Quantifying and recognizing human movement patterns from monocular video images-part II: applications to biometrics

Biometric authentication of gait, anthropometric data, human activities, and movement disorders are presented in this paper using the continuous human movement recognition (CHMR) framework introduced in Part I. A novel biometric authentication of anthropometric data is presented based on the realization that no one is average-sized in as many as ten dimensions. These body part dimensions are quantified using the CHMR body model. Gait signatures are then evaluated using motion vectors, temporally segmented by gait dynemes, and projected into a gait space for an eigengait-based biometric authentication. Left-right asymmetry of gait is also evaluated using robust CHMR left-right labeling of gait strides. Accuracy of the gait signature is further enhanced by incorporating the knee-hip angle-angle relationship popular in biomechanics gait research, together with other gait parameters. These gait and anthropometric biometrics are fused to further improve accuracy. The next biometric identifies human activities which require a robust segmentation of the many skills encompassed. For this reason, the CHMR activity model is used to identify various activities from making coffee to using a computer. Finally, human movement disorders were evaluated by studying patients with dopa-responsive Parkinsonism and age-matched normals who were videotaped during several gait cycles to determine a robust metric for classifying movement disorders. The results suggest that the CHMR system enabled successful biometric authentication of anthropometric data, gait signatures, human activities, and movement disorders.