A method for human behavior identification based on integrated sensor data using XGBoost classifier with PCA techniques

Abstract

Abstract In recent decades, there has been a noticeable increase in the recognition among professionals of the importance of human acts. The identification of human activity has gained significant prominence because of its wide-ranging applications in several domains, including healthcare and recreational activities. Moreover, a substantial body of research in the field of machine learning has been dedicated to the development of methodologies aimed at automating the identification and analysis of human behavior. This increased interest is mostly due to the fact that there are now more tools that can collect information about how people live their daily lives. The data utilized in this study is readily available for public access on the Internet. The data set under consideration comprises sensor readings from several components integrated inside the smartphone, including the global positioning system (GPS), accelerometer, magnetometer, and gyroscope. The data sets are indifferent to the categories, positions, or alignments of the items. The data set is of considerable magnitude due to its collection from several sensors, including GPS, accelerometer, magnetometer, and gyroscope. Consequently, we are employing the Principal Component Analysis (PCA) technique to diminish the dimensionality of the data and enhance its precision. Our recommendation is to utilize the XGBoost classifier in combination with Principal Component Analysis (PCA). The recommended model had a total identification rate of 97.58%. In order to demonstrate the flexibility of the proposed method, we employ a 10-fold cross-validation technique, together with the utilization of a confusion matrix and ROC curve. These evaluation metrics serve to provide a tangible illustration of the recommended strategy. The proposed methodology might have encouraging outcomes in effectively discerning human behaviors, thereby potentially facilitating the restoration of walking and pushing gaits into a Bipedal Robot and other Parkinson’s diseases.