Gait identification using stochastic OXRRAM-based time sequence machine learning

Abstract

The way a person walks, i.e. his/her gait, can be as unique as a fingerprint. With portable accelerometers and/or gyroscopes available in present-day smartphones, gait verification and identification can be exploited for low-level security [1]. Achieving this requires machine learning of a time sequence.