Personal Identification Using Transfer Functions of Acceleration Between Body Parts

Abstract

This study develops and evaluates a method for personal identification using acceleration data. By employing transfer functions between different body parts, the method extracts individual-specific movement characteristics. Specifically, acceleration data from the left thigh to the right pelvis are used, and the transfer functions are calculated using the Z-transform. As a result, transfer functions unique to each individual is computed, enabling personal identification from a set of time-series patterns. This study presents a potentially novel approach to personal identification.