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.

随着无线通讯技术所带动的移动设备的不断成熟和普及，人们更倾向于通过各种轻巧智能的设备，例如手机、智能手表等来记录自己的运动锻炼数据或保健数据。同时，随着时间的推移，海量的运动数据或医疗保健数据不断产生，成为医疗保健大数据的主要来源。对此类健康数据进行深度挖掘和分析，对于准确识别移动用户的实时健康状况并进一步向其推荐合适的运动项目具有积极意义。然而，传统的集中式医疗数据挖掘和推荐方法需要移动用户将移动设备收集的健康数据传输到远程云平台，这通常涉及从移动设备到云平台的大量数据传输。结果，传输成本高，时延长。此外，长距离数据传输容易泄露用户隐私。考虑到这些限制，我们提出了一种基于边缘-云协作机制的、用于运动项目推荐的新的时间效率和隐私保护的医疗保健数据集成和挖掘方法。最后，我们设计了一组仿真实验来验证我们方法的有效性和效率。实验比较表明不同评估指标之间的平衡良好。考虑到这些限制，我们提出了一种基于边缘-云协作机制的、用于运动项目推荐的新的时间效率和隐私保护的医疗保健数据集成和挖掘方法。最后，我们设计了一组仿真实验来验证我们方法的有效性和效率。实验比较表明不同评估指标之间的平衡良好。考虑到这些限制，我们提出了一种基于边缘-云协作机制的、用于运动项目推荐的新的时间效率和隐私保护的医疗保健数据集成和挖掘方法。最后，我们设计了一组仿真实验来验证我们方法的有效性和效率。实验比较表明不同评估指标之间的平衡良好。