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This chapter presents and discusses the desired characteristics that the research community and users expect from machine learning (ML) approaches for the classification of motion-based activities of daily living (ADLs) using accelerometer measurements, with an emphasis on recognizing various types of falls. Falls are a major contributor to injuries and fatalities among the elderly population, highlighting the importance of real-time fall detection and alert systems. ML algorithms have demonstrated high accuracy in fall detection in controlled experimental environments. However, their effectiveness in real-world situations requires further investigation.

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Machine Learning Approaches for Lightweight, Reliable, Generalizable, and Explainable Classification of Accelerometer-Based Measurements of Movements and Falls

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  • 发表日期：

    2025年

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  • Linda Greta Dui,
  • Elena Bardi,
  • Alberto Antonietti

摘要

This chapter presents and discusses the desired characteristics that the research community and users expect from machine learning (ML) approaches for the classification of motion-based activities of daily living (ADLs) using accelerometer measurements, with an emphasis on recognizing various types of falls. Falls are a major contributor to injuries and fatalities among the elderly population, highlighting the importance of real-time fall detection and alert systems. ML algorithms have demonstrated high accuracy in fall detection in controlled experimental environments. However, their effectiveness in real-world situations requires further investigation.