Machine Learning Recognition of Gait Identity via Shoe Embedded Accelerometer

Machine learning and data analytics are becoming pervasive also in the analysis of human behaviour as more and more miniaturized sensors can observe and quantify human activities. The so-called wearables are particularly tiny transducers which give the possibility to understand an individual's behaviour possibly enabling innovative services such as gait analysis based identification, foot pressure analysis, fall prevention and automatic recognition of dangerous situations. In this work we investigate an innovative experimental setting where accelerations are captured, during walking, by an Inertial Measurement Unit embedded in the shoe's sole. The goal is to identify who is wearing the shoe by simply analyzing his gait. User classification is then performed comparing different machine learning methods, relying either on the k-Neareast-Neighbour or on the Linear Discriminant Analysis algorithm. An extensive experimental campaign was carried out on five young adults and a comparative analysis of the accuracy of the methods proves that machine learning recognition of gait identity via shoe embedded accelerometer is feasible and sufficiently reliable.