Smartphone and Smartwatch for Human Activity Recognition

Abstract

Human activity recognition (HAR) systems are developed as aspect of a model to allow continual assessment of human behaviors in IoT environments in the areas of ambient assisted living, sports injury detection, elderly care, rehabilitation, and entertainment and close monitoring. Smartphones are already used to recognize activity. Most of the research done in this field placed a restriction on fixing the smartphone securely in a certain location on the human body, along with the machine learning system, to promote the process of classifying raw data from smartphone sensors to human activities. Smartwatches solve this limitation by placing them in a consistent position, which becomes steady and precisely sensitive to body movements. For this experiment, we evaluate both the accelerometer and the gyroscope sensor on the smartphone and the smartwatch, and decide which sensors hybrid does superiorly. Five daily physical human activities are evaluated using five classifiers from WEKA, in addition to Artificial Neural Network (ANN), K- Nearest Neighbor (KNN), and Support Vector Machine (SVM) algorithms builtin MATLAB 2018a. We used confusion matrix and random simulation to compare the accuracy and efficiency of those models. The results showed that the accelerometer sensors combination has the highest accuracy among other combinations and achieved an overall accuracy of 97.7% using SVM that gives the best performance among all other classifiers.