Marathon is very popular in recent years. However, finishing the game is no easy task, especially for beginners. Regular practices and training are needed. With the availability of wearable devices, it is possible to develop virtual coaches that monitor the progresses of individual runners closeup and guide them through tailor-made training schedules. Unfortunately, most existing wearable devices only record physiological signals of the runners and rely on off-line processing to provide feedbacks. In this paper, we present the design and development of an on-line virtual coach, which performs real-time tracking and analysis of the physiological status of the runner and suggests appropriate adjustments on the exercise intensity. The proposed virtual coach is a pure software solution and can work with any wearable device that monitors the heart rate and running speed of the runner. The main challenge of our system is to predict when the runner will reach the various running states and instruct the runner to adjust the speed just ahead of time so that her/his body can react in time to maintain the required training intensity. Experiments on real users show that our proposed algorithms can correctly predict the running states of the runners and help them to better maintain the required intensity to maximize the training effects.

Automatic track and prediction of consumer behaviors involve huge commercial interests and have been studied extensively in the past. We have seen very successful application of such techniques on online consuming behaviors. However, it is still very challenging to predict consumer behaviors in physical stores, because many operations in the middle are not digitized. As smartphones are becoming indispensible in our daily life, we propose to build a framework, called ConsumSense, into the smartphones that observes in close-up the physical consuming behavior of the user and predicts his/her future purchases. Our framework addresses two difficult issues: (1) how to use the limited number of sensors on a smart phone to observe and predict the consuming behavior of its user? (2) how to verify and correlate the purchases? To demonstrate the feasibility of the proposed framework, we have developed the framework on Android and evaluated it by asking 14 participants to conduct a 3-week experiment by using Easy card during their daily life. The results show that time and location are the most important contexts for predicting consuming activities and our framework can achieve a 76% prediction accuracy.

Over the past few years, personal sensing applications, such as travel path sharing and location recording, have been more and more popular. These applications use GPS sensors to record track points on smartphones and upload the track points to clouds in real time for information sharing. However, uploading a lot of GPS points may lead to heavy network traffic and much higher power consumption. To address the problem, we present corner extraction by GPS filtering (CEGF) that extracts corner feature GPS points (CFGPs) from GPS track points (GTPs). Applications only need to upload the CFGPs to save the uploading energy on smartphones. CFGPs can be regarded as characteristic points of corners to represent the corresponding roads. To reduce the number of uploaded points, we use CEGF to filter out the CFGPs from a large amount of GTPs.

Launch delay has been an important factor affecting users' experiences in mobile multimedia applications. To launch applications quickly, modern mobile systems such as Android usually keep inactive applications in the background and manage them through an LRU-based activity stack. Whenever the user wants to run and interact with a background application again, that application can be switched back into the foreground quickly from the activity stack without delay in initializing the applications anew. Since background multimedia applications often continuously consume the battery power of the smart phone, the challenge is to effect a balance between application launch delay and battery lifetime. In this article, we propose innovative application management strategies that terminate “unbeneficial” background applications to save energy and pre-launch “beneficial” applications to improve the application launch delay. The proposed strategies are evaluated through a trace-driven simulation and a real experiment. The results show that the average application launch delay can be reduced by 15% while the average battery lifetime is increased by 18%.

Battery energy is one of the most critical resources in a handheld device. Modern designs for handheld devices thus call for optimized use of system power. To develop power-efficient systems, it is essential to understand how power is consumed throughout the system. A promising approach is to measure the power consumption of the system and then match the measurements with the profiled system events. The latter then provides information about how the system consumes power. However, existing tools mostly profile only at the process level due to problems such as profiling overhead and event synchronization. Modern handheld systems, such as Android, complicate the problem further because of the extra layers of software such as Java runtime environment and libraries. To address the above challenges, this paper presents ANEPROF, Android Energy Profiler - a profiling tool for Android that allows energy profiling down to the function level. The design issues and considerations are discussed and its implementation is described. The performance of the tool is evaluated by comparing with other profiling methods.