In recent years

In recent years, there is a drastic growth in the human population which resulted in huge cost of living and sophisticated jobs. Employees are unable to take care of their old parents, who mostly live in the country side. Because of this the old aged pupils are left unmonitored and often no one to care for them.
A study revealed that most of the elder people and old aged people die because of immediate response when they met with accidents. These accidents are of minor damage, but because of them not being monitored, even there small accidents at home are becoming a cause for their death.
By god’s grace there is a proportional growth in the field of science and technology. Scientists and engineers invented devices to monitor the people who are prone to these kinds of accidents. Most of the accidents commonly occur with a fall of the person. The devices in this case are known as fall detectors, which sense a fall of the person.
Fall detectors can be classified into different types depending on the sensors used, the algorithms involved, the method of detecting the fall, and few other factors.
Keywords: accelerometer, Arduino, Bluetooth, algorithm
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In a research paper “Automatic Detection of Human Fall in Video”, authored by Vinay Vishwakarma, IIT kharaghpur, the fall detector used was of video based. Human activity is captured and further analyzed using image processing techniques. The fall detector in this case used an adaptive background subtraction method using a Gaussian mixture model. Two fall models are used, they are: fall detection, fall conformation. It uses a two-state finite state machine for the detection and confirmation of fall.
In a research paper named “A Multi-Camera Vision System for Fall Detection
and Alarm Generation” authored by R. Cucchiara, University of Modena and Reggio Emilia introduced a multi camera vision system for fall detection, tracking people, recognizing dangerous behavior. In such situations a suitable alarm, can be sent by means of SMS. The multi camera system, objects are extracted from each single camera module by using background suppression.
In a research paper “Human Fall Detection Using Kinect Sensor” by Michal Kepski, a fall detector using kinect sensor is introduced. The device of concern takes depth reference images, and the distance between the person and the ground plane is calculated. RANSAC algorithm is used to calculate the ground plane’s depth. Hough transform, NITE based skeleton tracking is used.
In a research paper “A Simply Fall-Detection Algorithm Using Accelerometers on a Smartphone” by Ekachai Thammasat, Thailand Institute of Scientific and Technological Research a fall detector using the tri-axial accelerometer in the phone is introduced. Tri-axial accelerometer delivers the x, y; z coordinates of the device or indirectly that of the person to the microcontroller. So, when the person falls there is a spike generated in the x, y, and z axes, which then is detected by the system.


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