Intelligent Sensing Technology, Smart Healthcare Services, and Internet of Medical Things-based Diagnosis

2019 ◽  
Vol 6 (1) ◽  
pp. 13 ◽  
Keyword(s):  
Healthcare Services ◽  
Smart Healthcare ◽  
Sensing Technology ◽  
Internet Of Medical Things

Enhanced Sensing and Activity Recognition System Using IoT for Healthcare

2021 ◽  
Vol 13 (2) ◽  
pp. 42-49
Author(s):  
Yamini G. ◽  
Gopinath Ganapathy
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Healthcare Services ◽  
Recognition System ◽  
Physical Activities ◽  
Video Data ◽  
Large Coupling ◽  
Sensing Technology ◽  
Medical Benefits ◽  
Primary Focus

Through the integration of advanced algorithms and smart sensing technology in healthcare services, huge medical benefits could be gained by the aged and sick people in determining their activity recognition. Human activity recognition (HAR) is still in the research for the past decades that promotes recognition of physical activities automatically. The main aim of HAR is to obtain and analyze the physical activities of a person, which could be promoted through several in-built sensors examined in the form of video data. Through this technique, necessary information could be obtained that also helps in preventing significant risks and also averts or alerts unfortunate events from happening. However, there is no particular categorization for human activity, and there is no description of the particular events to occur. The objective of this paper is to propose a healthcare information system based on IoT where enhancing activity recognition is the primary focus. Human activities are supposed to be diverse; it is necessary to choose appropriate sensors and the effective placement of those sensors in recognizing specific activities. One of the major challenges here is choosing the appropriate sensor for that particular instance and gathering data under particular circumstances. Due to the large coupling of sensors and their activity monitoring functionality, the solution to promote feasibility for the HAR predicament cannot be determined. A distinguishing feature of this paper is that it includes future users' perspectives.

Malware Detection Approaches and Analysis for the Internet of Medical Things Enabled Healthcare Systems

2021 ◽  
Vol 10 (6) ◽  
pp. 3117-3121
Keyword(s):  
Denial Of Service ◽  
Malware Detection ◽  
Healthcare Systems ◽  
Information And Communications Technology ◽  
The Internet ◽  
Smart Healthcare ◽  
Man In The Middle ◽  
Internet Of Medical Things ◽  
Detection Schemes ◽  
The Internet Of Things

The advancement of information and communications technology has changed an IoMT-enabled healthcare system. The Internet of Medical Things (IoMT) is a subset of the Internet of Things (IoT) that focuses on smart healthcare (medical) device connectivity. While the Internet of Medical Things (IoMT) communication environment facilitates and supports our daily health activities, it also has drawbacks such as password guessing, replay, impersonation, remote hijacking, privileged insider, denial of service (DoS), and man-in-the-middle attacks, as well as malware attacks. Malware botnets cause assaults on the system's data and other resources, compromising its authenticity, availability, confidentiality and, integrity. In the event of such an attack, crucial IoMT communication data may be exposed, altered, or even unavailable to authorised users. As a result, malware protection for the IoMT environment becomes critical. In this paper, we provide several forms of malware attacks and their consequences. We also go through security, privacy, and different IoMT malware detection schemes

IOT-Based Smart Healthcare System: A Review on Constituent Technologies

2021 ◽  
pp. 2130008
Author(s):  
Manju Lata Sahu ◽  
Mithilesh Atulkar ◽  
Mitul Kumar Ahirwal
Keyword(s):  
Healthcare System ◽  
Service Providers ◽  
Data Communication ◽  
Healthcare Services ◽  
Vital Role ◽  
Medical Practitioners ◽  
Smart Healthcare ◽  
Rural And Remote Areas ◽  
Medical Infrastructure ◽  
Physiological Sensing

The revolution in the Internet of Things (IoT) is redesigning and reshaping the healthcare system technologically, economically and socially. The emerging and rapidly growing IoT-based Smart Healthcare System (SHCS) is seen as a sustainable solution to reduce the burden on the existing healthcare system due to increasing diseases and limited medical infrastructure. IoT-based SHCS plays a vital role in delivery of healthcare services in rural and remote areas where the essential medical amenities, necessary infrastructures and qualified medical practitioners are not available. Therefore, in this paper, a comprehensive investigation of futuristic IoT-based SHCS and its constituents is presented. This paper provides exhaustive review on different techniques and technologies dealing with smart healthcare framework, physiological sensing, signal processing, data communication, cloud computing and data analytics used in IoT-based SHCS. A comparative analysis of existing literature has been carried out to identify the recent trends and advancements in this very dynamic field of global importance. In addition to this, it highlights different issues and challenges, along with the recommendation for further research in the field. The prime objective of this paper is to deliver the state-of-the-art understanding and update about IoT-based SHCS and its constituents by providing a good source of information to the researchers, service providers, technologists, medical practitioners and the general population.

