Human Activity Recognition and assisting user on the basis of his context is attracting researchers since decade Researchers are working in the area to increase the accuracy of detection by various means. The challenging issue is to determine the correct supervised classifier for the detection purpose. This paper intent to examine the methodology used to recognize HAR and the impact of classifiers practiced in training and Testing. We have also tried to identify the suitable supervised machine learning model for HAR. Data of 30 Users with 561 features belonging to accelerometer and gyroscope sensor of smartphone from UCI repository is used for evaluation purpose. Nine different supervised machine learning Models are trained and tested on the dataset. The result concludes that HAR is a process which depends upon the classifiers used. It also conclude that out of 9 different Machine learning models ANN performs well and after that SVM, kNN, Random Forest and Extra Tree are equally good models for the purpose of HAR with Accuracy and execution time as the performance evaluation metric.
Human activity recognition using 2D skeleton data and supervised machine learning