This work investigates how context should be taken into account when performing continuous authentication of a smartphone user based on touchscreen and accelerometer readings extracted from swipe gestures. The study is conducted on the publicly available HMOG dataset consisting of 100 study subjects performing pre-defined reading and navigation tasks while sitting and walking. It is shown that context-specific models are needed for different smartphone usage and human activity scenarios to minimize authentication error. Also, the experimental results suggests that utilization of phone movement improves swipe gesture-based verification performance only when the user is moving.
The preliminary studies @cite_11 @cite_2 @cite_10 @cite_3 have demonstrated that application or task specific (phone usage context) modelling can indeed boost the performance of swipe-based CA, while only marginal improvement has been achieved when human activity context has been considered @cite_3 . So far, human activity context-based models have shown to be useful only when CA is performed based on solely phone movement patterns @cite_8 or combined with typing (tap gestures) @cite_7 .
@cite_7 @cite_8 @cite_3 @cite_2 @cite_10 @cite_11
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It is well documented that interacting with a mobile phone is associated with poorer performance on concurrently performed tasks because limited attentional resources must be shared between tasks. However, mobile phones generate auditory or tactile notifications to alert users of incoming calls and messages. Although these notifications are generally short in duration, they can prompt task-irrelevant thoughts, or mind wandering, which has been shown to damage task performance. We found that cellular phone notifications alone significantly disrupted performance on an attention-demanding task, even when participants did not directly interact with a mobile device during the task. The magnitude of observed distraction effects was comparable in magnitude to those seen when users actively used a mobile phone, either for voice calls or text messaging. (PsycINFO Database Record Keywords: Driver distraction; Language: en Background: Ecological momentary assessment (EMA) is a useful method to tap the dynamics of psychological and behavioral phenomena in real-world contexts. However, the response burden of (self-report) EMA limits its clinical utility. Objective: The aim was to explore mobile phone-based unobtrusive EMA, in which mobile phone usage logs are considered as proxy measures of clinically relevant user states and contexts. Methods: This was an uncontrolled explorative pilot study. Our study consisted of 6 weeks of EMA unobtrusive EMA data collection in a Dutch student population (N=33), followed by a regression modeling analysis. Participants self-monitored their mood on their mobile phone (EMA) with a one-dimensional mood measure (1 to 10) and a two-dimensional circumplex measure (arousal valence, –2 to 2). Meanwhile, with participants’ consent, a mobile phone app unobtrusively collected (meta) data from six smartphone sensor logs (unobtrusive EMA: calls short message service (SMS) text messages, screen time, application usage, accelerometer, and phone camera events). Through forward stepwise regression (FSR), we built personalized regression models from the unobtrusive EMA variables to predict day-to-day variation in EMA mood ratings. The predictive performance of these models (ie, cross-validated mean squared error and percentage of correct predictions) was compared to naive benchmark regression models (the mean model and a lag-2 history model). Results: A total of 27 participants (81 ) provided a mean 35.5 days (SD 3.8) of valid EMA unobtrusive EMA data. The FSR models accurately predicted 55 to 76 of EMA mood scores. However, the predictive performance of these models was significantly inferior to that of naive benchmark models. Conclusions: Mobile phone-based unobtrusive EMA is a technically feasible and potentially powerful EMA variant. The method is young and positive findings may not replicate. At present, we do not recommend the application of FSR-based mood prediction in real-world clinical settings. Further psychometric studies and more advanced data mining techniques are needed to unlock unobtrusive EMA’s true potential. [J Med Internet Res 2016;18(3):e72] Recently, multitasking on smart phones during navigation has emerged as a problematic social behavior due to its potential danger. In this paper, we propose to alleviate the situation by employing a sensor system for obstacle detection and aid the multi-tasking user so that one can safely navigate and carry out the on-going secondary task as effectively as possible. As such, we have implemented an ultrasonic sensor system interfaced into the smart phone that can constantly appraise the user of the incoming obstacles. We ran experiments to validate our approach, checking whether such a system would help user bump less into obstacles than without, and observe their multitasking behavior such as the physical attentional switch. Our experiments have shown that with the aid of the sensor system, the user's attention switch was significantly reduced, however, there was no differences in the performance (e.g. no. of collision) because the momentary strategic spatial and path planning was quite effective with slow pace and light pedestrian traffic. We conclude therefore human dual or multitasking ability is sufficient to overlap "casual" video watching and "slow" navigation. However, with more stringent task, e.g. text messaging and heavier traffic, we expect the reduced attention shift to ultimately improve the performance as well. 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.