A New Approach for Human Recognition Through Wearable Sensor Signals

Abstract

Recently, subjects such as human recognition (HR), age estimation and gender recognition have been among the most investigated human-computer interaction topics, in both the academic and other fields. HR is a process in which a person is detected based on the obtained biometrical features. In this study, a new feature extraction method has been suggested through using the signals received from the sensors of the accelerometer, magnetometer and gyroscope attached to the 5 areas on the human body. The feature extraction from the signals is one of the most crucial stage. The reason behind the success of HR is based on the extracted features. However, the extraction of appropriate features for HR is a challenging issue. Various transformation methods like 1D-LBP and 1D-FbLBP have been applied to the sensor-based signals. Following the transformation process, the statistical features have been acquired from the newly developed signals. The classification processes have been carried out with the distinctive methods concerning machine learning (Knn, RF, A1DE, A2DE and ANN) by using these features. According to these results, 1D-LBP (88.4649%) and 1D-FbLBP (91.8281%) methods have been chosen to provide effective features for HR.