Technology Category
Planning, Routing, and Scheduling: Activity and Plan RecognitionIntelligent Robots: Multimodal Perception & Sensor FusionComputer Vision: Video Understanding & Activity Analysis
Generative Pretrained Embedding and Hierarchical Irregular Time Series Representation for Daily Living Activity Recognition
🤖 AI Summary
This paper addresses the challenging task of recognizing complex daily activities in smart homes using environmental sensors. We propose a novel approach that integrates generative pretraining with hierarchical temporal modeling. Our key contributions are: (1) the first Transformer decoder-based pretraining framework tailored for environmental sensor data, replacing conventional ELMo-style representations; (2) a hierarchical irregular temporal encoder that jointly captures inter-activity dependencies and non-uniform temporal structures; and (3) learnable hour-level positional embeddings to significantly improve temporal awareness for time-sensitive activities (e.g., waking up, sleeping). Evaluated on multiple public benchmark datasets, our method achieves an average 5.3% absolute improvement in F1-score over the ELMo-based state-of-the-art, and yields a 9.1% gain in accuracy specifically for time-sensitive activity recognition.
Technology Category
Application Category
📝 Abstract
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.
Problem
Research questions and friction points this paper is trying to address.
Activity Recognition
Sensor Data
Smart Home Environment
Innovation
Methods, ideas, or system contributions that make the work stand out.
Pre-trained Generative Models
Temporal Embeddings
Hierarchical Design
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