Wearable Device Based on IoT in the Healthcare System for Disease Detection and Symptom Recognition

2021 ◽  
Vol 30 (06n08) ◽  
Author(s):  
Manas Ranjan Pradhan ◽  
Karamath Ateeq ◽  
Beenu Mago
Keyword(s):  
Patient Information ◽  
Food Habits ◽  
Interaction Analysis ◽  
Wearable Sensors ◽  
Disease Prediction ◽  
Good Shape ◽  
Symptom Recognition ◽  
Health Support ◽  
Health Situation ◽  
The Internet Of Things

Humans in good shape face many challenges in their lives, such as food habits and climate change. The result must be aware of the health situation to survive. Lack of accurate patient information, preventive errors, data risks, overdiagnosis, and delayed implementation are challenges that health support services face. Wearable sensors that connect extensive data, data mining analysis for healthcare, and the Internet of things (IoT) have been proposed to solve this problem. This research, Disease Prediction and Symptom Recognition Model using IoT (DDSR-IoT) framework, is proposed for reasoning with regression rules to gather patient information. The Boltzmann network to train Artificial Intelligence (AI) feedback is introduced in the end. As a result, the broad interaction analysis of genomes is used to predict conditions. If those infections have affected people, emails are sent to warn them and provide them with prescriptions and medical advice. In the recommended approach, the experimental study resulted in an enhanced forecast rate of 97.4 percent and a precision of 97.42 percent.

scholarly journals A Privacy Protection User Authentication and Key Agreement Scheme Tailored for the Internet of Things Environment: PriAuth

2017 ◽  
Vol 2017 ◽  
pp. 1-17 ◽  
Author(s):  
Yuwen Chen ◽  
José-Fernán Martínez ◽  
Pedro Castillejo ◽  
Lourdes López
Keyword(s):  
User Authentication ◽  
Wearable Sensors ◽  
Mutual Authentication ◽  
Sensor Data ◽  
Gateway Node ◽  
Diffie Hellman ◽  
Internet Of Things Environment ◽  
Patient Will ◽  
Suitable Treatment ◽  
The Internet Of Things

In a wearable sensor-based deployment, sensors are placed over the patient to monitor their body health parameters. Continuous physiological information monitored by wearable sensors helps doctors have a better diagnostic and a suitable treatment. When doctors want to access the patient’s sensor data remotely via network, the patient will authenticate the identity of the doctor first, and then they will negotiate a key for further communication. Many lightweight schemes have been proposed to enable a mutual authentication and key establishment between the two parties with the help of a gateway node, but most of these schemes cannot enable identity confidentiality. Besides, the shared key is also known by the gateway, which means the patient’s sensor data could be leaked to the gateway. In PriAuth, identities are encrypted to guarantee confidentiality. Additionally, Elliptic Curve Diffie–Hellman (ECDH) key exchange protocol has been adopted to ensure the secrecy of the key, avoiding the gateway access to it. Besides, only hash and XOR computations are adopted because of the computability and power constraints of the wearable sensors. The proposed scheme has been validated by BAN logic and AVISPA, and the results show the scheme has been proven as secure.

Implementation of Disease Prediction and Drug Recommendation

2019 ◽  
Vol 16 (8) ◽  
pp. 3300-3303
Author(s):  
G. Likhithaa ◽  
T. Renuka ◽  
A. Christy
Keyword(s):  
Medication Errors ◽  
Patient Information ◽  
Error Rate ◽  
Medical Records ◽  
Current Method ◽  
Reliable Information ◽  
Disease Prediction ◽  
Staff Time ◽  
Electronic Prescription ◽  
Classification Technique

Medication errors are one of the major problems mostly seen in the hospitals. Manual prescription of medicine is difficult now days, so electronic prescription came into form. It is the alternative to the current method of manually prescription in hospitals. This paper introduces a simple and easy classification technique that can be used to prescribe drugs according to the symptom parameters and if higher rate of symptoms exist then a better hospital for the treatment is suggested for the patient. It has many benefits for those who prescribe and dispense the medicines, and also for maintenance of medical records. The complexity of the usage of medications has increased enormously. Due to the higher demand it can lead to a greater risk of errors. Hence the usage of E-prescribing has been developed out of it with less error rate. It provides better and more reliable information about the patient’s medication. It saves staff time and improves the availability of patient information when needed. It also reduces the time spent by rewriting the charts of prescription etc.

