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<p>Human Activity Recognition (HAR) allows for unobtrusive indoor monitoring, particularly in elderly care. However, existing HAR methods face significant challenges due to the variability in home layouts, sensor types, and activity labels across different datasets, which limits their generalization and scalability. Most approaches require extensive customization, making cross-environment HAR implementation challenging in real-world scenarios. To address these challenges, we propose a unified HAR framework that introduces Functional Areas, which abstract physical spaces into standardized activity zones, and Detector Units, which map heterogeneous sensor configurations into a common representation. We evaluate our framework using multiple publicly available HAR datasets based on ambient sensor data of smart homes, testing two model architectures: a Holistic Approach, which trains a single GRU-based neural network on the combined datasets, and a Reductionist Approach, which employs an ensemble bagging method. The Holistic Approach demonstrated superior generalisation, achieving 0.84 precision and 0.73 accuracy, outperforming the reductionist approach.</p>

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A uniform approach to HAR recognition in unobtrusive indoor monitoring systems

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年9月18日

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  • Fabio Salice,
  • Davide Mangano,
  • Kévin Bouchard,
  • Sara Comai

摘要

Human Activity Recognition (HAR) allows for unobtrusive indoor monitoring, particularly in elderly care. However, existing HAR methods face significant challenges due to the variability in home layouts, sensor types, and activity labels across different datasets, which limits their generalization and scalability. Most approaches require extensive customization, making cross-environment HAR implementation challenging in real-world scenarios. To address these challenges, we propose a unified HAR framework that introduces Functional Areas, which abstract physical spaces into standardized activity zones, and Detector Units, which map heterogeneous sensor configurations into a common representation. We evaluate our framework using multiple publicly available HAR datasets based on ambient sensor data of smart homes, testing two model architectures: a Holistic Approach, which trains a single GRU-based neural network on the combined datasets, and a Reductionist Approach, which employs an ensemble bagging method. The Holistic Approach demonstrated superior generalisation, achieving 0.84 precision and 0.73 accuracy, outperforming the reductionist approach.