scholarly journals A Benchmark Database and Baseline Evaluation for Fall Detection Based on Wearable Sensors for the Internet of Medical Things Platform

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 51286-51296 ◽  
Author(s):  
Zhi Liu ◽  
Yankun Cao ◽  
Lizhen Cui ◽  
Jiahua Song ◽  
Guangzhe Zhao
Keyword(s):  
Wearable Sensors ◽  
Fall Detection ◽  
The Internet ◽  
Benchmark Database ◽  
Baseline Evaluation ◽  
Internet Of Medical Things

Mutual Authentication Scheme for the Device-to-Server Communication in the Internet of Medical Things

2021 ◽  
pp. 1-1
Author(s):  
Jiangfeng Sun ◽  
Fazlullah Khan ◽  
Junxia Li ◽  
Mohammad Dahman Alshehri ◽  
Ryan Alturki ◽  
...  
Keyword(s):  
Mutual Authentication ◽  
Authentication Scheme ◽  
The Internet ◽  
Internet Of Medical Things

EWPS: Emergency data communication in the Internet of Medical Things

2021 ◽  
pp. 1-1
Author(s):  
S. Gopikrishnan ◽  
P. Priakanth ◽  
Gautam Srivastava ◽  
Giancarlo Fortino
Keyword(s):  
Data Communication ◽  
The Internet ◽  
Internet Of Medical Things

scholarly journals On the internet of medical things

2020 ◽  
Vol 63 (8) ◽  
pp. 5-5
Author(s):  
Vinton G. Cerf
Keyword(s):  
The Internet ◽  
Internet Of Medical Things

scholarly journals Gynecology Meets Big Data in the Disruptive Innovation Medical Era: State-of-Art and Future Prospects

2021 ◽  
Vol 18 (10) ◽  
pp. 5058
Author(s):  
Rola Khamisy-Farah ◽  
Leonardo B. Furstenau ◽  
Jude Dzevela Kong ◽  
Jianhong Wu ◽  
Nicola Luigi Bragazzi
Keyword(s):  
Big Data ◽  
Information And Communication Technologies ◽  
Imaging Techniques ◽  
Treatment Options ◽  
Wearable Sensors ◽  
Three Dimensional ◽  
Disruptive Innovation ◽  
Healthcare Management ◽  
The Internet ◽  
Big Data Visualization

Tremendous scientific and technological achievements have been revolutionizing the current medical era, changing the way in which physicians practice their profession and deliver healthcare provisions. This is due to the convergence of various advancements related to digitalization and the use of information and communication technologies (ICTs)—ranging from the internet of things (IoT) and the internet of medical things (IoMT) to the fields of robotics, virtual and augmented reality, and massively parallel and cloud computing. Further progress has been made in the fields of addictive manufacturing and three-dimensional (3D) printing, sophisticated statistical tools such as big data visualization and analytics (BDVA) and artificial intelligence (AI), the use of mobile and smartphone applications (apps), remote monitoring and wearable sensors, and e-learning, among others. Within this new conceptual framework, big data represents a massive set of data characterized by different properties and features. These can be categorized both from a quantitative and qualitative standpoint, and include data generated from wet-lab and microarrays (molecular big data), databases and registries (clinical/computational big data), imaging techniques (such as radiomics, imaging big data) and web searches (the so-called infodemiology, digital big data). The present review aims to show how big and smart data can revolutionize gynecology by shedding light on female reproductive health, both in terms of physiology and pathophysiology. More specifically, they appear to have potential uses in the field of gynecology to increase its accuracy and precision, stratify patients, provide opportunities for personalized treatment options rather than delivering a package of “one-size-fits-it-all” healthcare management provisions, and enhance its effectiveness at each stage (health promotion, prevention, diagnosis, prognosis, and therapeutics).

Deep Deterministic Learning for Pattern Recognition of Different Cardiac Diseases through the Internet of Medical Things

2018 ◽  
Vol 42 (12) ◽  
Author(s):  
Uzair Iqbal ◽  
Teh Ying Wah ◽  
Muhammad Habib ur Rehman ◽  
Ghulam Mujtaba ◽  
Muhammad Imran ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
The Internet ◽  
Cardiac Diseases ◽  
Deterministic Learning ◽  
Internet Of Medical Things

scholarly journals Machine Learning based Human Fall Detection System

2021 ◽  
Vol 9 (VI) ◽  
pp. 2677-2682
Author(s):  
Nishanth P
Keyword(s):  
Machine Learning ◽  
Detection System ◽  
Wearable Sensors ◽  
Fall Detection ◽  
The Elderly ◽  
World Health ◽  
Support Vector ◽  
Data Output ◽  
Health Organization ◽  
Context Features

Falls have become one of the reasons for death. It is common among the elderly. According to World Health Organization (WHO), 3 out of 10 living alone elderly people of age 65 and more tend to fall. This rate may get higher in the upcoming years. In recent years, the safety of elderly residents alone has received increased attention in a number of countries. The fall detection system based on the wearable sensors has made its debut in response to the early indicator of detecting the fall and the usage of the IoT technology, but it has some drawbacks, including high infiltration, low accuracy, poor reliability. This work describes a fall detection that does not reliant on wearable sensors and is related on machine learning and image analysing in Python. The camera's high-frequency pictures are sent to the network, which uses the Convolutional Neural Network technique to identify the main points of the human. The Support Vector Machine technique uses the data output from the feature extraction to classify the fall. Relatives will be notified via mobile message. Rather than modelling individual activities, we use both motion and context information to recognize activities in a scene. This is based on the notion that actions that are spatially and temporally connected rarely occur alone and might serve as background for one another. We propose a hierarchical representation of action segments and activities using a two-layer random field model. The model allows for the simultaneous integration of motion and a variety of context features at multiple levels, as well as the automatic learning of statistics that represent the patterns of the features.

scholarly journals A Review on Data Securing Techniques using Internet of Medical Things

2021 ◽  
Vol 3 (3) ◽  
pp. 150-163
Author(s):  
P. P. Joby
Keyword(s):  
Older People ◽  
Basic Principle ◽  
Medical Devices ◽  
Data Security ◽  
Critical Factor ◽  
The Internet ◽  
Major Goal ◽  
Internet Of Medical Things ◽  
Machine To Machine Communication ◽  
Machine Communication

At present, the traditional healthcare system is completely replaced by the revolutionary technique, the Internet of Medical Things (IoMT). Internet of Medical Things is the IoT hub that comprises of medical devices and applications which are interconnected through online computer networks. The basic principle of IoMT is machine-to-machine communication that takes place online. The major goal of IoMT is to reduce frequent or unwanted visits to the hospitals which makes it comfortable and is also highly preferred by the older people. Another advantage of this methodology is that the interpreted or collected data is stored in cloud modules unlike amazon and Mhealth, making it accessible remotely. Although there are countless advantages in IoMT, the critical factor lies in data security or encryption. A surplus number of threat related to devices, connectivity, and cloud might occur under unforeseen or threatening circumstances which makes the person in the situation helpless. Yet, with the help of data security techniques designed especially for Internet of Medical Things, it is possible to address these challenges. In this paper, a review on data securing techniques for the internet of medical things is made along with a discussion on related concepts.

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