Human Fall Detection using Built-in Smartphone Accelerometer

Falls are serious health hazard issue among the aged people around the world. It's a common accident for the elderly people living alone. Obviously, this accident can be timely reduced by using accurate fall detection method in order to reduce injuries and loss of life. For this purpose, we used smartphone-based fall detection method using the features of triaxial acceleration values of x, y and z which is obtained from the built-in accelerometer sensor embedded on our smartphones. We do a lot of daily activities like sitting, walking, standing, lying, and running. These were collected through accelerometer data. An app was used called Physics Toolbox Sensor Suite to take the data values which consist of accelerometer. The data values were taken through two positions one in chest pocket and another in pant pocket for both falls and non-falls. Also, intentional falls were also taken like fall -forward, fall-backward, right lateral fall, left lateral fall and so on. All these data were collected together to distinguish between fall and non-fall. These falls and non-falls were submerged together in a given time set keeping its frequency fixed along 6000samples from each data set through MATLAB. Then by using the Neural Net Pattern Recognition app leads us solving data classification problem using two-layer feed forward network. Using our data, we trained, validated and test the data through Neural Network Pattern Recognition, and achieved our classification accuracy to 90.6%. Using 67 data consisting of 26 falls and 41 non-falls. Basically, we classified and predict the data's through offline activity recognition. Once the falling victim is detected his positions along its locations will be tracked. And instantly will send an alert to the caregivers for immediate assistance.