Mobile computing-enabled health physique evaluation in campus based on amplified hashing

Abstract

With the increasingly wide spread of COVID-19 pandemic, people’s various behavior activities are influenced more or less all over the world. For example, students in campus have to learn at home or in dormitory so as to avoid the attacks of the virus as much as possible. However, such a location distribution structure of student places a heavy burden on the monitoring and evaluating the sport physique of students in an effective and efficient way. Fortunately, the wide adoption of various mobile computing terminals (e.g., smart watches, mobile phones, etc.) and wireless communication technology makes it possible to know about the daily physique of students in a remote way. However, students’ health physique data are accumulated with time, which raises a challenge of quick data processing and cost-effective data scalability. Moreover, since the students are geographically distributed, we need to integrate their respective health physique data into a central cloud platform for more comprehensive data analysis and mining. However, the above data integration operations often involve student privacy. Motivated by the above two challenges, a mobile computing-aided health physique evaluation solution is brought forth in this paper, which is mainly based on a kind of amplified hashing technique. To prove the evaluation performances of the proposal, extensive experiments are designed to test the algorithm performances in terms of various evaluation metrics.