Wearable Internet of Cardiac Health (IoCH) Monitoring: The need of the Hour

2021 ◽  
Vol 1 (1) ◽  
Keyword(s):  
Real Time ◽  
Heart Diseases ◽  
Smart Phone ◽  
Analytical Models ◽  
Sensor Technology ◽  
Real Time Processing ◽  
Coronary Heart Diseases ◽  
Cardiac Health ◽  
Healthcare Data ◽  
Iot Devices

In the recent years, the advancements in the wearable sensor technology has made it possible to apply sensor embedded IoT devices such as smart watch, smart glass, smart phone, and smart helmet to monitor the vital cardiac health parameters. The sensor embedded IoT devices collects the healthcare data in a continuous fashion, which are least useful if not stored, processed, and analyzed in a real-time. Moreover, mearly real-time processing of the healthcare data may not serve the purpose as the underlying data might be highly unstructured and messy. Therefore, Artificial Intelligent (AI) assisted analytical models are required to analyze the healthcare data for cardiac early warning prediction. In this paper, we provide a narrative mini review on the recent advancement in wearable technology is discussed. The paper describes the growin problem of Coronary Heart Diseases (CHDs) and the wearable devices that assist in the acquisition of healthcare data.

scholarly journals A Novel Framework of Health Monitoring Systems

2021 ◽  
Vol 6 (1) ◽  
pp. 1-14
Author(s):  
Sonam Gupta ◽  
Lipika Goel ◽  
Abhay Kumar Agarwal
Keyword(s):  
Real Time ◽  
Health Monitoring ◽  
The Body ◽  
Healthcare Data ◽  
Quality Of Patient Care ◽  
Health Monitoring Systems ◽  
Iot Devices ◽  
Rate Sensor ◽  
Cloud Servers

IoT plays an important role in the healthcare domain for improving the quality of patient care. To analyze the patients' healthcare data, a real-time health-monitoring system is required. The proposed framework in this work is cable of such monitoring and sending alerts on critical circumstances. In this framework, the use of IoT devices makes it possible. This is very helpful in taking care of especially old wards and children in the absence or their caretakers. The function of alerting the caretakers and to inform hospital in critical condition makes this system one of its kind. Readings of patient pulse rates are taken from the pulse rate sensor and the body temperature is measured by MAX30205, a temperature sensor. The data is collected through sensors and sent over the cloud servers. Linear regression is used for further analysis and prediction of pulse and temperature trend lines. Corresponding health repots will be sent to the nearby hospitals and registered mobile numbers. The framework is validated with real-time patient data, and prediction is made regarding the trends.

scholarly journals Smart and secure home using IOT Simulations with Cisco Packet Tracer

2020 ◽  
pp. 88-93
Author(s):  
Ravi Ray Chaudhari ◽  
Krishna Kumar Joshi ◽  
Neelam Joshi ◽  
Manjit Kumar
Keyword(s):  
Programming Languages ◽  
Real Time ◽  
Smart Home ◽  
Electronic Devices ◽  
Smart Phone ◽  
Home Network ◽  
Gateway System ◽  
Different Types ◽  
Packet Tracer ◽  
Iot Devices

This is an era of technology; we are surrounded with the technology. Now a day’s people become smarter they want to operate each and everything’s with his smart phone/laptop/pc without going anywhere, it happens due to IOT devices. That’s why in this paper we have design a smart home with enhance security. In smart home we can operate the household things(fan, light, ac, any applications, door, windows, etc. )with the mobile. We design a IOT based smart and secure home model in latest released cisco packet tracer. Cisco packet is basically a simulator to teach the student about the network. But in newly released cisco packet tracer 7.2.1 it have more sensors, board, Programming languages, IOE devices. In this we can design, test, see the actual working of the network/model in real time. In testing the IoT home network wireless network gateway system, multiple electronic devices can be controlled and monitored via smartphone based on predefined configuration conditions. The smart and secure home is implemented using different types of IOE devices with enhanced security, house environment prospective and safety.

A Simple and Practical Method for Incorporating Augmented Reality into the Classroom and Laboratory

2018 ◽  
Author(s):  
Kyle Plunkett
Keyword(s):  
Organic Chemistry ◽  
Augmented Reality ◽  
Real Time ◽  
Real World ◽  
Smart Phone ◽  
Practical Method ◽  
Virtual Expert ◽  
Video Projection

This manuscript provides two demonstrations of how Augmented Reality (AR), which is the projection of virtual information onto a real-world object, can be applied in the classroom and in the laboratory. Using only a smart phone and the free HP Reveal app, content rich AR notecards were prepared. The physical notecards are based on Organic Chemistry I reactions and show only a reagent and substrate. Upon interacting with the HP Reveal app, an AR video projection shows the product of the reaction as well as a real-time, hand-drawn curved-arrow mechanism of how the product is formed. Thirty AR notecards based on common Organic Chemistry I reactions and mechanisms are provided in the Supporting Information and are available for widespread use. In addition, the HP Reveal app was used to create AR video projections onto laboratory instrumentation so that a virtual expert can guide the user during the equipment setup and operation.

