Classification of Human Motion Activities using Mobile Phone Sensors and Deep Learning Model

Abstract

Recently, human activity recognition (HAR) has gained a lot of importance due to its wide range of applications in virtual reality, healthcare, surveillance, security, automated control systems, etc. Latest mobile phones have advanced computational capabilities along with several embedded MEMS sensors, which enable us to detect various physical activities unobtrusively. Incorporating personalized motion samples can improve the accuracy of motion detection by mobile devices or wearable devices that are tailored to the individual. Recent works have demonstrated that the use of machine learning and statistical techniques can detect human activities more accurately. In this paper, we have classified two different physical activities, viz., walking and brisk-walking using a deep neural network (DNN). The personalized data has been collected using multiple sensors of the mobile phone with the two kinds of physical activity. Using mobile phone sensors like accelerometer, gyroscope, magnetometer, etc, data has been collected, examined, and used for training and testing the DNN model. Out of multiple sensors on the phone, we have identified the sensors which are more appropriate for the given motion/activities. Finally, we have achieved a classification accuracy of 96.5%.