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.

翻译：在智能家居不断发展的背景下，利用环境传感器数据精确识别日常生活活动至关重要。本文不仅旨在通过评估两种适用于环境传感器激活的预训练嵌入来增强现有算法，还引入了一种新颖的层次化架构。我们深入探讨了一种基于Transformer Decoder预训练嵌入的架构（其设计灵感源自GPT），并将其与先前建立的、针对环境传感器的先进ELMo嵌入进行对比。我们提出的层次化结构充分利用了每种预训练嵌入的优势，能够识别活动间的依赖关系与序列顺序，从而提升分类精度。为进一步优化识别效果，我们在所提出的架构中引入了"一天中的时刻"嵌入。实证评估突显了Transformer Decoder嵌入在分类任务中的卓越性能。此外，我们创新的层次化设计显著增强了两种预训练嵌入的效能，尤其在捕捉活动间细微差异方面。时间特征的融入虽细微但显著地提升了分类效果，尤其对于时间敏感型活动。总之，我们受GPT启发的、融合了时间洞察的层次化方法，其性能超越了先进的ELMo基准。