An Effective Deep Learning Framework for Fall Detection: Model Development and Study Design.

Journal: Journal of medical Internet research
PMID: 39102676

Abstract

BACKGROUND: Fall detection is of great significance in safeguarding human health. By monitoring the motion data, a fall detection system (FDS) can detect a fall accident. Recently, wearable sensors-based FDSs have become the mainstream of research, which can be categorized into threshold-based FDSs using experience, machine learning-based FDSs using manual feature extraction, and deep learning (DL)-based FDSs using automatic feature extraction. However, most FDSs focus on the global information of sensor data, neglecting the fact that different segments of the data contribute variably to fall detection. This shortcoming makes it challenging for FDSs to accurately distinguish between similar human motion patterns of actual falls and fall-like actions, leading to a decrease in detection accuracy.

Authors

  • Jinxi Zhang
    Beijing Kupei Sports Culture Corporation Limited, Beijing, China.
  • Zhen Li
    PepsiCo R&D, Valhalla, NY, United States.
  • Yu Liu
    Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Science, Beijing, China.
  • Jian Li
    Fujian Key Laboratory of Traditional Chinese Veterinary Medicine and Animal Health, College of Animal Science, Fujian Agriculture and Forestry University, Fuzhou, China.
  • Hualong Qiu
    Guangdong Provincial Key Laboratory of Silviculture, Protection and Utilization, Guangdong Academy of Forestry, Guangzhou, China.
  • Mohan Li
    College of Food Science Shenyang Agricultural University Shenyang China.
  • Guohui Hou
    Bioelectronics Center of YZW, Shanghai, China.
  • Zhixiong Zhou
    Institute of Artificial Intelligence in Sports, Capital University of Physical Education and Sports, Beijing, China.

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