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This research addresses the need for reliable fall detection systems to safeguard older adults by identifying anomalous movement patterns. Using a CNN-based framework, this research employs proposed CNNs combined with data augmentation techniques to improve model accuracy. Testing was conducted on publicly available datasets for fall detection, yielding high accuracy, sensitivity, and specificity metrics. The proposed solution can serve as an effective tool in monitoring elderly individuals, potentially reducing healthcare costs and enhancing their quality of life.

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Tuning Impact Parameters of the Convolutional Neural Network for Efficient Detection

  • 章节

  • 发表日期：

    2026年

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  • Pinthusorn Pasanajano,
  • Sunantha Sodsee

摘要

This research addresses the need for reliable fall detection systems to safeguard older adults by identifying anomalous movement patterns. Using a CNN-based framework, this research employs proposed CNNs combined with data augmentation techniques to improve model accuracy. Testing was conducted on publicly available datasets for fall detection, yielding high accuracy, sensitivity, and specificity metrics. The proposed solution can serve as an effective tool in monitoring elderly individuals, potentially reducing healthcare costs and enhancing their quality of life.