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Asian Journal of Scientific Research

Year: 2018 | Volume: 11 | Issue: 3 | Page No.: 344-356
Research Article

Development of Electronic Floor Mat for Fall Detection and Elderly Care

Viknesh Kumar
Faculty of Engineering and Technology, Multimedia University, Melaka, Malaysia
Boon-Chin Yeo
Faculty of Engineering and Technology, Multimedia University, Melaka, Malaysia
LiveDNA: 60.20931
Way-Soong Lim
Faculty of Engineering and Technology, Multimedia University, Melaka, Malaysia
Joseph Emerson Raja
Faculty of Engineering and Technology, Multimedia University, Melaka, Malaysia
Kim-Boon Koh
Acculex Sdn. Bhd., Melaka, Malaysia
Background and Objective: The risk of falls increases for elderly people who are aged 65 and above. Approximately one-third to one-half of the elderly people experience falls on a yearly basis. Falling can be a serious life-threatening event and there is a need to alert their nurses or family members instantly. The developed motion and fall detection is important to response immediate call for medical caregivers and consequently reducing the mortality rate. The objective for this project is to build a cost effective and user-friendly surveillance device. The primary objective of making such system is to track the motion or movement of elderly people in the house. Materials and Methods: Every motion on the mat will reflect to pressure data and collected via a special hardware designed with conductive grids that act like a switch, which is triggered upon pressure exerted over certain area. The pressure value is extracted from a pressure conductive material called velostat. The values are read by microcontroller. Results: The innovative mat will study user’s behaviour daily. Once the fall is detected, an alert message will be sent in the form of SMS and email. Apart from fall detection, surveillance system has been incorporated to support vision-based activation for the fall detection system and user access logging. Conclusion: This designed mat is a non-invasive device essential for detection of fall and monitoring. The developed system gives an accuracy of 80% in detecting a fall event for phase 1 detection and 90% in detecting a fall event for phase 2 detection.
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