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This paper gives an insight about the influence of different walking speeds (slow, normal and fast) and surfaces (flat carpeted, grass, gravel and inclined) on gait recognition. Gait recognition is a type of biometric authentication that operates on behavioral characteristics of human beings. This research utilizes wearable sensors, and we have used a commercially available mobile device. Gait data...
Accelerometer-based biometric gait recognition offers a convenient way to authenticate users on their mobile devices. Modern smartphones contain in-built accelerometers which can be used as sensors to acquire the necessary data while the subjects are walking. Hence, no additional costs for special sensors are imposed to the user. In this publication we extract several features from the gait data and...
Support Vector Machines (SVMs) and Hidden Markov Models (HMMs) have been in use for numerous classification tasks in pattern recognition. HMMs can be considered as a de-facto standard in speaker recognition. For accelerometer-based biometric gait recognition these methods have also shown good classification results, which are, however, not comparable as different data sets and features have been used...
The goal of our research is to develop methods for accelerometer-based gait recognition, which are robust, stable and fast enough to be used for authentication on mobile devices. To show how far we are in reaching this goal we developed a new cycle extraction method, implemented an application for android phones and conducted a scenario test. We evaluated two different methods, which apply the same...
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