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A blockchain-based fog computing framework for activity recognition as an application to e-Healthcare services

机译：用于活动识别的基于区块链的雾计算框架，作为电子医疗服务的应用程序

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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. (C) 2019 Elsevier B.V. All rights reserved.
机译：在现代的电子医疗系统中，人类活动识别（HAR）是在远程监视精神疾病或残疾患者以寻求必要协助方面最具挑战性的任务之一。主要问题之一是为互联网上的许多不同连接的设备提供安全性，这些设备被称为物联网（IoT）。这个问题的潜在解决方案是基于区块链的架构。另外，人类在各种医疗环境中进行的活动的复杂性降低了用于提取代表各种人类行为的不同特征的定性措施。为了应对这一挑战，我们提出了一种活动监视和识别框架，该框架基于多类协作分类过程，以提高支持基于雾或基于云计算的区块链架构的视频中的活动分类准确性。在提出的方法中，从包含不同人类活动的视频中提取基于帧的显着特征，将其进一步处理为动作词汇以提高效率和准确性。类似地，基于错误校正输出代码（ECOC）框架，使用支持向量机（SVM）对活动进行分类。通过实验结果已经观察到，与其他最新的动作识别方法相比，所提出的方法在人类活动识别方面更加有效并且实现了更高的准确性。 （C）2019 Elsevier B.V.保留所有权利。

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