University of South Florida

Ravichandran Subramanian

Miguel A Labrador

Sudeep Sarkar

Aspects of gait matching are described herein. In one embodiment, a method of gait matching includes identifying a gait cycle timing in a data sequence captured by an inertial sensor, splitting the data sequence into a gait cycle segment, resampling the gait cycle segment, and estimating a rotation matrix for the resampled gait cycle segment. The rotation matrix, in one embodiment, can be estimated by minimizing a root mean squared deviation between the resampled gait cycle segment and a reference gait cycle segment. The method further includes calculating an aligned gait cycle segment from the resampled gait cycle segment using the rotation matrix, and generating a similarity measure between the aligned gait cycle segment and the reference gait cycle segment.

Orientation invariant gait matching

Aspects of gait matching are described herein. In one embodiment, a method of gait matching includes identifying a gait cycle timing in a data sequence captured by an inertial sensor, splitting the data sequence into a gait cycle segment, resampling the gait cycle segment, and estimating a rotation matrix for the resampled gait cycle segment. The rotation matrix, in one embodiment, can be estimated by minimizing a root mean squared deviation between the resampled gait cycle segment and a reference gait cycle segment using the Kabsch algorithm, for example. The method further includes calculating an aligned gait cycle segment from the resampled gait cycle segment using the rotation matrix, and generating a similarity measure between the aligned gait cycle segment and the reference gait cycle segment. The Kabsch algorithm can be used to minimize error and reduce identity authentication errors.

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Tampa, FL, United States of America

Ravichandran Subramanian