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NABNet: Deep Learning-Based IoT Alert System for Detection of Abnormal Neck Behavior.

Hongshuai Qin1, Minya Cai1, Huibin Qin1

  • 1School of Computer Science, Hangzhou Dianzi University, Hangzhou 310018, China.

Sensors (Basel, Switzerland)
|August 29, 2024
PubMed
Summary

Prolonged electronic device use causes neck issues. A new deep learning model, NABNet, detects abnormal neck behavior using pose estimation and object detection, achieving 94.13% accuracy for early intervention.

Keywords:
IoT systemYOLOv5sabnormal behavior detectionlightweight modelpose estimation

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Area of Science:

  • Biomedical Engineering
  • Computer Science
  • Health Informatics

Background:

  • Sedentary lifestyles and prolonged electronic device use contribute to neck pain and pressure injuries.
  • Early detection and correction of abnormal neck posture are crucial to prevent serious physical health risks.
  • Existing generic object detectors fail to identify subtle, detrimental neck behaviors.

Purpose of the Study:

  • To develop a deep learning-based system for detecting abnormal neck behavior.
  • To propose NABNet, a novel model integrating YOLOv5s object detection and Lightweight OpenPose for detailed neck behavior analysis.
  • To enable remote monitoring and real-time alarming for abnormal neck postures via an IoT system.

Main Methods:

  • Developed NABNet by combining YOLOv5s for object detection and Lightweight OpenPose for pose estimation.
  • Extracted detailed neck behavior characteristics from global to local perspectives.
  • Analyzed neck angles to detect abnormal behavior patterns.
  • Deployed the NABNet-based system on cloud and edge devices for a comprehensive IoT solution.

Main Results:

  • The NABNet system demonstrated effective detection of abnormal neck behavior.
  • The system successfully raised alarms on a cloud platform.
  • Achieved a highest accuracy rate of 94.13% in detecting abnormal neck postures.
  • Validated the effectiveness of the IoT system for real-time monitoring.

Conclusions:

  • The proposed NABNet model offers a robust solution for identifying detrimental neck behaviors.
  • The integrated IoT system provides a practical approach for remote monitoring and early intervention.
  • This technology has the potential to mitigate health risks associated with prolonged screen time.