Browsing by Author "Tseng, Jerry C. C."

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A Big Data Analytical Framework for Sports Behavior Mining and Personalized Health Services
(2017-01-01)
Tseng, Vincent S.
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Chou, Chih-Hsin
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Yang, Kai-Qi
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Tseng, Jerry C. C.
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資訊工程學系
Mobile healthcare has become an important trend in medical and healthcare domains. With the rapid development of wearable and sensing technologies, various health-related information can now be recorded, forming valuable big health data. Physical activities are considered to have a great impact on heart rate, and the analysis of heart rate data now is widely used in medical/healthcare researches. The analysis of exercise records and heart rate data have been used for the research of the exercise intensity in many institutes. Heart rate patterns refers to a symbol of health status of heart, which is based on the current rate, and other physiological parameters. An effective heart rate pattern discovering is very helpful for the healthcare and cardiovascular prevention. In this work, we aim to build a big data analytics framework for sports behavior mining and personalized health services. We analyzed users' exercise data including heart rate and GPS data, which were collected in a practical sports and social platform, to discover users' periodic sports patterns and the trend of heart rate change during exercise. Since the dataset is not only very huge but also growing very quickly, we adopt Apache Spark as the development framework to address this Velocity issue in Big Data. The analytical results can serve as important core for personalized healthcare applications. Moreover, we also group the individual result to discover the clustering result, which can be further applied for advanced healthcare applications.
A Scalable Complex Event Analytical System with Incremental Episode Mining over Data Streams
(2016)
Tseng, Jerry C. C.
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Gu, Jia-Yuan
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Tseng, Vincent S.
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Wang, P. F.
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Chen, Ching-Yu
Episode pattern mining is a very powerful technique to get high-valued information for people to solve real-life cross-disciplinary problems, such as for the analysis of manufacturing, stock markets, weather records and so on. As data grows, the mining process must be re-triggered again and again to obtain the most updated information. However, periodically re-mining the full dataset is not cost-effective, and thus a number of incremental mining approaches arise for the growing data. However, to our best knowledge, there exist few studies targeted on the problem of incremental episode mining. Moreover, streaming data of complex events is more and more popular because digital sensors always collect data around us in this big data age. Now the challenge is not only mining valuable episode patterns of incremental dataset, but also mining episode patterns over data streams of complex events. To address this research problem, we adopt the Lambda Architecture to design a scalable complex event analytical system that could be used to facilitate the incremental episode mining process over complex event sequences of data streams. Apache Spark and Apache Spark Streaming are applied as the development framework of the batch layer and the speed layer, respectively. To take both the efficiency and accuracy into consideration, we develop a series of modules and three algorithms, namely, batch episode mining, delta episode mining and pattern merging. Results from the experimental validation on a real dataset show that the proposed system carries high scalability and delivers excellent performance in terms of efficiency and accuracy.
An Interactive Healthcare System with Personalized Diet and Exercise Guideline Recommendation
(2015)
Tseng, Jerry C. C.
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Lin, Bo-Hau
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Lin, Yu-Feng
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Day, Miin-Luen
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Wang, Shyh-Chyi
Recently people pay more and more attention on how to effectively and efficiently analyze the result of regular physical examinations to provide the most helpful information for individual health management. In this paper, we design and develop an interactive system of virtual healthcare assistant to help people, especially for those who suffer from chronic diseases (e.g., metabolic syndrome) to easily understand their health conditions and then well manage it. This system analyzes the result of regular physical examination to evaluate the health risk and provide personalized healthcare services for users in terms of diet and exercise guideline recommendations. We developed some interactive ways for users to easily feedback their vital signs to the system and quickly get the suggestions for health management from the system. Besides the browser-based system, we also developed a mobile App that can regularly remind users to carry out the recommendations, which are provided by the system. To prove the system is feasible in the real-world clinical environment, we also applied the Institutional Review Board (IRB) for a human subject research to validate this system. Other than the functional features, there are also several important non-functional features of the extensibility and the convenience for use. First, we use the physical examination result as the raw data to be analyzed. It\'s very convenient for users with very low cost. Second, the system design is extendable, so it can be easily adjusted to work for any chronic ills, even other kinds of diseases. Moreover, it can be extended to provide other kinds of healthcare guideline recommendations as well. These features constitute the main contributions of this work.
Long-Term User Location Prediction Using Deep Learning and Periodic Pattern Mining
(2017-01-01)
Wong, Mun Hou
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Tseng, Vincent S.
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Tseng, Jerry C. C.
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Liu, Sun-Wei
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Tsai, Cheng-Hung
In recent years, with the advances in mobile communication and growing popularity of the fourth-generation mobile network along with the enhancement in location positioning techniques, mobile devices have generated extensive spatial trajectory data, which represent the mobility of moving objects. New services are emerged to serve mobile users based on their predicted locations. Most of the existing studies on location prediction were focused on predicting the next location of a user, which is regarded as short-term next location prediction. While more advanced location-based services could be enabled for the users if long-term location prediction could be achieved, the existing methods constrained in next-location prediction are not applicable for long-term prediction scenario. In this paper, we propose a novel prediction framework named LSTM-PPM that utilises deep learning and periodic pattern mining for long-term prediction of user locations. Our framework devises the ideology from natural language model and uses multi-step recursive strategy to perform long-term prediction. Furthermore, the periodic pattern mining technique is utilized to reduce the accumulated loss in the multi-step strategy. Through empirical evaluation on a real-life trajectory dataset, our proposed approach is shown to provide effective performance in long-term location prediction. To the best of our knowledge, this is the first work addressing the research topic on long-term user location prediction.