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Mobile healthcare data mining for sport item recommendation in edge-cloud collaboration

Abstract

With the continuous maturity and adoption of mobile devices enabled by wireless communication technology, people are more apt to record their sport exercise data or healthcare data through various lightweight and smart devices, e.g., mobile phones and smart watches. Meanwhile, massive sport data or healthcare data keep being produced with time, which forms a main source of big healthcare data. Deep mining and analysis of such healthcare data are of positive significance for accurately recognizing the real-time health condition of mobile users and further recommend appropriate sport items to them. However, traditional centralized healthcare data mining and recommendation approaches require mobile users to transmit their health data collected by mobile devices to a remote cloud platform, which often involves heavy data transmissions from mobile devices to cloud platform. As a consequence, the transmission cost is high and the time delay is long. Moreover, long-distance data transmissions are prone to disclose user privacy. Considering these limitations, we bring forth a novel time-efficient and privacy-preserving healthcare data integration and mining approach for sport item recommendation, based on edge-cloud collaboration mechanism. At last, we design a group of simulation experiments to validate the effectiveness and efficiency of our approach. Experimental comparisons indicate a good balance between different evaluation metrics.

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Correspondence to Yucong Duan.

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Chen, C., Li, C. & Duan, Y. Mobile healthcare data mining for sport item recommendation in edge-cloud collaboration. Wireless Netw (2022). https://doi.org/10.1007/s11276-022-03059-w

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  • DOI: https://doi.org/10.1007/s11276-022-03059-w

Keywords

  • Mobile computing
  • Healthcare
  • Privacy
  • Edge-cloud collaboration
  • Sport item recommendation