"Filling the Blanks'': Identifying Micro-activities that Compose Complex Human Activities of Daily Living

• URL: http://arxiv.org/abs/2306.13149v2
• Date: Wed, 7 Feb 2024 23:08:29 GMT
• Title: "Filling the Blanks'': Identifying Micro-activities that Compose Complex Human Activities of Daily Living
• Authors: Soumyajit Chatterjee, Bivas Mitra and Sandip Chakraborty
• Abstract summary: AmicroN adapts a top-down'' approach by exploiting coarse-grained annotated data to expand the macro-activities into constituent micro-activities. In the backend, AmicroN uses textitunsupervised change-point detection to search for the micro-activity boundaries across a complex ADL. We evaluate AmicroN on two real-life publicly available datasets and observe that AmicroN can identify the micro-activities with micro Ftextsubscript1-score $>0.75$ for both datasets.
• Score: 6.841115530838644
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Complex activities of daily living (ADLs) often consist of multiple micro-activities. When performed sequentially, these micro-activities help the user accomplish the broad macro-activity. Naturally, a deeper understanding of these micro-activities can help develop more sophisticated human activity recognition (HAR) models and add explainability to their inferred conclusions. Previous research has attempted to achieve this by utilizing fine-grained annotated data that provided the required supervision and rules for associating the micro-activities to identify the macro-activity. However, this ``bottom-up'' approach is unrealistic as getting such high-quality, fine-grained annotated sensor datasets is challenging, costly, and time-consuming. Understanding this, in this paper, we develop AmicroN, which adapts a ``top-down'' approach by exploiting coarse-grained annotated data to expand the macro-activities into their constituent micro-activities without any external supervision. In the backend, AmicroN uses \textit{unsupervised} change-point detection to search for the micro-activity boundaries across a complex ADL. Then, it applies a \textit{generalized zero-shot} approach to characterize it. We evaluate AmicroN on two real-life publicly available datasets and observe that AmicroN can identify the micro-activities with micro F\textsubscript{1}-score $>0.75$ for both datasets. Additionally, we also perform an initial proof-of-concept on leveraging the state-of-the-art (SOTA) large language models (LLMs) with attribute embeddings predicted by AmicroN to enhance further the explainability surrounding the detection of micro-activities.

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