【摘要】 针对当前人体运动数据挖掘算法无法对实时数据进行采集与分析，导致人体运动数据挖掘正确率较低且时间较长的问题，提出基于可穿戴式纳米生物传感器的人体运动数据挖掘算法。首先，利用可穿戴式纳米生物传感器采集人体运动数据，将采集到的数据转换为二进制数据形式，并对转换后的数据进行清洗与补位处理；最后，使用萤火虫算法对K均值聚类方法进行优化，利用优化后的K均值聚类方法对清洗与补位后的数据进行聚类处理。实验结果表明，所提算法的召回率平均值为97.12%,数据挖掘正确率平均值为98.42%,为运动员生理指标的实时监测与分析提供重要的数据基础。更多还原

【Abstract】 To address the problem that current human motion data mining algorithms cannot collect and analyze real-time data, which leads to low correct rate and long time for human motion data mining, we propose a human motion data mining algorithm based on wearable nano-biosensor. Firstly, the human motion data is collected using the wearable nano-biosensor. Secondly, the collected data are converted into binary data form, and the converted data are cleaned and complemented. Finally, the K-mean clustering method is optimized using the firefly algorithm, and the optimized K-mean clustering method is used to cluster the cleaned and patched data. The results show that the average recall rate of the proposed algorithm is 97.12% and the average correct data mining rate is 98.42%, which provides an important data base for real-time monitoring and analysis of athletes′ physiological indexes.更多还原