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.