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Continuously and unobtrusively identifying the phone's owner using accelerometer sensing and gait analysis has a great potential to improve user experience on the go. However, a number of challenges, including gait modeling and training data acquisition, must be addressed before unobtrusive gait verification is practical. In this paper, we describe a gait verification system for mobile phone without any assumption of body placement or device orientation. Our system uses a combination of supervised and unsupervised learning techniques to verify the user continuously and automatically learn unseen gait pattern from the user over time. We demonstrate that it is capable of recognizing the user in natural settings. We also investigated an unobtrusive training method that makes it feasible to acquire training data without explicit user annotation.
Lu et al. (Sat,) studied this question.