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AuthorIslam N.
AuthorFaheem Y.
AuthorDin I.U.
AuthorTalha M.
AuthorGuizani M.
AuthorKhalil M.
Available date2020-04-05T10:53:20Z
Publication Date2019
Publication NameFuture Generation Computer Systems
ResourceScopus
ISSN0167739X
URIhttp://dx.doi.org/10.1016/j.future.2019.05.059
URIhttp://hdl.handle.net/10576/13808
AbstractIn modern e-Healthcare systems, human activity recognition (HAR) is one of the most challenging tasks in remote monitoring of patients suffering from mental illness or disabilities for necessary assistance. One of the major issues is to provide security to a number of different connected devices to the Internet, known as Internet of Things (IoT). A potential solution to this problem is the blockchain-based architecture. In addition, the complex nature of activities performed by humans in diverse healthcare environments reduces the qualitative measures for extracting distinct features representing various human actions. To answer this challenge, we propose an activity monitoring and recognition framework, which is based on multi-class cooperative categorization procedure to improve the activity classification accuracy in videos supporting the fog or cloud computing-based blockchain architecture. In the proposed approach, frame-based salient features are extracted from videos consisting of different human activities, which are further processed into action vocabulary for efficiency and accuracy. Similarly, the classification of activities is performed using support vector machine (SVM) based on the error-correction-output-codes (ECOC) framework. It has been observed through experimental results that the proposed approach is more efficient and achieves higher accuracy regarding human activity recognition as compared to other state-of-the-art action recognition approaches. - 2019 Elsevier B.V.
Languageen
PublisherElsevier B.V.
SubjectCloud computing
e-Health
Error-correcting-output-code
Fog computing
Human action recognition
Support vector machine
TitleA blockchain-based fog computing framework for activity recognition as an application to e-Healthcare services
TypeArticle
Pagination569-578
Volume Number100
dc.accessType Abstract Only


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