Human activity recognition with mobile phone sensors: Impact of sensors and window size.

With the help of the sensors available on smart phones and smart watches, inference on user context, particularly the activity, can be inferred. Raw data collected from the sensors enables the classification of human activities with machine learning algorithms. In studies focusing on mile activity recognition, usually the motion sensors, such as accelerometer and gyroscope are used. In this paper, our aim is to analyze the performance of activity classification when these sensors are used separately or in combination. By using a dataset which is collected from fifteen participants including six different activities, we extract various features from raw data and afterwards supervised machine learning algorithms are used to train and validate the results. Five different classifiers and different validation methods are used for performance analysis.