Electromyogram-based algorithm using bagged trees for biometric person authentication and motion recognition

With the recent rising interest in health and convenience, bio-signal measurement, and sensor technology are developing rapidly. Using bio-signals, one can calculate motion and other variables, can authenticate individuals using fingerprints, and can control objects using gestures. These biosignals are convenient because they can be measured easily, anytime, anywhere. However, there are cases of misuse of biosignal based technology. To solve this problem, we develop an electromyogram (EMG)-based algorithm that can be used for biometric authentication. The proposed algorithm uses two channels to acquire EMG data, and hand motion recognition and authentication are carried out through signal processing. As a result of dividing 50 data sets into artificial neural networks and applying the ensemble technique, the authentication success rate achieved by the proposed algorithm was 83.8%.