Classification methods performance on human activity recognition

Abstract

There are around 650 million people from all over the world who lived with disabilities. One of the fundamental rights of people with disabilities is the existence of a companion to supervise his activity. Meanwhile, the use of mobile phones for monitoring the activities of people with disabilities has been widely carried out. The human activity monitoring mobile application requires human activity recognition methods that provide high accuracy, precision, and recall to reduce the error rate of the activity estimation. Some researches use machine learning algorithms like K-Nearest Neighbour (k-NN) algorithm, Artificial Neural Networks (ANN), Support Vector Machine (SVM), and Random Forest for human activity recognition methods. However, the results of these studies have not been compared apples to apples. Therefore, this study presents a performance comparison of SVM, KNN, and Random Forest machine learning methods. Based on our findings, the SVM method with Support Vector Classifier (SVC) and Radial Basis Function (RBF) kernels can achieve the highest precision and recall, 87% and 85% respectively. The fastest processing time is obtained using the SVM method with the Stochastic Gradient Descent. However, in general, the best performance is shown by Random Forest. The Random Forest method with a depth of 100 and 300 trees can reach an accuracy of 96% within 0.45 minutes.