Generative Pretrained Embedding and Hierarchical Irregular Time Series Representation for Daily Living Activity Recognition
This work addresses activity recognition in smart homes, offering incremental improvements through novel embeddings and architecture for better classification.
The paper tackled daily living activity recognition from ambient sensor data by evaluating Transformer Decoder-based embeddings (GPT-inspired) against ELMo embeddings and introducing a hierarchical architecture with hour-of-the-day embeddings. The results showed the Transformer Decoder embedding outperformed ELMo, and the hierarchical design enhanced both embeddings, with temporal integration improving time-sensitive activity classification.
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.