Adaptive HAR System to Improve Recognition Accuracy

Abstract

HAR (Human Activity Recognition) system becomes complex, inefficient and less accurate as we keep on adding new activities into the system; because it follows a specific procedure for activity recognition, from raw data collection to classification. In this study, we discuss an adaptive system to improve recognition accuracy. We developed a mathematical model to categorize the activities based on their data pattern. It observed that as we group the activities; although a separate classification model is required for each group, but it increases the recognition accuracy and efficiency of the system. The experiments on the data of eleven activities gathered from 10 volunteers proved the usability, scalability and effectiveness of our proposed methodology. The recognition accuracy of eleven activities was increased in total about 9-37% and reached up to 90% in different cases, using different number of groups and classification algorithms.