A multivariate Singular Spectrum Analysis approach to clinically-motivated movement biometrics

Abstract

Biometrics are quantities obtained from analyses of biological measurements. For human based biometrics, the two main types are clinical and authentication. This paper presents a brief comparison between the two, showing that on many occasions clinical biometrics can motivate for its use in authentication applications. Since several clinical biometrics deal with temporal data and also involve several dimensions of movement, we also present a new application of Singular Spectrum Analysis, in particular its multivariate version, to obtain significant frequency information across these dimensions. We use the most significant frequency component as a biometric to distinguish between various types of human movements. The signals were collected from triaxial accelerometers mounted in an object that is handled by a user. Although this biometric was obtained in a clinical setting, it shows promise for authentication.