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arXiv · 2007.07172

Attend And Discriminate: Beyond the State-of-the-Art for Human Activity Recognition using Wearable Sensors

Alireza Abedin·Mahsa Ehsanpour·Qinfeng Shi·Hamid Rezatofighi·Damith C. Ranasinghe
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Abstract

Wearables are fundamental to improving our understanding of human activities, especially for an increasing number of healthcare applications from rehabilitation to fine-grained gait analysis. Although our collective know-how to solve Human Activity Recognition (HAR) problems with wearables has progressed immensely with end-to-end deep learning paradigms, several fundamental opportunities remain overlooked. We rigorously explore these new opportunities to learn enriched and highly discriminating activity representations. We propose: i) learning to exploit the latent relationships between multi-channel sensor modalities and specific activities; ii) investigating the effectiveness of data-agnostic augmentation for multi-modal sensor data streams to regularize deep HAR models; and iii) incorporating a classification loss criterion to encourage minimal intra-class representation differences whilst maximising inter-class differences to achieve more discriminative features. Our contributions achieves new state-of-the-art performance on four diverse activity recognition problem benchmarks with large margins -- with up to 6% relative margin improvement. We extensively validate the contributions from our design concepts through extensive experiments, including activity misalignment measures, ablation studies and insights shared through both quantitative and qualitative studies.

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Alireza Abedin, Mahsa Ehsanpour, Qinfeng Shi, Hamid Rezatofighi, Damith C. Ranasinghe. 2020-07-14. Attend And Discriminate: Beyond the State-of-the-Art for Human Activity Recognition using Wearable Sensors. https://arxiv.org/abs/2007.07172

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