A new approach to modelling and classification of human gait is proposed. Body movements are obtained using a sensor suit that records inertial signals that are subsequently modelled on a humanoid frame with 23 degrees of freedom (DOF). Measured signals include position, velocity, acceleration, orientation, angular velocity and angular acceleration. Using a range of concurrent features extracted from the sensor signals, a system using induced symbolic classification models, such as decision trees or rule sets, has been used for classification of identity. It is anticipated that this approach will also enable the identification of a variety of gestures. The feasibility of generating the identified behaviours in a humanoid robot will be explored. The approach is described and the characteristics of the algorithm are presented. The results obtained so far are reported and conclusions drawn.
A new modelling and classification approach for human gait evaluation is proposed. The body movements are obtained using a sensor suit recording inertial signals that are subsequently modelled on a humanoid frame with 23 degrees of freedom (DOF). Measured signals include position, velocity, acceleration, orientation, angular velocity and angular acceleration. Using the features extracted from the sensory signals, a system with induced symbolic classification models, such as decision trees or rule sets, based on a range of several concurrent features has been used to classify deviations from normal gait. It is anticipated that this approach will enable the evaluation of various behaviours including departures from the normal pattern of expected behaviour. The approach is described and the characteristics of the algorithm are presented. The results obtained so far are reported and conclusions are drawn.
The feasibility of generating a Dynamic FingerPrint (DFP) for an individual is explored. DFP is a unique signature generated based on a combination of body part movements. The body movements are obtained using a sensor suit recording inertial signals that are subsequently modeled on a humanoid frame with 23 degrees of freedom (DOF). Measured signals include position, velocity, acceleration, orientation, angular velocity and angular acceleration. DTW (Dynamic Time Warping) is XVHG WR FODVVLI\ WKH LQGLYLGXDO ¶V identity. The approach is described and the characteristics of the algorithms are presented. It is anticipated that these approaches will have applications in surveillance and security, medical science and animation modeling. Classification results show an accuracy rate of 100% for the 10 subjects studied during validation.
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