The able amble: gait recognition using Gaussian mixture model for biometric applications

With the advent of wearable devices and commonality of on-body monitoring devices, it is anticipated that a day will come in the future where body-area networks will become commonplace in our lives. It is envisioned that the whole process will be automated wherein a user wearing such a device automatically enables the security mechanism and establishes communication between that user and his/her surroundings. This paper addresses a technique to identify the wearer of the device by way of Gaussian Mixture Models (GMM), allowing for identification and verification before establishing communication. It suggests using gait as a metric for identity association using wearable sensors.