Smartphones have ubiquitously integrated into our home and work environments, however, users normally rely on explicit but inefficient identification processes in a controlled environment. Therefore, when a device is stolen, a thief can have access to the owner's personal information and services against the stored password s. As a result of this potential scenario, this work demonstrates the possibilities of legitimate user identification in a semi-controlled environment through the built-in smartphones motion dynamics captured by two different sensors. This is a two-fold process: sub-activity recognition followed by user impostor identification. Prior to the identification; Extended Sammon Projection (ESP) method is used to reduce the redundancy among the features. To validate the proposed system, we first collected data from four users walking with their device freely placed in one of their pants pockets. Through extensive experimentation, we demonstrate that together time and frequency domain features optimized by ESP to train the wavelet kernel based extreme learning machine classifier is an effective system to identify the legitimate user or an impostor with (97 ) accuracy.
Moreover, the idea behind the behavior-based model is that the person's habits are a set of its attributes; therefore, each event (activity) has a correlation between two fundamental attributes: space and time. In addition, the architecture proposed in @cite_27 , utilizes the resources found in the smartphone devices, such as user calls, user schedule, GPS, device battery level, user applications, and sensors. A similar methodology has also been adopted in @cite_45 . Clarke, et al @cite_32 proposed smartphone's user perception of identification, in which results showed the system implicitly and continuously performing user identification in the background. Koreman, et al @cite_1 recommended continuous multiple model-based approaches for user identification. Mantyjarvi, et al @cite_38 used an accelerometer in television remote controls to identify individuals. Gafurov, et al @cite_54 and Cuntoor. et al @cite_49 suggested an experimented user identification using gait analysis and recognition. Jakobsson, et al @cite_24 put forward another unique implicit user identification framework by using recorded phone call history and location for continuous user identification.
@cite_38 @cite_54 @cite_1 @cite_32 @cite_24 @cite_27 @cite_45 @cite_49
1988545169 2116274755 1980481605 2002261403
In this work, we discover the daily location-driven routines that are contained in a massive real-life human dataset collected by mobile phones. Our goal is the discovery and analysis of human routines that characterize both individual and group behaviors in terms of location patterns. We develop an unsupervised methodology based on two differing probabilistic topic models and apply them to the daily life of 97 mobile phone users over a 16-month period to achieve these goals. Topic models are probabilistic generative models for documents that identify the latent structure that underlies a set of words. Routines dominating the entire group's activities, identified with a methodology based on the Latent Dirichlet Allocation topic model, include “going to work late”, “going home early”, “working nonstop” and “having no reception (phone off)” at different times over varying time-intervals. We also detect routines which are characteristic of users, with a methodology based on the Author-Topic model. With the routines discovered, and the two methods of characterizing days and users, we can then perform various tasks. We use the routines discovered to determine behavioral patterns of users and groups of users. For example, we can find individuals that display specific daily routines, such as “going to work early” or “turning off the mobile (or having no reception) in the evenings”. We are also able to characterize daily patterns by determining the topic structure of days in addition to determining whether certain routines occur dominantly on weekends or weekdays. Furthermore, the routines discovered can be used to rank users or find subgroups of users who display certain routines. We can also characterize users based on their entropy. We compare our method to one based on clustering using K-means. Finally, we analyze an individual's routines over time to determine regions with high variations, which may correspond to specific events. In this paper we present the uDirect algorithm as a novel approach for mobile phone centric observation of a user's facing direction, through which the device and user orientations relative to earth coordinate are estimated. While the device orientation estimation is based on accelerometer and magnetometer measurements in standing mode, the unique behavior of measured acceleration during stance phase of a human's walking cycle is used for detecting user direction. Furthermore, the algorithm is independent of initial orientation of the device which gives the user higher space of freedom for long term observations. As the algorithm only relies on embedded accelerometer and magnetometer sensors of the mobile phone, it is not susceptible to shadowing effect as GPS. In addition, by performing independent estimations during each step of walking the model is robust to error accumulation. Evaluating the algorithm with 180 data samples from 10 participates has empirically confirmed the assumptions of our analytical model about the unique characteristics of the human stance phase for direction estimation. Moreover, our initial inspection has shown a system based on our algorithm outperforms conventional use of GPS and PCA analysis based techniques for walking distances more than 2 steps. The rapid deployment of sensing technology in smartphones and the explosion of their usage in people's daily lives provide users with the ability to collectively sense the world. This leads to a growing trend of mobile healthcare systems utilizing sensing data collected from smartphones with without additional external sensors to analyze and understand people's physical and mental states. However, such healthcare systems are vulnerable to user spoofing attacks, in which an adversary distributes his registered device to other users such that data collected from these users can be claimed as his own to obtain more healthcare benefits and undermine the successful operation of mobile healthcare systems. Existing mitigation approaches either only rely on a secret PIN number (which can not deal with colluded attacks) or require an explicit user action for verification. In this paper, we propose a user verification scheme leveraging unique gait patterns derived from acceleration readings in mobile healthcare systems to detect possible user spoofing attacks. Our framework exploits the readily available accelerometers embedded within smartphones for user verification. Specifically, our user spoofing attack mitigation scheme (which consists of three components, namely Step Cycle Identification, Step Cycle Interpolation, and Similarity Score Computation) is used to extract gait patterns from run-time accelerometer measurements to perform robust user verification under various walking speeds. Our experiments using 322 smartphone-based traces over a period of 6 months confirm that our scheme is highly effective for detecting user spoofing attacks. This strongly indicates the feasibility of using smartphone based low grade accelerometer to conduct gait recognition and facilitate effective user verification without active user cooperation. A variety of real-life mobile sensing applications are becoming available, especially in the life-logging, fitness tracking and health monitoring domains. These applications use mobile sensors embedded in smart phones to recognize human activities in order to get a better understanding of human behavior. While progress has been made, human activity recognition remains a challenging task. This is partly due to the broad range of human activities as well as the rich variation in how a given activity can be performed. Using features that clearly separate between activities is crucial. In this paper, we propose an approach to automatically extract discriminative features for activity recognition. Specifically, we develop a method based on Convolutional Neural Networks (CNN), which can capture local dependency and scale invariance of a signal as it has been shown in speech recognition and image recognition domains. In addition, a modified weight sharing technique, called partial weight sharing, is proposed and applied to accelerometer signals to get further improvements. The experimental results on three public datasets, Skoda (assembly line activities), Opportunity (activities in kitchen), Actitracker (jogging, walking, etc.), indicate that our novel CNN-based approach is practical and achieves higher accuracy than existing state-of-the-art methods.