Activity With Gender Recognition Using Accelerometer and Gyroscope

Recently, the use of the inertia measurement units (IMU), especially the gyroscope and accelerometer sensors, has increased in the human activity recognition (HAR) due to the extensive use of smartwatches and smartphones. In addition to the high quality and efficiency result in by these sensors, they can capture the data of the body dynamic motion as function of time, then the stream of data is analyzed and processed to classify and predict the action being done, the gender, the health status and many other characteristics. Gender and activity recognition have been deeply studied recently, using various ways to recognize either of them through many interfaces, like voice, image, or inertia measurement motion data. All these types of classifications are crucial in many applications such as recommendation systems, speech recognition, sports tracking, security and most importantly in healthcare. In this research, we present two models (hierarchical model and joint distribution model) and compare between two datasets (MoVi and MotionSense), using only two IMU sensors on right and left hand and motion sense dataset using mobile phone, to predict gender with activity and see how every activity reflect on gender, and explore the efficiency on using autocorrelation function as a feature extractor and compare between three classifiers, Random Forest (RF), Support Vector Machine (SVM) and Convolution Neural Network (CNN).