methods commonly rely on training with substantial amounts of labeled data to offer good accuracy, the proposed scheme can release the potential usage of publicly available smartphone-based IMU datasets. Furthermore, we explore the feasibility of applying a filter-based data processing method to mitigate the impact of head movement. EarDA, the proposed system, enables more data-efficient and accurate activity sensing. It achieves an accuracy of 88.8% under HAR task, demonstrating a significant 43% improvement over methods without domain adaptation. This clearly showcases its effectiveness in mitigating domain gaps.