Abstract
In 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.
•HAR is a challenging task in remote monitoring of patients suffering from mental illness or disabilities.•Humans’ activities in healthcare reduces the qualitative measures for extracting the features representing human actions.•The proposed framework is based on multi-class cooperative categorization procedure to improve the activity classification accuracy.