ABSTRACT
Logical statuses of mobile users, such as isBusy and isAlone, are the key enabler for a plethora of context-aware mobile applications. While on-board hardware sensors, such as motion, proximity, and location sensors, have been extensively studied for logical status inference, the continuous usage incurs formidable energy consumption and therefore user experience degradation. In this paper, we argue that smartphone usage statistics can be used for logical status inference with negligible energy cost. To validate this argument, this paper presents a continuous inference engine that (1) intercepts multiple operating system events, in particular foreground app, notifications, screen states, and connected networks; (2) extracts informative features from OS events; and (3) efficiently infers the logical status of mobile users. The proposed inference engine is implemented for unmodified Android phones, and an evaluation on a four-week trial has shown promising accuracy in identifying four logical statuses of mobile users with over 87% accuracy while the average energy impact on the battery life is less than 0.5%.
Supplemental Material
- Aharony, N., Pan, W., Ip, C., Khayal, I., and Pentland, A. Social fMRI: Investigating and shaping social mechanisms in the real world. Pervasive Mob. Comput. (2011). Google ScholarDigital Library
- Baldauf, M., Dustdar, S., and Rosenberg, F. A survey on context-aware systems. International Journal of Ad Hoc and Ubiquitous Computing (2007). Google ScholarDigital Library
- Beach, A., Gartrell, M., Xing, X., Han, R., Lv, Q., Mishra, S., and Seada, K. Fusing mobile, sensor, and social data to fully enable context-aware computing. In Proc. of Hotmobile (2010). Google ScholarDigital Library
- Bettini, C., Brdiczka, O., Henricksen, K., Indulska, J., Nicklas, D., Ranganathan, A., and Riboni, D. A survey of context modelling and reasoning techniques. Pervasive and Mobile Computing (2010). Google ScholarDigital Library
- Ekman, P. Facial expression and emotion. American Psychologist (1993).Google Scholar
- Ganti, R., Srivatsa, M., Ranganathan, A., and Han, J. Inferring human mobility patterns from taxicab location traces. In Proc. of UbiComp (2013). Google ScholarDigital Library
- Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., and Witten, I. H. The weka data mining software: an update. ACM SIGKDD explorations newsletter 11, 1 (2009), 10--18. Google ScholarDigital Library
- Kanade, T., Cohn, J. F., and Tian, Y. Comprehensive database for facial expression analysis. In Proc. of Fourth IEEE International Conference on Automatic Face and Gesture Recognition (FG) (2000). Google ScholarDigital Library
- Keally, M., Zhou, G., Xing, G., Wu, J., and Pyles, A. PBN: towards practical activity recognition using smartphone-based body sensor networks. In Proc. of SenSys (2011). Google ScholarDigital Library
- Khan, W. Z., Xiang, Y., Aalsalem, M. Y., and Arshad, Q. Mobile Phone Sensing Systems: A Survey. Commun. Surveys Tuts. (2013).Google Scholar
- Kwapisz, J. R., Weiss, G. M., and Moore, S. A. Activity Recognition Using Cell Phone Accelerometers. SIGKDD Explor. Newsl. (2011). Google ScholarDigital Library
- Lane, N. D., Miluzzo, E., Lu, H., Peebles, D., Choudhury, T., and Campbell, A. T. A survey of mobile phone sensing. Communications Magazine, IEEE (2010). Google ScholarDigital Library
- LiKamWa, R., Liu, Y., Lane, N. D., and Zhong, L. MoodScope: building a mood sensor from smartphone usage patterns. In Proc. of MobiSys (2013). Google ScholarDigital Library
- Lu, H., Pan, W., Lane, N. D., Choudhury, T., and Campbell, A. T. SoundSense: scalable sound sensing for people-centric applications on mobile phones. In Proc. of MobiSys (2009). Google ScholarDigital Library
- Lu, H., Yang, J., Liu, Z., Lane, N. D., Choudhury, T., and Campbell, A. T. The Jigsaw continuous sensing engine for mobile phone applications. In Proc. of SenSys (2010). Google ScholarDigital Library
- Lu, Y., Cohen, I., Zhou, X. S., and Tian, Q. Feature selection using principal feature analysis. In Proc. of Multimedia (2007). Google ScholarDigital Library
