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Kowshik Gudimetla
Coimbatore, Tamil Nadu
Improved Elderly Fall Detection System - Wireless and Compact
We have built an Elderly fall detection system to detect the fall - Real time. It is a compact device and wireless too. Inside the room and also in the bathrooms, we shall detect the fall and alert the care takers immediately. ...learn more
Project status: Published/In Market
Overview / Usage
The potential of IoT is much appreciated these days in the healthcare sector. One area where we used IoT is elderly healthcare. It is a very interesting product that we built; with IoT, Sensors and data analytics playing a major role in it. Let us understand why we need this product in the first place before we dive deeper into the technical aspects. NCOA, expanded as National Council On Aging says that, rate of deaths in the elderly population has reached a critical state and once in 11 seconds an elderly person is being treated for the fall. The shock is not over yet. Once in 19 minutes, a death happens in the elderly population. Most of the old aged people are suffering from back pain, joint pain, knee pain and they mostly are the ones who aren’t able to walk properly and are mostly constrained to the bed. Falls are indications to some critical problems like immobility, fragility of bones and chronic health impairment. Falls in elders vary from the falls in kids, as the healing power in the old people slackens with their age. Falls are going to play a crucial role in the health line of the old people. Due to the falls there will be a huge impact even on the heart. Old people with heart diseases when fall suddenly, there is a risk of being affected by Tachyarrhythmia which is typically a heart disorder in which the heartbeat raises to an abnormal rate of more than 100 beats per minute which could lead to a cardiac arrest. So, in order to reduce these types of risks in elderly people, we present a frugal and affordable system that could monitor the motion of the old people and can detect their fall. Detection is not done before the fall or when person remains in rest state, it detects immediately after the fall and alerts the concerned persons to do the necessary actions to save the person. We use IoT, sensors and data analytics to build this system. We have not only detected the fall in the bedrooms for the elderly, but have also detected the falls even inside bathrooms, where the elderly people would not use the wearable we proposed.
Methodology / Approach
The accelerometer (GY-61) sensor and gyro (MPU-6050) sensor are attached to the proposed wearable. The wearable has to be used on both the hands. It can be worn at any comfortable place in the hands of the elderly people. This can be used just like wearing a watch. Having measured the angle change of GY-61 and MPU-6050 with various deliberate falls, we have identified the threshold value for each of these sensors. The data acquired from these sensors is transmitted via Bluetooth node (HC-05) which in turn is connected to the microcontroller unit. Here, we use Arduino UNO R3. The server receives the data and a graph shall be plotted for further analysis. During analysis it can be observed if the data goes above the determined threshold. The moment the threshold value is crossed, the call shall be triggered to the care taker with an alert message also being sent to the registered mobile number. As an added alert mechanism, a buzzer is also raised to get immediate attention to the fallen elder. All these happen with IoT, Sensors and Data analytics. All the three domains make this possible.
Coming to another feature of the product, when there is a nature’s call, the elderly person will obviously remove the wearable and go to the bathroom. If they fall inside the bathroom, it goes unnoticed. IoT and sensors will come into play here and will act as a helping hand.
To enable the fall tracking inside the bathrooms, two IR sensors are placed at the entrance of the bathroom. Also, piezoelectric plates are implanted at different places of the bathroom just like tiles. When there is a fall, piezo will generate the data. This data is sent to the server via Bluetooth. When the person enters the bathroom both of these sensors detects the entry and stops the incoming data from the wearable and starts transmitting the data from the piezoelectric plates via Bluetooth. The moment when a person falls on the floor where piezo is implanted, it would trigger the same alert signal and thereby the fall inside the toilet or bathroom can also be tracked, without giving a chance for failure of the proposed system.
Technologies Used
IoT, Aruduino, Sensors and Data Analytics.
Repository
Collaborators