Product details
- ISBN 9781466588271
- Weight: 540g
- Dimensions: 156 x 234mm
- Publication Date: 05 Dec 2013
- Publisher: Taylor & Francis Inc
- Publication City/Country: US
- Product Form: Hardback
- Language: English
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Learn How to Design and Implement HAR Systems
The pervasiveness and range of capabilities of today’s mobile devices have enabled a wide spectrum of mobile applications that are transforming our daily lives, from smartphones equipped with GPS to integrated mobile sensors that acquire physiological data. Human Activity Recognition: Using Wearable Sensors and Smartphones focuses on the automatic identification of human activities from pervasive wearable sensors—a crucial component for health monitoring and also applicable to other areas, such as entertainment and tactical operations.
Developed from the authors’ nearly four years of rigorous research in the field, the book covers the theory, fundamentals, and applications of human activity recognition (HAR). The authors examine how machine learning and pattern recognition tools help determine a user’s activity during a certain period of time. They propose two systems for performing HAR: Centinela, an offline server-oriented HAR system, and Vigilante, a completely mobile real-time activity recognition system. The book also provides a practical guide to the development of activity recognition applications in the Android framework.
Miguel A. Labrador earned his M.Sc. in telecommunications and the Ph.D. degree in information science with concentration in telecommunications from the University of Pittsburgh, in 1994 and 2000, respectively. Since 2001, he has been with the University of South Florida, Tampa, where he is currently a full professor in the department of computer science and engineering, the director of the graduate programs, and the director of the research experiences for undergraduates program. His research interests are in ubiquitous sensing, location-based services, energy-efficient mechanisms for wireless sensor networks, and design and performance evaluation of computer networks and communication protocols. He has published more than 100 technical and educational papers in journals and conferences devoted to these topics. Dr. Labrador has served as technical program committee member of many IEEE conferences and is currently area editor of Computer Communications and editorial board member of the Journal of Network and Computer Applications, both Elsevier Science journals. Dr. Labrador is the lead author of Location-Based Information Systems: Developing Real-Time Tracking Applications, Taylor & Francis, and Topology Control in Wireless Sensor Networks, Springer. Dr. Labrador is senior member of the IEEE and a member of ACM, ASEE and Beta Phi Mu.
Oscar D. Lara Yejas received his B.Sc. in systems engineering from Universidad del Norte, Barranquilla, Colombia, in 2007. He received his M.Sc. in computer science in 2010 and his Ph.D. in computer science and engineering in 2012, both from the University of South Florida. Dr. Lara Yejas has significant industry experience in the public utilities sector, leading projects related to mobile visualization of geographic and cartographic information, real-time tracking applications, and telemetry. He has also worked on intelligent transportation systems with the Center for Urban Transportation Research (CUTR) at the University of South Florida. He was part of the development team of the Travel Assistance Device (TAD), a mobile application for aiding cognitively disabled people to use public transportation. Dr. Lara Yejas’ dissertation on human activity recognition with wearable sensors under the advising of Dr. Labrador has given birth to this book. In 2012, Dr. Lara Yejas joined International Business Machines Corporation (IBM) within the InfoSphere BigInsights group. His current work focuses on large-scale analytics in distributed computing environments. Further research interests of his encompass but are not limited to machine learning, big data analytics, location-based systems, as well as multiobjective optimization using swarm intelligence methods.
