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Articles 1 - 7 of 7
Full-Text Articles in Physical Sciences and Mathematics
Bikemate: Bike Riding Behavior Monitoring With Smartphones, Weixi Gu, Zimu Zhou, Yuxun Zhou, Han Zou, Yunxin Liu, Costas J. Spanos, Lin Zhang
Bikemate: Bike Riding Behavior Monitoring With Smartphones, Weixi Gu, Zimu Zhou, Yuxun Zhou, Han Zou, Yunxin Liu, Costas J. Spanos, Lin Zhang
Research Collection School Of Computing and Information Systems
Detecting dangerous riding behaviors is of great importance to improve bicycling safety. Existing bike safety precautionary measures rely on dedicated infrastructures that incur high installation costs. In this work, we propose BikeMate, a ubiquitous bicycling behavior monitoring system with smartphones. BikeMate invokes smartphone sensors to infer dangerous riding behaviors including lane weaving, standing pedalling and wrong-way riding. For easy adoption, BikeMate leverages transfer learning to reduce the overhead of training models for different users, and applies crowdsourcing to infer legal riding directions without prior knowledge. Experiments with 12 participants show that BikeMate achieves an overall accuracy of 86.8% for lane …
Mobile Cloud Computing: A New Way Of Cloud Computing, Festim Halili, Verda Misimi, Rilinda Iseini, Mirije Salihi
Mobile Cloud Computing: A New Way Of Cloud Computing, Festim Halili, Verda Misimi, Rilinda Iseini, Mirije Salihi
UBT International Conference
Cloud Computing seems to be the most promising technology of the century we are living. It provides a new manner of sharing distributed resources and services that may be part of different organizations, geographically located in different places and different time zones. Mobile Cloud Computing offers partially the same functionality, with the only additional requirement that, at least, some of the devices are mobile. In this paper, we will try to provide a detailed explanation of Mobile Cloud Computing concept by providing different examples, figures and charts showing usage, accessibility, pros and cons and comparison of different cloud platforms.
Sugarmate: Non-Intrusive Blood Glucose Monitoring With Smartphones, Weixi Gu, Yuxun Zhou, Zimu Zhou, Xi Liu, Han Zou, Pei Zhang, Costas J. Spanos, Lin Zhang
Sugarmate: Non-Intrusive Blood Glucose Monitoring With Smartphones, Weixi Gu, Yuxun Zhou, Zimu Zhou, Xi Liu, Han Zou, Pei Zhang, Costas J. Spanos, Lin Zhang
Research Collection School Of Computing and Information Systems
Inferring abnormal glucose events such as hyperglycemia and hypoglycemia is crucial for the health of both diabetic patients and non-diabetic people. However, regular blood glucose monitoring can be invasive and inconvenient in everyday life. We present SugarMate, a first smartphone-based blood glucose inference system as a temporary alternative to continuous blood glucose monitors (CGM) when they are uncomfortable or inconvenient to wear. In addition to the records of food, drug and insulin intake, it leverages smartphone sensors to measure physical activities and sleep quality automatically. Provided with the imbalanced and often limited measurements, a challenge of SugarMate is the inference …
Mopeye: Opportunistic Monitoring Of Per-App Mobile Network Performance, Daoyuan Wu, Rocky K. C. Chang, Weichao Li, Eric K. T. Cheng, Debin Gao
Mopeye: Opportunistic Monitoring Of Per-App Mobile Network Performance, Daoyuan Wu, Rocky K. C. Chang, Weichao Li, Eric K. T. Cheng, Debin Gao
Research Collection School Of Computing and Information Systems
Crowdsourcing mobile user’s network performance has become an effective way of understanding and improving mobile network performance and user quality-of-experience. However, the current measurement method is still based on the landline measurement paradigm in which a measurement app measures the path to fixed (measurement or web) servers. In this work, we introduce a new paradigm of measuring per-app mobile network performance. We design and implement MopEye, an Android app to measure network round-trip delay for each app whenever there is app traffic. This opportunistic measurement can be conducted automatically without user intervention. Therefore, it can facilitate a large-scale and long-term …
Inferring Smartphone Keypress Via Smartwatch Inertial Sensing, Sougata Sen, Karan Grover, Vigneshwaran Subbaraju, Archan Misra
Inferring Smartphone Keypress Via Smartwatch Inertial Sensing, Sougata Sen, Karan Grover, Vigneshwaran Subbaraju, Archan Misra
Research Collection School Of Computing and Information Systems
Due to numerous benefits, sensor-rich smartwatchesand wrist-worn wearable devices are quickly gaining popularity.The popularity of these devices also raises privacy concerns. Inthis paper we explore one such privacy concern: the possibility ofextracting the location of a user’s touch-event on a smartphone,using the inertial sensor data of a smartwatch worn by the useron the same arm. This is a major concern not only because itmight be possible for an attacker to extract private and sensitiveinformation from the inputs provided but also because the attackmode utilises a device (smartwatch) that is distinct from thedevice being attacked (smartphone). Through a user study wefind …
Mobile Hands: What Your Phone Use Says About You, Parker Williams, Jeff Jenkins
Mobile Hands: What Your Phone Use Says About You, Parker Williams, Jeff Jenkins
Journal of Undergraduate Research
The percentage of individuals using mobile phones to respond to online applications, forms, and surveys is predicted to rapidly increase in the future. As of June 2, 2015, there were an estimated 2.6 billion smartphones in use in the world; by 2020 there will be 6.1 billion smartphones in use. Smartphones will account for roughly 80% of all mobile driven data traffic by 2020 (Lunden, 2015). With the proliferation of smartphones, measuring the fidelity of information gathered from and about the user becomes extremely important. Smartphones are a rich source of behavioral and biometric information, gathering data about how the …
Towards A Continuous Assessment Of Cognitive Workload For Smartphone Multitasking Users, Angel Jimenez-Molina, Hernan Lira
Towards A Continuous Assessment Of Cognitive Workload For Smartphone Multitasking Users, Angel Jimenez-Molina, Hernan Lira
H-Workload 2017: Models and Applications (Works in Progress)
The intermeshing of Smartphone interactions and daily activities depletes the availability of cognitive resources. This excessive demand may lead to several undesirable cognitive states, which can be avoided by continuously assessing the user cognitive workload. Recently, many attempts have emerged to assess this workload by using psycho physiological signals. This paper provides evidence that it is possible to train models that accurately identify in short time windows such cognitive workload by processing heart rate and blood oxygen saturation signals. This assessment could be applied in Smartphone notification delivery, interface adaptations or cognitive capabilities evaluation.