GPU Acceleration of Algorithm to Design Directional Volumetric Display for Real-time Processing

2019 ◽  
pp. 989
Author(s):  
Daiki Matsumoto ◽  
Ryuji Hirayama ◽  
Naoto Hoshikawa ◽  
Hirotaka Nakayama ◽  
Tomoyoshi Shimobaba ◽  
...  
Keyword(s):  
Real Time ◽  
Gpu Acceleration ◽  
Real Time Processing ◽  
Time Processing ◽  
Volumetric Display

Putting up barriers

2017 ◽  
Author(s):  
David J. Lobina
Keyword(s):  
Knowledge Base ◽  
Real Time ◽  
Language Processing ◽  
Cognitive Domain ◽  
Linguistic Knowledge ◽  
Levels Of Explanation ◽  
Real Time Processing ◽  
Time Processing ◽  
Neural Underpinnings ◽  
Different Levels

The study of cognitive phenomena is best approached in an orderly manner. It must begin with an analysis of the function in intension at the heart of any cognitive domain (its knowledge base), then proceed to the manner in which such knowledge is put into use in real-time processing, concluding with a domain’s neural underpinnings, its development in ontogeny, etc. Such an approach to the study of cognition involves the adoption of different levels of explanation/description, as prescribed by David Marr and many others, each level requiring its own methodology and supplying its own data to be accounted for. The study of recursion in cognition is badly in need of a systematic and well-ordered approach, and this chapter lays out the blueprint to be followed in the book by focusing on a strict separation between how this notion applies in linguistic knowledge and how it manifests itself in language processing.

scholarly journals Intelligent IoT systems for civil infrastructure health monitoring: a research roadmap

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
E. Bertino ◽  
M. R. Jahanshahi ◽  
A. Singla ◽  
R.-T. Wu
Keyword(s):  
Data Acquisition ◽  
Real Time ◽  
Original Data ◽  
Local Analysis ◽  
Civil Infrastructure ◽  
Time Requirements ◽  
Iot Devices ◽  
Learning Reinforcement ◽  
The Times ◽  
Acquisition Processes

AbstractThis paper addresses the problem of efficient and effective data collection and analytics for applications such as civil infrastructure monitoring and emergency management. Such problem requires the development of techniques by which data acquisition devices, such as IoT devices, can: (a) perform local analysis of collected data; and (b) based on the results of such analysis, autonomously decide further data acquisition. The ability to perform local analysis is critical in order to reduce the transmission costs and latency as the results of an analysis are usually smaller in size than the original data. As an example, in case of strict real-time requirements, the analysis results can be transmitted in real-time, whereas the actual collected data can be uploaded later on. The ability to autonomously decide about further data acquisition enhances scalability and reduces the need of real-time human involvement in data acquisition processes, especially in contexts with critical real-time requirements. The paper focuses on deep neural networks and discusses techniques for supporting transfer learning and pruning, so to reduce the times for training the networks and the size of the networks for deployment at IoT devices. We also discuss approaches based on machine learning reinforcement techniques enhancing the autonomy of IoT devices.

scholarly journals Influence of Features on Accuracy of Anomaly Detection for an Energy Trading System

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4237
Author(s):  
Hoon Ko ◽  
Kwangcheol Rim ◽  
Isabel Praça
Keyword(s):  
Anomaly Detection ◽  
Real Time ◽  
Feature Detection ◽  
Trading System ◽  
Judgment Accuracy ◽  
Energy Trading ◽  
Real Time Processing ◽  
Detection Model ◽  
Time Required ◽  
The Relationship

The biggest problem with conventional anomaly signal detection using features was that it was difficult to use it in real time and it requires processing of network signals. Furthermore, analyzing network signals in real-time required vast amounts of processing for each signal, as each protocol contained various pieces of information. This paper suggests anomaly detection by analyzing the relationship among each feature to the anomaly detection model. The model analyzes the anomaly of network signals based on anomaly feature detection. The selected feature for anomaly detection does not require constant network signal updates and real-time processing of these signals. When the selected features are found in the received signal, the signal is registered as a potential anomaly signal and is then steadily monitored until it is determined as either an anomaly or normal signal. In terms of the results, it determined the anomaly with 99.7% (0.997) accuracy in f(4)(S0) and in case f(4)(REJ) received 11,233 signals with a normal or 171anomaly judgment accuracy of 98.7% (0.987).

scholarly journals Limits on expectation-based processing: Use of grammatical aspect for co-reference in L2

2020 ◽  
pp. 1-25
Author(s):  
Theres Grüter ◽  
Hannah Rohde
Keyword(s):  
Real Time ◽  
Language Processing ◽  
Judgment Task ◽  
Linguistic Knowledge ◽  
Real Time Processing ◽  
Time Processing ◽  
Grammatical Aspect ◽  
Level Information ◽  
Native Speakers Of English ◽  
Reference Processing

Abstract This study examines the use of discourse-level information to create expectations about reference in real-time processing, testing whether patterns previously observed among native speakers of English generalize to nonnative speakers. Findings from a visual-world eye-tracking experiment show that native (L1; N = 53) but not nonnative (L2; N = 52) listeners’ proactive coreference expectations are modulated by grammatical aspect in transfer-of-possession events. Results from an offline judgment task show these L2 participants did not differ from L1 speakers in their interpretation of aspect marking on transfer-of-possession predicates in English, indicating it is not lack of linguistic knowledge but utilization of this knowledge in real-time processing that distinguishes the groups. English proficiency, although varying substantially within the L2 group, did not modulate L2 listeners’ use of grammatical aspect for reference processing. These findings contribute to the broader endeavor of delineating the role of prediction in human language processing in general, and in the processing of discourse-level information among L2 users in particular.

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