Design of Portable Wireless Fall Monitoring and Warning System

Authors

  • Hao Li
  • Yaxiong Gu

DOI:

https://doi.org/10.54097/ajst.v2i3.1491

Keywords:

Fall detection, Wearable devices, Discrete Fourier Transform, Support Vector Machine.

Abstract

Aiming at preventing the old man falling down in the field of study, this article studies the status quo, focusing on sitting down research and analyze the behavior characteristic, this paper proposes a portable wireless fall monitoring and alarm system scheme design and manufacture, the conventional time-domain motion data multilevel threshold detection algorithm combined with combined with frequency domain characteristics of machine learning classification has realized the real-time identification of fall behavior. The time domain data used rectangular window filter to preserve the critical time series of behavior occurrence, fully considering the time characteristics of data changes, and completed the detection of lying posture and falling and the exclusion of daily low-intensity activities of the elderly. In the frequency domain, Discrete Fourier Transform (DFT) was applied to the combined acceleration data of sitting posture fall and moderate intensity activity, and the Support Vector Machine (SVM) classifier model was established by using frequency domain features. Complete the recognition and detection of sitting posture fall, and complement the detection of lying posture fall. After the decision of falling is made, the information of falling will be sent to the guardian of the elderly in time through wireless communication module, and the on-site alarm will be made at the same time, so that the elderly can get help from others in time when there are people nearby.

References

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Dean M Karantonis,Michael R Narayanan,Merryn Mathie,et al. Implementation of a Real-Time Human Movement Classifier Using a Triaxial Accelerometer for Ambulatory Monitoring [J]. IEEE Transactions on Information Technology in Biomedicine, 2006,10 (1):156-167.

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Published

1 September 2022

How to Cite

Li, H., & Gu, Y. (2022). Design of Portable Wireless Fall Monitoring and Warning System. Academic Journal of Science and Technology, 2(3), 56–61. https://doi.org/10.54097/ajst.v2i3.1491

Issue

Section

Articles