Abstract
Personal Activity Intelligence (PAI) is a recently proposed metric for physical activity tracking, which takes into account continuous heart rate and other physical parameters. PAI plays an important role to inform users of the risk of premature cardiovascular disease, and helps to promote physical activity. However, the PAI computing is too expensive to provide feedback in time, which restricts its practical value in disease warning. In this paper, we present PAIE, a Personal Activity Intelligence Estimator based on massive heart rate data in the cloud. PAIE provides approximate PAI with desired accuracy of statistical significance, which costs much less time than that used to provide the exact value. We design the PAI estimate framework in the cloud, and propose a novel estimate mechanism to leverage the efficiency and accuracy. We analyze the PAI algorithm, and formulate the statistical foundation that supports block-level stratified sampling, effective estimation of PAI and error bounding. We experimentally validate our techniques on Storm, and the results demonstrate that PAIE can provide promising physical activity estimate for massive heart rate data in the cloud.
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Acknowledgment
This research was supported by the grants from the Natural Science Foundation of China (No. 61502279), the General Program of Science and Technology Development Project of Beijing Municipal Education Commission (No. KM201710012008), the Special Funds for High-level Teacher’s Building of Beijing Institute of Fashion Technology (No. BIFTQG201803), the Ningxia Natural Science Foundation (No. 2018A0899). We would like to thank Yadong You, Guangyao Guo, Yongpeng Sun and Tianchen Xiong from Beijing Institute of Fashion Technology, who gave much help in the experiment.
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Shi, Y., Du, F., Zhang, Y., Li, Z., Zhang, T. (2019). PAIE: A Personal Activity Intelligence Estimator in the Cloud. In: Zheng, C., Zhan, J. (eds) Benchmarking, Measuring, and Optimizing. Bench 2018. Lecture Notes in Computer Science(), vol 11459. Springer, Cham. https://doi.org/10.1007/978-3-030-32813-9_9
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DOI: https://doi.org/10.1007/978-3-030-32813-9_9
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