With the recent development of microelectromechanical systems (MEMS), inertial sensors have become widely used in the research of wearable gait analysis due to several factors, such as being easy-to-use and low-cost. Considering the fact that each individual has a unique way of walking, inertial sensors can be applied to the problem of gait recognition where assessed gait can be interpreted as a biometric trait. Thus, inertial sensor-based gait recognition has a great potential to play an important role in many security-related applications. Since inertial sensors are included in smart devices that are nowadays present at every step, inertial sensor-based gait recognition has become very attractive and emerging field of research that has provided many interesting discoveries recently. This paper provides a thorough and systematic review of current state-of-the-art in this field of research. Review procedure has revealed that the latest advanced inertial sensor-based gait recognition approaches are able to sufficiently recognise the users when relying on inertial data obtained during gait by single commercially available smart device in controlled circumstances, including fixed placement and small variations in gait. Furthermore, these approaches have also revealed considerable breakthrough by realistic use in uncontrolled circumstances, showing great potential for their further development and wide applicability.

We propose a novel efficient and reliable gait authentication approach. It is based on the analysis of accelerometer signals using higher-order statistics. Gait patterns are obtained by transformation of acceleration data in feature space represented with higher-order cumulants. The proposed approach is able to operate on multi-channel and multi-sensor data by combining feature-level and sensor-level fusion. Evaluation of the proposed approach was performed using the largest currently available dataset OU-ISIR containing inertial data of 744 subjects. Authentication was performed by cross-comparison of gallery and probe gait patterns transformed in feature space. Additionally, the proposed approach was evaluated using dataset collected by McGill University, containing long-sequence acceleration signals of 20 subjects acquired by smartphone during casual walking. The results have shown an average EER of 6 % to 12 %, depending on the selected experimental parameters and setup. When compared to the latest state-of-the-art, evaluated performance reveal the proposed approach as one of the most efficient and reliable of the currently available accelerometerbased gait authentication approaches.

No abstract

No abstract

No abstract