Smartphone apps usage patterns as a predictor of perceived stress levels at workplace

Explosion of number of smartphone apps and their diversity has created a fertile ground to study behaviour of smartphone users. Patterns of app usage, specifically types of apps and their duration are influenced by the state of the user and this information can be correlated with the self-reported state of the users. The work in this paper is along the line of understanding patterns of app usage and investigating relationship of these patterns with the perceived stress level within the workplace context. Our results show that using a subject-centric behaviour model we can predict stress levels based on smartphone app usage. The results we have achieved, of average accuracy of 75% and precision of 85.7%, can be used as an indicator of overall stress levels in work environments and in turn inform stress reduction organisational policies, especially when considering interrelation between stress and productivity of workers

On the Feature Discovery for App Usage Prediction in Smartphones

With the increasing number of mobile Apps developed, they are now closely integrated into daily life. In this paper, we develop a framework to predict mobile Apps that are most likely to be used regarding the current device status of a smartphone. Such an Apps usage prediction framework is a crucial prerequisite for fast App launching, intelligent user experience, and power management of smartphones. By analyzing real App usage log data, we discover two kinds of features: The Explicit Feature (EF) from sensing readings of built-in sensors, and the Implicit Feature (IF) from App usage relations. The IF feature is derived by constructing the proposed App Usage Graph (abbreviated as AUG) that models App usage transitions. In light of AUG, we are able to discover usage relations among Apps. Since users may have different usage behaviors on their smartphones, we further propose one personalized feature selection algorithm. We explore minimum description length (MDL) from the training data and select those features which need less length to describe the training data. The personalized feature selection can successfully reduce the log size and the prediction time. Finally, we adopt the kNN classification model to predict Apps usage. Note that through the features selected by the proposed personalized feature selection algorithm, we only need to keep these features, which in turn reduces the prediction time and avoids the curse of dimensionality when using the kNN classifier. We conduct a comprehensive experimental study based on a real mobile App usage dataset. The results demonstrate the effectiveness of the proposed framework and show the predictive capability for App usage prediction.Comment: 10 pages, 17 figures, ICDM 2013 short pape

Mining and Predicting Smart Device User Behavior

Three types of user behavior are mined in this paper: application usage, smart device usage and periodicity of user behavior. When mining application usage, the application installation, most frequently used applications and application correlation are analyzed. The application usage is long-tailed. When mining the device usage, the mean, variance and autocorrelation are calculated both for duration and interval. Both the duration and interval are long-tailed but only duration satisfies power-law distribution. Meanwhile, the autocorrelation of both duration and interval is weak, which makes predicting user behavior based on adjacent behavior not so reasonable in related works. Then DFT (Discrete Fourier Transform) is utilized to analyze the periodicity of user behavior and results show that the most obvious periodicity is 24 hours, which is in agreement with related works. Based on the results above, an improved user behavior predicting model is proposed based on Chebyshev inequality. Experiment results show that the performance is good in accurate rate and recall rate

Mfingerprint: Privacy-preserving user modeling with multimodal mobile device footprints

Abstract. Mobile devices collect a variety of information about their environments, recording "digital footprints" about the locations and activities of their human owners. These footprints come from physical sensors such as GPS, WiFi, and Bluetooth, as well as social behavior logs like phone calls, application usage, etc. Existing studies analyze mobile device footprints to infer daily activities like driving/running/walking, etc. and social contexts such as personality traits and emotional states. In this paper, we propose a different approach that uses multimodal mobile sensor and log data to build a novel user modeling framework called mFingerprint that can effectively and uniquely depict users. mFingerprint does not expose raw sensitive information from the mobile device, e.g., the exact location, WiFi access points, or apps installed, but computes privacy-preserving statistical features to model the user. These descriptive features obscure sensitive information, and thus can be shared, transmitted, and reused with fewer privacy concerns. By testing on 22 users' mobile phone data collected over 2 months, we demonstrate the effectiveness of mFingerprint in user modeling and identification, with our proposed statistics achieving 81% accuracy across 22 users over 10-day intervals

Mfingerprint: Privacy-preserving user modeling with multimodal mobile device footprints

Abstract. The dramatic increase of daily usage of mobile devices generates massive digital footprints of users. Such footprints come from physical sensing such as GPS, WiFi, and Bluetooth, as well as social behavior sensing, e.g., call logs, application usage, etc. Many existing studies apply the mobile device footprints to infer daily activities like sitting/standing and social contexts such as personality traits and emotional states. In this paper, we propose a different approach to explore multimodal mobile footprints and build a novel user modeling framework called mFingerprint that can effectively and uniquely depict users. mFingerprint does not expose raw sensitive information from mobile device, e.g., the exact location, WiFi access points, or apps installed, but computes privacy-preserving statistical features to model the user discriminatively. These descriptive features protect sensitive information, thus can be shared, transmitted, and reused with less privacy concerns. By testing on 22 users' mobile phone data collected over 2 months, we demonstrate the effectiveness of mFingerprint in user modeling and identification. In particular, our conditional entropy footprint statistics can achieve 81% accuracy across all 22 users while evaluating over 10-day intervals

Offload decision models and the price of anarchy in mobile cloud application ecosystems

With the maturity of technologies, such as HTML5 and JavaScript, and with the increasing popularity of cross-platform frameworks, such as Apache Cordova, mobile cloud computing as a new design paradigm of mobile application developments is becoming increasingly more accessible to developers. Following this trend, future on-device mobile application ecosystems will not only comprise a mixture of native and remote applications, but also include multiple hybrid mobile cloud applications. The resource competition in such ecosystems and its impact over the performance of mobile cloud applications has not yet been studied. In this paper, we study this competition from a game theoretical perspective and examine how it affects the behavior of mobile cloud applications. Three offload decision models of cooperative and non-cooperative nature are constructed and their efficiency compared. We present an extension to the classic load balancing game to model the offload behaviors within a non-cooperative environment. Mixed-strategy Nash equilibria are derived for the non-cooperative offload game with complete information, which further quantifies the price of anarchy in such ecosystems. We present simulation results that demonstrate the differences between each decision model’s efficiency. Our modeling approach facilitates further research in the design of the offload decision engines of mobile cloud applications. Our extension to the classic load balancing game broadens its applicability to real-life applications