Generative Pretrained Embedding and Hierarchical Irregular Time Series Representation for Daily Living Activity Recognition - Equipe Robot interaction, Ambient system, Machine learning, Behaviour, Optimization
Communication Dans Un Congrès Année : 2024

Generative Pretrained Embedding and Hierarchical Irregular Time Series Representation for Daily Living Activity Recognition

Résumé

Within the evolving landscape of smart homes, the precise recognition of daily living activities using ambient sensor data stands paramount. This paper not only aims to bolster existing algorithms by evaluating two distinct pretrained embeddings suited for ambient sensor activations but also introduces a novel hierarchical architecture. We delve into an architecture anchored on Transformer Decoder-based pre-trained embeddings, reminiscent of the GPT design, and contrast it with the previously established state-of-the-art (SOTA) ELMo embeddings for ambient sensors. Our proposed hierarchical structure leverages the strengths of each pre-trained embedding, enabling the discernment of activity dependencies and sequence order, thereby enhancing classification precision. To further refine recognition, we incorporate into our proposed architecture an hour-of-the-day embedding. Empirical evaluations underscore the preeminence of the Transformer Decoder embedding in classification endeavors. Additionally, our innovative hierarchical design significantly bolsters the efficacy of both pre-trained embeddings, notably in capturing inter-activity nuances. The integration of temporal aspects subtly but distinctively augments classification, especially for time-sensitive activities. In conclusion, our GPT-inspired hierarchical approach, infused with temporal insights, outshines the SOTA ELMo benchmark.
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hal-04858744 , version 1 (29-12-2024)

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Damien Bouchabou, Sao Mai Nguyen. Generative Pretrained Embedding and Hierarchical Irregular Time Series Representation for Daily Living Activity Recognition. ECAI 2024, Oct 2024, Santiago de Compostela, Spain. pp.4764 - 4771, ⟨10.3233/faia241075⟩. ⟨hal-04858744⟩
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