DISCOVER: Data-driven Identification of Sub-activities via Clustering and Visualization for Enhanced Activity Recognition in Smart Homes

• URL: http://arxiv.org/abs/2503.01733v1
• Date: Tue, 11 Feb 2025 20:02:24 GMT
• Title: DISCOVER: Data-driven Identification of Sub-activities via Clustering and Visualization for Enhanced Activity Recognition in Smart Homes
• Authors: Alexander Karpekov, Sonia Chernova, Thomas Plötz,
• Abstract summary: We introduce DISCOVER, a method to discover fine-grained human sub-activities from unlabeled sensor data without relying on pre-segmentation.<n>We demonstrate its effectiveness through a re-annotation exercise on widely used HAR datasets.
• Score: 52.09869569068291
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Human Activity Recognition (HAR) using ambient sensors has great potential for practical applications, particularly in elder care and independent living. However, deploying HAR systems in real-world settings remains challenging due to the high cost of labeled data, the need for pre-segmented sensor streams, and the lack of flexibility in activity granularity. To address these limitations, we introduce DISCOVER, a method designed to discover fine-grained human sub-activities from unlabeled sensor data without relying on pre-segmentation. DISCOVER combines unsupervised feature extraction and clustering with a user-friendly visualization tool to streamline the labeling process. DISCOVER enables domain experts to efficiently annotate only a minimal set of representative cluster centroids, reducing the annotation workload to a small number of samples (0.05% of our dataset). We demonstrate DISCOVER's effectiveness through a re-annotation exercise on widely used HAR datasets, showing that it uncovers finer-grained activities and produces more nuanced annotations than traditional coarse labels. DISCOVER represents a step toward practical, deployable HAR systems that adapt to diverse real environments.

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