Development Of IoT Fall Detection System For Elderly People

Gurdir Singh, Amarjeet Singh (2019) Development Of IoT Fall Detection System For Elderly People. Project Report. Universiti Teknikal Malaysia Melaka, Melaka, Malaysia. (Submitted)

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Abstract

In the recent time, detection of fall has become one of the most important aspect due to increase in the living standards and population of the elderly people. Nowadays, many working professionals are leaving behind their elder family members at home alone. As the fact that elderly people has the higher chances of falling down and hurting themselves due to age factor and weak joins. Falls can be very dangerous among elderly people especially those who are living alone and cannot seek any help after it. Hereby, this development of IoT Fall Detection System will ensure that the fall can be detected and the victim can be treated well at the right time. When any type of indoor falls occur, it should be correctly detected and alert the family member to take immediate action. In this case, if an individual has fallen down, this prototype device which is attached to the victim’s hand will detect the fall and send notification to the family member to alert them. Furthermore, it has become extremely necessary to adopt the Internet of Things (IoT) technology to integrate with the fall detection system and receive the alert notification using the Cayenne app. The key components in developing this project are the Wemos D1 Mini, MPU 6050 sensor and Cayenne app. The input of the system is the accelerometer/gyroscope sensor with 6 axis integrated which will detect any change in body posture or motion of the particular person while Wemos D1 Mini will integrate with the Cayenne app to send notification via IoT to the mobile phone and stores relevant information

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