Article originally published International Journal of Machine Learning and ComputingSmartphones are widely used today, and it
becomes possible to detect the user's environmental changes by using the smartphone sensors, as demonstrated in this paper where we propose a method to identify human activities with
reasonably high accuracy by using smartphone sensor data. First, the raw smartphone sensor data are collected from two categories of human activity: motion-based, e.g., walking and running; and phone movement-based, e.g., left-right, up-down, clockwise and counterclockwise movement. Firstly, two types of features extraction are designed from the raw sensor data, and activity recognition is analyzed using machine learning classification models based on these features. Secondly, the
activity recognition performance is analyzed through the Convolutional Neural Network (CNN) model using only the raw data. Our experiments show substantial improvement in the result with the addition of features and the use of CNN model
based on smartphone sensor data with judicious learning techniques and good feature designs