scholarly journals Smart Cities using Internet of Things: Recent Trends and Techniques

2019 ◽  
Vol 8 (9S) ◽  
pp. 24-28
Keyword(s):  
Internet Of Things ◽  
Smart Cities ◽  
Wearable Sensors ◽  
The Internet ◽  
Human Beings ◽  
Smart Building ◽  
Recent Trends ◽  
The Internet Of Things ◽  
Smart Infrastructure

A Smart Cities focuses on the way we live. Smart governments are also acknowledged as augmentations of electronic governments based on the Internet of Things (IoT). There are many existing challenges in the environment such as, research in gadgets, framework and programming etc. Particularly, the Smart Cities are facing difficulties with IoT frameworks, systems administration, independent registration, wearable sensors, gadgets and systematization of aggregates including human beings as well as programming specialists. This paper incorporates role of Smart Cities in various domains such as smart infrastructure, smart building, smart security and so on. Moreover, the work depicts the IoT technologies for Smart Cities and the primary components along with the features of Smart Cities. This paper is based on technologies for Smart Cities which will benefit citizens by facilitating a platform for integrating all the resources and prompt communication of information. Furthermore, merits, demerits and main challenges of Smart Cities are discussed.

scholarly journals ADLAuth: Passive Authentication Based on Activity of Daily Living Using Heterogeneous Sensing in Smart Cities

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2466 ◽  
Author(s):  
Maryam Naseer Malik ◽  
Muhammad Awais Azam ◽  
Muhammad Ehatisham-Ul-Haq ◽  
Waleed Ejaz ◽  
Asra Khalid
Keyword(s):  
Internet Of Things ◽  
User Authentication ◽  
Smart Cities ◽  
Activity Patterns ◽  
Wearable Sensors ◽  
Machine Learning Algorithms ◽  
Smart Devices ◽  
The Internet ◽  
Healthcare Applications ◽  
The Internet Of Things

The Internet of Things is a rapidly growing paradigm for smart cities that provides a way of communication, identification, and sensing capabilities among physically distributed devices. With the evolution of the Internet of Things (IoTs), user dependence on smart systems and services, such as smart appliances, smartphone, security, and healthcare applications, has been increased. This demands secure authentication mechanisms to preserve the users’ privacy when interacting with smart devices. This paper proposes a heterogeneous framework “ADLAuth” for passive and implicit authentication of the user using either a smartphone’s built-in sensor or wearable sensors by analyzing the physical activity patterns of the users. Multiclass machine learning algorithms are applied to users’ identity verification. Analyses are performed on three different datasets of heterogeneous sensors for a diverse number of activities. A series of experiments have been performed to test the effectiveness of the proposed framework. The results demonstrate the better performance of the proposed scheme compared to existing work for user authentication.

Looking out for one another First Responder Physiological Monitoring (Preprint)

2021 ◽  
Author(s):  
Martin Anderson
Keyword(s):  
Physiological Stress ◽  
Wearable Sensors ◽  
The Internet ◽  
First Responder ◽  
Incident Command ◽  
Healthcare Workforce ◽  
Remote Care ◽  
The Internet Of Things ◽  
Physician Response ◽  
Shed Light

UNSTRUCTURED The pandemic had shed light on healthcare burnout and stress throughout the healthcare workforce even more so the First Responder. First responders experience significant physiological stress during response operations and face exposure to a myriad of hazards. Miniaturized, wearable sensors attached to or carried by respond- ers can provide incident command with information about an in- dividual’s health status and specific threats and hazards at the in- cident scene. Improved awareness of these factors helps incident command make decisions that increase the safety of responders and the population. Blended with new advancements in the internet of things and remote care, we are best to look out for one another. Rapid response services like the physician response service at Barts Health NHS trust in east London can offer a new model of working we’re we can look after one another.

