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

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.