- Maloney, S., and Boci, I. Survey: Techniques for Efficient energy consumption in Mobile Architectures. Power (mW) 16, 9.56 (2012), 7--35.Google Scholar
- Nath, S. ACE: exploiting correlation for energy-efficient and continuous context sensing. In Proc. of MobiSys (2012). Google ScholarDigital Library
- Noulas, A., Scellato, S., Lathia, N., and Mascolo, C. Mining User Mobility Features for Next Place Prediction in Location-Based Services. In Proc. of ICDM (2012). Google ScholarDigital Library
- OTT communication services. http://www.analysysmason.com/About-Us/News/Insight/consumers-smartphone-usage-May2014-RDMV0. Consumer smartphone usage 2014.Google Scholar
- Parate, A., Bohmer, M., Chu, D., Ganesan, D., and Marlin, B. M. Practical prediction and prefetch for faster access to applications on mobile phones. In Proc. of UbiComp (2013). Google ScholarDigital Library
- Rachuri, K. K., Musolesi, M., Mascolo, C., Rentfrow, P. J., Longworth, C., and Aucinas, A. EmotionSense: a mobile phones based adaptive platform for experimental social psychology research. In Proc. of UbiComp (2010). Google ScholarDigital Library
- Tian, Y., Kanade, T., and Cohn, J. F. Facial expression recognition. In Handbook of face recognition. 2011.Google Scholar
- Ubicomp Lab, University of Arkansas. https://play.google.com/store/apps/details?id=edu.uark.mindtracker/. Mind Tracker Android App.Google Scholar
- Xu, Y., Lin, M., Lu, H., Cardone, G., Lane, N., Chen, Z., Campbell, A., and Choudhury, T. Preference, context and communities: a multi-faceted approach to predicting smartphone app usage patterns. In Proc. of ISWC (2013). Google ScholarDigital Library
- Yan, T., Chu, D., Ganesan, D., Kansal, A., and Liu, J. Fast app launching for mobile devices using predictive user context. In Proc. of MobiSys (2012). Google ScholarDigital Library
- Yan, Z., Subbaraju, V., Chakraborty, D., Misra, A., and Aberer, K. Energy-efficient continuous activity recognition on mobile phones: An activity-adaptive approach. In Proc. of ISWC (2012). Google ScholarDigital Library
- Yatani, K., and Truong, K. N. BodyScope: a wearable acoustic sensor for activity recognition. In Proc. of UbiComp (2012). Google ScholarDigital Library
- Zheng, Y., Li, Q., Chen, Y., Xie, X., and Ma, W.-Y. Understanding mobility based on gps data. In Proc. of UbiComp (2008). Google ScholarDigital Library
- Zhu, H., Chen, E., Yu, K., Cao, H., Xiong, H., and Tian, J. Mining personal context-aware preferences for mobile users. In Proc. of ICDM (2012). Google ScholarDigital Library
Index Terms
- Exploiting usage statistics for energy-efficient logical status inference on mobile phones
Recommendations
Exploiting User Context and Network Information for Mobile Application Usage Prediction
HOTPOST '15: Proceedings of the 7th International Workshop on Hot Topics in Planet-scale mObile computing and online Social neTworkingThe explosive increasing mobile Applications (Apps) have been attracting researchers and developers to investigate user preferences on various mobile Apps. Understanding mobile Apps usage pattern of end users will help to improve the quality of mobile ...
A Large-Scale Study of iPhone App Launch Behaviour
CHI '18: Proceedings of the 2018 CHI Conference on Human Factors in Computing SystemsThere have been many large-scale investigations of users' mobile app launch behaviour, but all have been conducted on Android, even though recent reports suggest iPhones account for a third of all smartphones in use. We report on the first large-scale ...
Understanding the Challenges of Mobile Phone Usage Data
MobileHCI '15: Proceedings of the 17th International Conference on Human-Computer Interaction with Mobile Devices and ServicesDriven by curiosity and our own three diverse smartphone application usage datasets, we sought to unpack the nuances of mobile device use by revisiting two recent Mobile HCI studies [1, 17]. Our goal was to add to our broader understanding of smartphone ...
Comments