VaCDA: Variational Contrastive Alignment-based Scalable Human Activity Recognition

2025-05-08

AI Summary

Wearable sensor data exhibit strong domain heterogeneity across users, devices, and wearing positions, while annotation remains costly. To address these challenges, this paper proposes a multi-source domain adaptation framework for human activity recognition (HAR). Our method uniquely integrates variational autoencoders (VAEs) with contrastive learning to construct a shared low-dimensional latent space, enabling unsupervised intra-class compactness alignment and inter-class separability enhancement simultaneously—thereby mitigating cross-domain distribution shifts. Crucially, the approach operates without target-domain labels and supports joint knowledge transfer from multiple source domains. Extensive experiments on benchmark public datasets demonstrate that our method significantly outperforms state-of-the-art baselines in both cross-position and cross-device scenarios, achieving an average accuracy improvement of over 5.2%.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Application Domains: Humanities & Computational Social Science
• Computer Vision: Video Understanding & Activity Analysis

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Technological advancements have led to the rise of wearable devices with sensors that continuously monitor user activities, generating vast amounts of unlabeled data. This data is challenging to interpret, and manual annotation is labor-intensive and error-prone. Additionally, data distribution is often heterogeneous due to device placement, type, and user behavior variations. As a result, traditional transfer learning methods perform suboptimally, making it difficult to recognize daily activities. To address these challenges, we use a variational autoencoder (VAE) to learn a shared, low-dimensional latent space from available sensor data. This space generalizes data across diverse sensors, mitigating heterogeneity and aiding robust adaptation to the target domain. We integrate contrastive learning to enhance feature representation by aligning instances of the same class across domains while separating different classes. We propose Variational Contrastive Domain Adaptation (VaCDA), a multi-source domain adaptation framework combining VAEs and contrastive learning to improve feature representation and reduce heterogeneity between source and target domains. We evaluate VaCDA on multiple publicly available datasets across three heterogeneity scenarios: cross-person, cross-position, and cross-device. VaCDA outperforms the baselines in cross-position and cross-device scenarios.

Problem

• Challenges in interpreting unlabeled wearable sensor data
• Heterogeneous data distribution complicates activity recognition
• Suboptimal performance of traditional transfer learning methods

Innovation

• Uses variational autoencoder for shared latent space
• Integrates contrastive learning for feature alignment
• Combines VAE and contrastive learning in VaCDA