Gait authentication and identification using wearable accelerometer sensor

This paper describes gait recognition using a body worn sensor. An accelerometer sensor (placed in the trousers pocket) is used for collecting gait features. From the acceleration signal of the person, cycles have been detected and analysed for recognition. We have applied four different methods (absolute distance, correlation, histogram, and higher order moments) to evaluate performance of the system both in authentication and identification modes. Our data set consists of 300 gait sequences collected from 50 subjects. Absolute distance metric has shown the best performance in terms of EER, which is equal to 7.3% (recognition rate is 86.3%). Furthermore, we have also analysed recognition performance when subjects were carrying a backpack.