Wearable Technology as a Source of Data-Generation Tool for Artificial Intelligence

2021 ◽  
pp. 17-34
Author(s):  
Gyasi Emmanuel Kwabena ◽  
Mageshbabu Ramamurthy ◽  
Akila Wijethunga ◽  
Purushotham Swarnalatha
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Wearable Sensors ◽  
Wearable Technology ◽  
Data Generation ◽  
Learning Models ◽  
The World ◽  
Smart Wearable ◽  
The Internet Of Things ◽  
Machine Learning Models

The world is fascinated to see how technology evolves each passing day. All too soon, there's an emerging technology that is trending around us, and it is no other technology than smart wearable technology. Less attention is paid to the data that our bodies are radiating and communicating to us, but with the timely arrival of wearable sensors, we now have numerous devices that can be tracking and collecting the data that our bodies are radiating. Apart from numerous benefits that we derive from the functions provided by wearable technology such as monitoring of our fitness levels, etc., one other critical importance of wearable technology is helping the advancement of artificial intelligence (AI) and machine learning (ML). Machine learning thrives on the availability of massive data and wearable technology which forms part of the internet of things (IoT) generates megabytes of data every single day. The data generated by these wearable devices are used as a dataset for the training and learning of machine learning models. Through the analysis of the outcome of these machine learning models, scientific conclusions are made.

scholarly journals Vision-Based Fall Detection with Convolutional Neural Networks

2017 ◽  
Vol 2017 ◽  
pp. 1-16 ◽  
Author(s):  
Adrián Núñez-Marcos ◽  
Gorka Azkune ◽  
Ignacio Arganda-Carreras
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Older Persons ◽  
Healthy Aging ◽  
Wearable Sensors ◽  
Fall Detection ◽  
Detection Rates ◽  
Alternative Approaches ◽  
Public Datasets ◽  
The Internet Of Things

One of the biggest challenges in modern societies is the improvement of healthy aging and the support to older persons in their daily activities. In particular, given its social and economic impact, the automatic detection of falls has attracted considerable attention in the computer vision and pattern recognition communities. Although the approaches based on wearable sensors have provided high detection rates, some of the potential users are reluctant to wear them and thus their use is not yet normalized. As a consequence, alternative approaches such as vision-based methods have emerged. We firmly believe that the irruption of the Smart Environments and the Internet of Things paradigms, together with the increasing number of cameras in our daily environment, forms an optimal context for vision-based systems. Consequently, here we propose a vision-based solution using Convolutional Neural Networks to decide if a sequence of frames contains a person falling. To model the video motion and make the system scenario independent, we use optical flow images as input to the networks followed by a novel three-step training phase. Furthermore, our method is evaluated in three public datasets achieving the state-of-the-art results in all three of them.

scholarly journals Playground Social Interaction Analysis using Bespoke Wearable Sensors for Tracking and Motion Capture

2018 ◽  
Author(s):  
Behzad Heravi ◽  
Jenny Louise Gibson ◽  
Stephen Hailes ◽  
David Skuse
Keyword(s):  
Social Behaviour ◽  
Interaction Analysis ◽  
System Development ◽  
Wearable Sensors ◽  
Movement Patterns ◽  
Observational Research ◽  
Sensor Technology ◽  
Collective Movement ◽  
Unstructured Play

Unstructured play is considered important for the social, physical and cognitive development of children. Traditional observational research examining play behaviour at playtime (recess) has been hampered by challenges in obtaining reliable data and in processing sufficient quantities of that data to permit credible inferences to be drawn. The emergence of wearable wireless sensor technology makes it possible to study individual differences in childhood social behaviour based on collective movement patterns during playtime. In this work, we introduce a new method to enable simultaneous collection of GNSS/IMU data from a group of children interacting on a playground. We present a detailed description of system development and implementation before going on to explore methods of characterising social groups based on collective movement recording and analysis. A case study was carried out for a class of 7-8 year old children in their school playground during 10 episodes of unstructured play. A further 10 play episodes were monitored in the same space following the introduction of large, loose play materials. This experimental design allowed us to study the effect of an environmental intervention on social movement patterns. Sociometric analysis was conducted for comparison and validation. This successful case study demonstrates that sensor based movement data can be used to explore children’s social behaviour during naturalistic play.

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