Wearable Systems for Monitoring Mobility Related Activities

2011 ◽  
pp. 801-824 ◽  
Author(s):  
Wiebren Zijlstra ◽  
Clemens Becker ◽  
Klaus Pfeiffer
Keyword(s):  
Real Life ◽  
Healthcare Services ◽  
Sensing Technology ◽  
Mobility Assessment ◽  
Wearable Systems ◽  
Assessment And Monitoring ◽  
Single Sensor ◽  
Wearable Monitoring Systems ◽  
Older Populations ◽  
User Perspectives

Monitoring the performance of daily life mobility related activities, such as rising from a chair, standing and walking may be used to support healthcare services. This chapter identifies available wearable motion-sensing technology; its (potential) clinical application for mobility assessment and monitoring; and it addresses the need to assess user perspectives on wearable monitoring systems. Given the basic requirements for application under real-life conditions, this chapter emphasizes methods based on single sensor locations. A number of relevant clinical applications in specific older populations are discussed; i.e. (risk-) assessment, evaluation of changes in functioning, and monitoring as an essential part of exercise-based interventions. Since the application of mobility monitoring as part of existing healthcare services for older populations is rather limited, this chapter ends with issues that need to be addressed to effectively implement techniques for mobility monitoring in healthcare.

Internet of Things (IOT) in Healthcare – Smart Health and Surveillance, Architectures, Security Analysis and Data Transfer

2019 ◽  
Vol 7 (2) ◽  
pp. 21-40 ◽  
Author(s):  
Parthasarathy Panchatcharam ◽  
Vivekanandan S.
Keyword(s):  
Health Care ◽  
Internet Of Things ◽  
Data Transfer ◽  
Security Analysis ◽  
Healthcare Services ◽  
The Internet ◽  
Quality Health Care ◽  
Smart Healthcare ◽  
Smart Health ◽  
The Internet Of Things

Wellbeing is fundament requirement. What's more, it is human appropriate to get quality health care. These days, India is confronting numerous medical problems in light of fewer assets. This survey article displays the idea of solving health issues by utilizing a recent innovation, the Internet of Things (IOT). The Internet of Things with their developing interdisciplinary applications has changed our lives. Smart health care being one such IoT application interfaces brilliant gadgets, machines, patients, specialists, and sensors to the web. At long last, the difficulties and prospects of the improvement of IoT-based medicinal service frameworks are talked about in detail. This review additionally summarizes the security and protection worries of IoT, administrations and application of IoT and smart healthcare services that have changed the customary medicinal services framework by making healthcare administration more proficient through their applications.

scholarly journals A Study of Prescriptive Analysis Framework for Human Care Services Based On CKAN Cloud

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Jangwon Gim ◽  
Sukhoon Lee ◽  
Wonkyun Joo
Keyword(s):  
Healthcare Services ◽  
Current Situation ◽  
Sensor Data ◽  
Technological Advancement ◽  
Analysis Framework ◽  
Data Interoperability ◽  
Care Services ◽  
Smart Healthcare ◽  
Future Events ◽  
Prescriptive Analysis

A number of sensor devices are widely distributed and used today owing to the accelerated development of IoT technology. In particular, this technological advancement has allowed users to carry IoT devices with more convenience and efficiency. Based on the IoT sensor data, studies are being actively carried out to recognize the current situation or to analyze and predict future events. However, research for existing smart healthcare services is focused on analyzing users’ behavior from single sensor data and is also focused on analyzing and diagnosing the current situation of the users. Therefore, a method for effectively managing and integrating a large amount of IoT sensor data has become necessary, and a framework considering data interoperability has become necessary. In addition, an analysis framework is needed not only to provide the analysis of the users’ environment and situation from the integrated data, but also to provide guide information to predict future events and to take appropriate action by users. In this paper, we propose a prescriptive analysis framework using a 5W1H method based on CKAN cloud. Through the CKAN cloud environment, IoT sensor data stored in individual CKANs can be integrated based on common concepts. As a result, it is possible to generate an integrated knowledge graph considering interoperability of data, and the underlying data is used as the base data for prescriptive analysis. In addition, the proposed prescriptive analysis framework can diagnose the situation of the users through analysis of user environment information and supports users’ decision making by recommending the possible behavior according to the coming situation of the users. We have verified the applicability of the 5W1H prescriptive analysis framework based on the use case of collecting and analyzing data obtained from various IoT sensors.

