Description

• Title:
  Research on Fall Detection During Elderly Exercise Activities Under the Background of Healthy China Initiative
• Creator:
  Liu, Yongsen;
  Zhang, mufan;
  Ji, Bin
• Description:
  For the elderly are easy to fall and cause accident frequently in sports, fall detection is of much importance. this paper presents a method of detecting movement fall, based on the acceleration signals and attitude angle of human activity t acquired from a triaxial accelerometer sensor MPU6050 module, and protect the elderly from the second injury after the fall. By extracting the characteristics of the acceleration signal amplitude vector SMA and the deflection angle θ. This algorithm divides the movement state of the elderly into three categories, recurrent physical exercise and short-term daily activities and post-exercise rest state, using Signal Magnitude Vector Sliding Average (SVMLS) to distinguish fast running and jumping, and studying the program to detect the fall of the elderly after the exercising. immediately after exercise to avoid casualties after the fall. The major advantage of this method is that it can detect the violent fall and slow fall and its applicability is strong. Experiments show that the accurate alarm rate of the device is 95%, meet the fall detection accuracy.
• Object availability:
  free access
• Rights:
  Prawa autorskie (c) 2025 Yongsen Liu, Mufan Zhang, Bin Ji;
  https://creativecommons.org/licenses/by-nc-sa/4.0
• Date:
  2025.01.30
• Type:
  info:eu-repo/semantics/article;
  info:eu-repo/semantics/publishedVersion
• Language:
  English
• Source:
  Quality in Sport; Tom 37 (2025); 57818;
  Quality in Sport; Vol. 37 (2025); 57818;
  2450-3118
• Format:
  application/pdf
• Publisher:
  University Center for Sports Research, Nicolaus Copernicus University, Torun, Poland
• Relation: https://apcz.umk.pl/QS/article/view/57818/41328
• Subject:
  tri-axial accelerometer;
  fall detection for the elderly;
  Signal Magnitude Vector SMA;
  attitude angle
• Identifier: https://apcz.umk.pl/QS/article/view/57818
• Data provider:
  Akademicka Platforma Czasopism
• Can I use it?:
  seek permission
• Type:
  text