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• Background

  Lefteris Kyriakakis • 08/02/2017 at 13:01 • 0 comments

  Falls are a clinical attribute of many diseases such as parkinson, heart failures, acoustic nerve defects and various movement dissabilities. More commonly, falls are a common occurance in elder people, according to the International Health Association approximately 28-35% of people above 65 years of age fall every year. Therefore, the research and development of movement classification, fall-detection and fall-prevention integrated systems, consists a necessicity for society to explore.

  This project aims to develop a wireless distributed monitoring solution that can be deployed in various enviroments and help in the immediate assistance of fall events both as a means of detection as well as a means of locating the person in need.
  

  The subject of fall-detection and movement classification was closely investigated by the co-author of this project as part of his BSc Thesis presented in: A Low Cost Open Architecture Fall Detection System Implemented as a WSN.

  Fig. 1 presents mixed movement patient data collected from two nodes in a WSN and successfully analyzed and classified in a custom MATLAB application:

  Figure 1. Mixed movements data and classification results from two nodes in real-time

  The subject of wireless node localization was investigated by the author of this project as part of his own BSc Thesis: Localization of a Mobile Node in a Wireless Sensor Network: an Evaluation of RSSI as a Distance Metric.

  Fig. 2 presents data collected from a mobile node, while static, 4 meters away from the central node.
  

  Figure 2. Static mobile node measurements at 4 meters from server node.

  In conclusion, this project aims to combine and advance the results of the authors 's background in order to develop a prototype deployable monitoring solution.
  
  

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