scholarly journals An Astrocyte-Flow Mapping on a Mesh-Based Communication Infrastructure to Defective Neurons Phagocytosis

Mathematics ◽  
2021 ◽  
Vol 9 (23) ◽  
pp. 3012
Author(s):  
Amir Masoud Rahmani ◽  
Rizwan Ali Naqvi ◽  
Saqib Ali ◽  
Seyedeh Yasaman Hosseini Mirmahaleh ◽  
Mohammed Alswaitti ◽  
...  
Keyword(s):  
Healthcare Services ◽  
Machine Learning Algorithms ◽  
Mesh Network ◽  
The Internet ◽  
Minimal Cost ◽  
Flow Mapping ◽  
Communication Infrastructure ◽  
Processing Elements ◽  
Internet Of Medical Things ◽  
The Internet Of Things

In deploying the Internet of Things (IoT) and Internet of Medical Things (IoMT)-based applications and infrastructures, the researchers faced many sensors and their output’s values, which have transferred between service requesters and servers. Some case studies addressed the different methods and technologies, including machine learning algorithms, deep learning accelerators, Processing-In-Memory (PIM), and neuromorphic computing (NC) approaches to support the data processing complexity and communication between IoMT nodes. With inspiring human brain structure, some researchers tackled the challenges of rising IoT- and IoMT-based applications and neural structures’ simulation. A defective device has destructive effects on the performance and cost of the applications, and their detection is challenging for a communication infrastructure with many devices. We inspired astrocyte cells to map the flow (AFM) of the Internet of Medical Things onto mesh network processing elements (PEs), and detect the defective devices based on a phagocytosis model. This study focuses on an astrocyte’s cholesterol distribution into neurons and presents an algorithm that utilizes its pattern to distribute IoMT’s dataflow and detect the defective devices. We researched Alzheimer’s symptoms to understand astrocyte and phagocytosis functions against the disease and employ the vaccination COVID-19 dataset to define a set of task graphs. The study improves total runtime and energy by approximately 60.85% and 52.38% after implementing AFM, compared with before astrocyte-flow mapping, which helps IoMT’s infrastructure developers to provide healthcare services to the requesters with minimal cost and high accuracy.

scholarly journals An IoMT-Enabled Smart Healthcare Model to Monitor Elderly People Using Machine Learning Technique

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Muhammad Farrukh Khan ◽  
Taher M. Ghazal ◽  
Raed A. Said ◽  
Areej Fatima ◽  
Sagheer Abbas ◽  
...  
Keyword(s):  
Machine Learning ◽  
Elderly People ◽  
Nearest Neighbor ◽  
Quality Of Healthcare ◽  
Healthcare Services ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Smart Healthcare ◽  
Healthcare Model ◽  
The Cost

The Internet of Medical Things (IoMT) enables digital devices to gather, infer, and broadcast health data via the cloud platform. The phenomenal growth of the IoMT is fueled by many factors, including the widespread and growing availability of wearables and the ever-decreasing cost of sensor-based technology. The cost of related healthcare will rise as the global population of elderly people grows in parallel with an overall life expectancy that demands affordable healthcare services, solutions, and developments. IoMT may bring revolution in the medical sciences in terms of the quality of healthcare of elderly people while entangled with machine learning (ML) algorithms. The effectiveness of the smart healthcare (SHC) model to monitor elderly people was observed by performing tests on IoMT datasets. For evaluation, the precision, recall, fscore, accuracy, and ROC values are computed. The authors also compare the results of the SHC model with different conventional popular ML techniques, e.g., support vector machine (SVM), K-nearest neighbor (KNN), and decision tree (DT), to analyze the effectiveness of the result.

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