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Private anomaly detection of student health conditions based on wearable sensors in mobile cloud computing.
Xie, Yu; Zhang, Kuilin; Kou, Huaizhen; Mokarram, Mohammad Jafar.
  • Xie Y; Chengdu University of Information Technology, Chengdu, China.
  • Zhang K; Shandong Provincial University Laboratory for Protected Horticulture, Weifang University of Science and Technology, Weifang, China.
  • Kou H; School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China.
  • Mokarram MJ; Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, Iran.
J Cloud Comput (Heidelb) ; 11(1): 38, 2022.
Article in English | MEDLINE | ID: covidwho-2009465
ABSTRACT
With the continuous spread of COVID-19 virus, how to guarantee the healthy living of people especially the students who are of relative weak physique is becoming a key research issue of significant values. Specifically, precise recognition of the anomaly in student health conditions is beneficial to the quick discovery of potential patients. However, there are so many students in each school that the education managers cannot know about the health conditions of students in a real-time manner and accurately recognize the possible anomaly among students quickly. Fortunately, the quick development of mobile cloud computing technologies and wearable sensors has provided a promising way to monitor the real-time health conditions of students and find out the anomalies timely. However, two challenges are present in the above anomaly detection issue. First, the health data monitored by massive wearable sensors are often massive and updated frequently, which probably leads to high sensor-cloud transmission cost for anomaly detection. Second, the health data of students are often sensitive enough, which probably impedes the integration of health data in cloud environment even renders the health data-based anomaly detection infeasible. In view of these challenges, we propose a time-efficient and privacy-aware anomaly detection solution for students with wearable sensors in mobile cloud computing environment. At last, we validate the effectiveness and efficiency of our work via a set of simulated experiments.
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Full text: Available Collection: International databases Database: MEDLINE Type of study: Diagnostic study / Prognostic study Language: English Journal: J Cloud Comput (Heidelb) Year: 2022 Document Type: Article Affiliation country: S13677-022-00300-x

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Diagnostic study / Prognostic study Language: English Journal: J Cloud Comput (Heidelb) Year: 2022 Document Type: Article Affiliation country: S13677-022-00300-x