Smart Healthcare Activity Recognition Using Statistical Regression and Intelligent Learning

In this present time,Human Activity Recognition(HAR)has been of considerable aid in the case of health monitoring and recovery.The exploitation of machine learning with an intelligent agent in the area of health informatics gathered using HAR augments the decision-making quality and significance.Although many research works conducted on Smart Healthcare Monitoring,there remain a certain number of pitfalls such as time,overhead,and falsification involved during analysis.Therefore,this paper proposes a Statistical Partial Regression and Support Vector Intelligent Agent Learning(SPR-SVIAL)for Smart Healthcare Monitoring.At first,the Statistical Partial Regression Feature Extraction model is used for data preprocessing along with the dimensionality-reduced features extraction process.Here,the input dataset the continuous beat-to-beat heart data,triaxial accelerometer data,and psychological characteristics were acquired from IoT wearable devices.To attain highly accurate Smart Healthcare Monitoring with less time,Partial Least Square helps extract the dimensionality-reduced features.After that,with these resulting features,SVIAL is proposed for Smart Healthcare Monitoring with the help of Machine Learning and Intelligent Agents to minimize both analysis falsification and overhead.Experimental evaluation is carried out for factors such as time,overhead,and false positive rate accuracy concerning several instances.The quantitatively analyzed results indicate the better performance of our proposed SPR-SVIAL method when compared with two state-of-the-art methods.

An IoT Based Secure Patient Health Monitoring System

Internet of things(IoT)field has emerged due to the rapid growth of artificial intelligence and communication technologies.The use of IoT technology in modern healthcare environments is convenient for doctors and patients as it can be used in real-time monitoring of patients,proper administration of patient information,and healthcare management.However,the usage of IoT in the healthcare domain will become a nightmare if patient information is not securely maintainedwhile transferring over an insecure network or storing at the administrator end.In this manuscript,the authors have developed a secure IoT healthcare monitoring system using the Blockchainbased XOR Elliptic Curve Cryptography(BC-XORECC)technique to avoid various vulnerable attacks.Initially,thework has established an authentication process for patient details by generating tokens,keys,and tags using Length Ceaser Cipher-based PearsonHashingAlgorithm(LCC-PHA),EllipticCurve Cryptography(ECC),and Fishers Yates Shuffled Based Adelson-Velskii and Landis(FYS-AVL)tree.The authentications prevent unauthorized users from accessing or misuse the data.After that,a secure data transfer is performed using BC-XORECC,which acts faster by maintaining high data privacy and blocking the path for the attackers.Finally,the Linear Spline Kernel-Based Recurrent Neural Network(LSK-RNN)classification monitors the patient’s health status.The whole developed framework brings out a secure data transfer without data loss or data breaches and remains efficient for health care monitoring via IoT.Experimental analysis shows that the proposed framework achieves a faster encryption and decryption time,classifies the patient’s health status with an accuracy of 89%,and remains robust comparedwith the existing state-of-the-art method.

Deployment of Hash Function to Enhance Message Integrity in Wireless Body Area Network (WBAN)

Message integrity is found to prove the transfer information of patient in health care monitoring system on the human body in order to collect and communicate the human personal data. Wireless body area network (WBAN) applications are the fast growing technology trend but security and privacy are still largely ignored, since they are hard to achieve given the limited computation and energy resources available at sensor node level. In this paper, we propose simple hash based message authentication and integrity code algorithm for wireless sensor networks. We test the proposed algorithm in MATLAB on path loss model around the human body in two scenarios and compare the result before and after enhancement and show how sensors are connected with each other to prove the message integrity in monitoring health environment.

Dry Fiber‑Based Electrodes for Electrophysiology Applications

Long-term continuous health care monitoring,using wearable technologies has received considerable interest due to the significant contribution of wearables to the diagnosis of diseases and identification of health conditions.Fibers have been widely applied in human societies due to their unique advantages,including stretchability,small diameters,high dynamic bending elasticity,high length-to-width ratios,and mechanical strength.A new generation of fiber-based electrodes is being integrated into smart textiles and wearables for continuous long-term biosignal monitoring.Dry fiber-based electrodes are breathable,flexible,and durable,unlike conventional disposable gel electrodes,which are difficult to employ for long-term applications because of skin irritation and allergic responses caused by their moist and adhesive interface with the skin.In this review,we provide a concise summary of recent breakthroughs in the design,and manufacturing of dry fiber-based electrodes for electrophysiology applications,with a particular emphasis on applications in electrocardiography,electromyography,and electroencephalography.Focusing on numerous features of electroactive fiber materials,fiber processing,electrode fabrication,scaled-up manufacturing,standardization of testing and performance criteria,we discuss current limitations and provide an outlook for the future development of this field.