Yanjie Li, Liqin Kang, Zhaojin Li, Fugao Jiang, Nan Bi, Tao Du, Maryam Abiri
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引用次数: 0
摘要
各种智能传感器和移动通信技术的日益普及,实现了对学生健康体质的快速感知、监测、采集和分析,极大地推动了体育教育的发展。通过采集学生的生理信号并传输到边缘服务器,我们可以精确地分析和判断学生的健康状况是否不良(如离群)。然而,随着时间的推移,学生的生理信号会大量积累,这给学生生理数据的快速存储和及时处理带来了沉重的负担。在这种情况下,开发一种具有时间感知能力的离群点检测技术,以高效的方式对学生进行健康体质评估就变得十分必要。考虑到这一挑战,我们提出了一种基于位置敏感散列的新型时间感知离群点检测方法,名为 TOD。TOD 将大量的学生生理数据浓缩成一组简洁的健康指数。利用这些指数,我们可以从大量候选数据中高效、准确、快速地识别出潜在的离群学生。最后,我们设计了一组基于 WS-DREAM 数据集的模拟实验。实验结果证明了 TOD 方法的可行性以及与其他现有方法相比的优越性。
Time-aware outlier detection in health physique monitoring in edge-aided sport education decision-makings
The increasing popularity of various intelligent sensor and mobile communication technologies has enabled quick health physique sensing, monitoring, collection and analyses of students, which significantly promoted the development of sport education. Through collecting the students’ physiological signals and transmitted them to edge servers, we can precisely analyze and judge whether a student is in poor health (e.g., an outlier). However, with time elapsing, the accumulated physiological signals of students become massive, which places a heavy burden on the quick storage and in-time processing of physiological data of students. In this situation, it is becoming a necessity to develop a time-aware outlier detection technique for health physique evaluation of students in a time-efficient way. Considering this challenge, we propose a novel time-aware outlier detection method named TOD based on Locality-Sensitive Hashing. TOD condenses extensive physiological student data into a concise set of health indices. Leveraging these indices, we can efficiently identify potential student outliers from a large pool of candidates with precision and speed. Finally, we have designed a group of simulated experiments based on WS-DREAM dataset. Experiment results prove the feasibility and superiority of the TOD method compared with other existing methods.