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Design and Analysis for Fall Detection System Simplification
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Machine Learning-Based Framework for Pre-Impact Same-Level Fall and Fall-from-Height Detection in Construction Sites

Oleksandr Yuhai1, Yubin Cho1, Joung Hwan Mun1

  • 1Department of Bio-Mechatronic Engineering, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon 16419, Republic of Korea.

Biosensors
|September 26, 2025
PubMed
Summary
This summary is machine-generated.

Researchers developed a machine learning system to accurately detect same-level-falls (SLFs) and falls-from-height (FFHs) using a waist-mounted sensor. This technology enables rapid pre-impact detection for advanced fall-prevention devices in construction.

Keywords:
construction safetyensemble feature selectiongradient-boosted decision treespre-impact fall detectionwearable inertial measurement unit

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Area of Science:

  • Occupational Safety and Health
  • Biomedical Engineering
  • Machine Learning Applications

Background:

  • Same-level-falls (SLFs) and falls-from-height (FFHs) are significant causes of severe injuries and fatalities in construction.
  • Accurate detection of these falls is crucial for developing effective fall-prevention systems, but current methods struggle with false positives in dynamic construction environments.
  • Wearable fall-prevention devices require rapid and precise pre-impact detection capabilities to be effective.

Purpose of the Study:

  • To establish a machine learning-based approach for accurate identification of SLFs, FFHs, and non-fall events.
  • To utilize data from a single waist-mounted inertial measurement unit (IMU) for fall detection.
  • To develop a system capable of rapid, pre-impact detection suitable for wearable fall-prevention technologies.

Main Methods:

  • Collected data from 48 participants performing various non-fall activities, SLFs, and FFHs, using a dummy for higher falls.
  • Employed a two-stage feature extraction process to generate 168 descriptors per data window.
  • Utilized an ensemble SHAP-PFI method to select the 153 most informative variables and a weighted XGBoost classifier optimized via Bayesian techniques.

Main Results:

  • The optimized XGBoost classifier achieved a high average macro F1-score of 0.901 and macro Matthews correlation coefficient of 0.869.
  • The system demonstrated low latency (1.51 × 10-3 ms per window) and achieved average lead times of 402 ms for SLFs and 640 ms for FFHs.
  • These lead times significantly exceed the 130 ms inflation time required for wearable airbags, indicating effective pre-impact detection.

Conclusions:

  • The developed machine learning approach provides rapid and precise detection of SLFs and FFHs using a single IMU.
  • This pre-impact detection capability positions the system as a viable core component for advanced wearable fall-prevention devices.
  • The findings offer a promising solution for enhancing worker safety in high-risk construction environments.