Abstract
In this book chapter, we present a novel system that recognizes and records the physical activity of a person using a mobile phone. The sensor data is collected by built-in accelerometer sensor that measures the motion intensity of the device. The system recognizes five everyday activities in real-time, i.e., stationary, walking, running, bicycling, and in vehicle. We first introduce the sensor’s data format, sensor calibration, signal projection, feature extraction, and selection methods. Then we have a detailed discussion and comparison of different choices of feature sets and classifiers. The design and implementation of one prototype system is presented along with resource and performance benchmark on Nokia N95 platform. Results show high recognition accuracies for distinguishing the five activities. The last part of the chapter introduces one demo application built on top of our system, physical activity diary, and a selection of potential applications in mobile wellness, mobile social sharing and contextual user interface domains.
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Yang, J., Lu, H., Liu, Z., Boda, P.P. (2010). Physical Activity Recognition with Mobile Phones: Challenges, Methods, and Applications. In: Shao, L., Shan, C., Luo, J., Etoh, M. (eds) Multimedia Interaction and Intelligent User Interfaces. Advances in Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-84996-507-1_8
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DOI: https://doi.org/10.1007/978-1-84996-507-1_8
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