Security of personal data across national borders

Tran, Elizabeth; Atkinson, MaryAnne

doi:10.1108/09685220210446588

Cited by 9 publications

(2 citation statements)

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“…Literature [ 2 ] designed an aerobics auxiliary evaluation system based on big data and motion recognition algorithm. Literature [ 3 ] talks about personal data privacy protection in the era of big data. In reference [ 4 ], a new motion recognition method based on key frame and skeleton information is proposed by using Kinect v2 and the weighted K-means algorithm.…”

Section: Introductionmentioning

confidence: 99%

Aided Evaluation of Motion Action Based on Attitude Recognition

Wang

2022

Journal of Healthcare Engineering

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For athletes who are eager for success, it is difficult to obtain their own movement data due to field equipment, artificial errors, and other factors, which means that they cannot get professional movement guidance and posture correction from sports coaches, which is a disastrous problem. To solve this big problem, combined with the latest research results of deep learning in the field of computer technology, based on the related technology of human posture recognition, this paper uses convolution neural network and video processing technology to create an auxiliary evaluation system of sports movements, which can obtain accurate data and help people interact with each other, so as to help athletes better understand their body posture and movement data. The research results show that: (1) using OpenPose open-source library for pose recognition, joint angle data can be obtained through joint coordinates, and the key points of video human posture can be identified and calculated for easy analysis. (2) The movements of the human body in the video are evaluated. In this way, it is judged whether the action amplitude of the detected target conforms to the standard action data. (3) According to the standard motion database created in this paper, a formal motion auxiliary evaluation system is established; compared with the standard action, the smaller the Euclidean distance is, the more standard it is. The action with an Euclidean distance of 4.79583 is the best action of the tested person. (4) The efficiency of traditional methods is very low, and the correct recognition rate of the method based on BP neural network can be as high as 96.4%; the correct recognition rate of the attitude recognition method based on this paper can be as high as 98.7%, which is 2.3% higher than the previous method. Therefore, the method in this paper has great advantages. The research results of the sports action assistant evaluation system in this paper are good, which effectively solves the difficult problems that plague athletes and can be considered to have achieved certain success; the follow-up system test and operation work need further optimization and research by researchers.

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Section: Introductionmentioning

confidence: 99%

Aided Evaluation of Motion Action Based on Attitude Recognition

Wang

2022

Journal of Healthcare Engineering

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“…International cloud service providers must be capable of satisfying users’ demands for cloud computing in a global marketplace. Therefore, regardless of their essential understanding about the technology that they are providing via their international platforms, they must be familiar with the data protection laws and policies of each country that they have a presence in, if they are keen to successfully run an international business (Tran & Atkinson, 2002). In addition, the policies regarding data transfer between countries need to be seriously accounted for in their business plans.…”

Section: Resultsmentioning

confidence: 99%

The Challenges of Institutional Distance: Data Privacy Issues in Cloud Computing

Abed¹,

Chavan²

2018

Science, Technology and Society

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Data protection and data privacy are significant challenges in cloud computing for multinational corporations. There are no standard laws to protect data across borders. The institutional and regulatory constraints and governance differ across countries. This article explores the challenges of institutional constraints faced by cloud computing service providers in regard to data privacy issues across borders. Through a qualitative case study methodology, this research compares the institutional structure of a few host countries, with regard to data privacy in cloud computing and delineates a relative case study. This article will also review the cloud computing legal frameworks and the history of cloud computing to make the concept more comprehensible to a layman.

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