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Fog computing is a modern computing model which offers geographically dispersed end-users with the latency-aware and highly scalable services. It is comparatively safer than cloud computing, due to information being rapidly stored and evaluated closer to data sources on local fog nodes. The advent of Blockchain (BC) technology has become a remarkable, most revolutionary, and growing development in recent years. BT's open platform stresses data protection and anonymity. It also guarantees data is protected and valid through the consensus process. BC is mainly used in money-related exchanges; now it will be used in many domains, including healthcare; This paper proposes efficient Blockchain-based secure healthcare services for disease prediction in fog computing. Diabetes and cardio diseases are considered for prediction. Initially, the patient health information is collected from Fog Nodes and stored on a Blockchain. The novel rule-based clustering algorithm is initially applied to cluster the patient health records. Finally, diabetic and cardio diseases are predicted using feature selection based adaptive neuro-fuzzy inference system (FS-ANFIS). To evaluate the performance of the proposed work, an extensive experiment and analysis were conducted on data from the real world healthcare. The experimental results show that the proposed work efficiently predict the disease.

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The paralyzed population is increasing worldwide due to stroke, spinal code injury, post-polio, and other related diseases. Different assistive technologies are used to improve the physical and mental health of the affected patients. Exoskeletons have emerged as one of the most promising technology to provide movement and rehabilitation for the paralyzed. But exoskeletons are limited by the constraints of weight, flexibility, and adaptability. To resolve these issues, we propose an adaptive and flexible Brain Energized Full Body Exoskeleton (BFBE) for assisting the paralyzed people. This paper describes the design, control, and testing of BFBE with 15 degrees of freedom (DoF) for assisting the users in their daily activities. The flexibility is incorporated into the system by a modular design approach. The brain signals captured by the Electroencephalogram (EEG) sensors are used for controlling the movements of BFBE. The processing happens at the edge, reducing delay in decision making and the system is further integrated with an IoT module that helps to send an alert message to multiple caregivers in case of an emergency. The potential energy harvesting is used in the system to solve the power issues related to the exoskeleton. The stability in the gait cycle is ensured by using adaptive sensory feedback. The system validation is done by using six natural movements on ten different paralyzed persons. The system recognizes human intensions with an accuracy of 85%. The result shows that BFBE can be an efficient method for providing assistance and rehabilitation for paralyzed patients.

Abstract:The E-health clouds generate an enormous amount of data driven from PHR's, EHR's, EMR's, patient care, compliance and regulatory requirements. To preserve the privacy and confidentiality of the e-health data at untrusted servers various solutions on symmetric key-based access control has aroused. But the major drawback of these techniques is that it is patient centric and do not provide security and fine-grained access control. To solve these issues the paper defines a secure access control scheme for E-health clouds. First, an efficient architecture for E-health clouds is stated and then the access control scheme is defined. As the data stored at the cloud server is highly confidential first a three-factor mutual authentication is made between the cloud server and the data user. Further, the proposed technique makes use of Attribute based searchable encryption with trapdoor function that prevents unauthorised access to the cloud data in an efficient way. The experiment is conducted using charm crypto and the results show that the proposed system provides comparatively better results than the existing techniques.