An Analysis of Human Activities Recognition using Smartwatches Dataset

Abstract

Today, the era of smart devices evolving the human behavior interaction to a changing environment where the learning of activities is monitored to predict the next step of human behavior. The smart devices have these sensors built-in (accelerometer and gyroscope), which are continuously generating a large amount of data. The data used to identify the novel patterns of human behavior, together with machine learning and data mining techniques. Classification of human motions with motion sensor data is among the current topics of study. The classification is an important part of data mining techniques and used in this work to find the accuracy of instances in the given dataset. Thus, it is possible to follow the activities of a user carrying only a smartwatch. The smartwatches consisting of four different models from two manufacturers are used. Furthermore, the experiment contains nine users and seven activities performed by them. After the classification was determined, the data set to which the principal component analysis has been applied was classified by decision stump, j48, Bayes net, naive Bayes, naive Bayes multinomial text, random forest, and logit boost methods, and their performances were compared. The most successful result was obtained from the random forest method. The accuracy of the Random Forest classification algorithm on nominal datasets is 99.99% on both accelerometer and gyroscope sensors.