scholarly journals Road Accidents Detection, Data Collection and Data Analysis Using V2X Communication and Edge/Cloud Computing

Electronics ◽  
2019 ◽  
Vol 8 (8) ◽  
pp. 896 ◽  
Author(s):  
Kishwer Abdul Khaliq ◽  
Omer Chughtai ◽  
Abdullah Shahwani ◽  
Amir Qayyum ◽  
Jürgen Pannek
Keyword(s):  
Cloud Computing ◽  
Real Time ◽  
Sensor Data ◽  
Practical Implementation ◽  
Delayed Response ◽  
Care Providers ◽  
Road Accidents ◽  
Death Toll ◽  
Alert Message ◽  
Accident Detection

With the improvement in transportation infrastructure and in-vehicle technology in addition to a meteoric increase in the total number of commercial and non-commercial vehicles on the road, traffic accidents may occur, which usually cause a high death toll. More than half of these deaths occur due to a delayed response by medical care providers and rescue authorities. The chances of survival of an accident victim could increase drastically if immediate medical assistance is provided at an accident location. This work proposes a low-cost accident detection and notification system, which utilizes a multi-tier IoT-based vehicular environment; principally, it uses V2X Communication and Edge/Cloud computing. In this work, vehicles are equipped with an On-Board Unit (OBU) in addition to mechanical sensors (accelerometer, gyroscope) for reliable accident detection along with a Global Positioning System (GPS) module for identification of accident location. In addition to this, a camera module is implanted on the vehicle to capture the moment when an accident takes place. In order to facilitate inter-vehicle communication (IVC), OBU in each vehicle incorporates a wireless networking interface. Once an accident occurs, a vehicle detects it and generates an alert message. It then sends the message along with the accident location to an intermediate device, placed at the edge of the vehicular network, and therefore called an edge device. Upon receiving the notification, this edge device finds the nearest hospital and makes a request for an ambulance to be dispatched immediately. It also performs some preprocessing of data and effectively acts as a bridge between the sensors installed inside the vehicle and the distant server deployed in the cloud. A significant issue that the traffic authorities are currently facing is the real-time visualization of data obtained through such environments. Wireless interfaces are usually capable of forwarding real-time sensor data; however, this feature is not yet commercially available in the OBU of the vehicle; therefore, practical implementation is carried out using the Internet of things (IoT) in order to create a network among the vehicles, the edge node, and the central server. By performing analysis on the adequate acquired data of road accidents, the constructive plans of action can be devised that may limit the death toll. In order to assist the relevant authorities in performing wholesome analysis of refined and reliable data, a dynamic front-end visualization is proposed, which is hosted in the cloud. The generated charts and graphs help the personnel at relevant organizations to make appropriate decisions based on the conclusive analysis of processed and stored data.

scholarly journals GPS & GSM Based Accident Detection And Auto Intimation

2018 ◽  
Vol 11 (1) ◽  
pp. 356 ◽  
Author(s):  
S.George Fernandez ◽  
R Palanisamy ◽  
K. Vijayakumar
Keyword(s):  
Road Accidents ◽  
Police Station ◽  
Vehicle Accidents ◽  
The Road ◽  
Vehicle Accident ◽  
Rescue Team ◽  
The People ◽  
Alert Message ◽  
Accident Detection ◽  
Short Time

<p>The high demand of automobiles has also increased the traffic hazards and the road accidents. Life of the people is under high risk. This is because of the lack of best emergency facilities available in our country. An automatic alarm device for vehicle accidents is introduced in this paper. This design is a system which can detect accidents in significantly less time and sends the basic information to first aid centre within a few seconds covering geographical coordinates, the time and angle in which a vehicle accident had occurred. This alert message is sent to the rescue team in a short time, which will help in saving the valuable lives. A Switch is also provided in order to terminate the sending of a message in rare case where there is no casualty, this can save the precious time of the medical rescue team. When the accident occurs the alert message is sent automatically to the rescue team and to the police station. The message is sent through the GSM module and the location of the accident is detected with the help of the GPS module. </p>

scholarly journals Cost effective Parking System Using Computer Vision

2020 ◽  
pp. 241-246
Author(s):  
Kaushal Shah ◽  
Shivang Rajbhoi ◽  
Nikhil Prasad ◽  
Charmi Patel ◽  
Roushan Raj
Keyword(s):  
Real Time ◽  
Cost Effective ◽  
Video Stream ◽  
Sensor Data ◽  
Practical Implementation ◽  
Wireless System ◽  
Central System ◽  
Static Images ◽  
High Processing ◽  
Changing Light

This paper presents an approach for detecting real-time parking slots which includes vision-based techniques. Traditional sensor-based systems are not cost effective as 'n' number of sensors are required for 'n' parking slots. Transmitting sensor data to central system is done by hardwiring or installing dedicated wireless system which is again costly. Our technique will overcome this problem by using camera instead of number of sensors which is expensive. For detection we are using a Convolutional Neural Networks (CNN) classifier which is custom trained. It is more robust and effective in changing light conditions and weather. The following system do not require high processing as detections are done on static images not on video stream. We have also demonstrated real-time parking scenario by constructing a small prototype which shows practical implementation of our system.

scholarly journals A new Internet of Things architecture for real-time prediction of various diseases using machine learning on big data environment

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Abderrahmane Ed-daoudy ◽  
Khalil Maalmi
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Health Status ◽  
Real Time ◽  
Tracking System ◽  
Health Data ◽  
Streaming Data ◽  
Data Generation ◽  
Care Providers ◽  
Alert Message

AbstractA number of technologies enabled by Internet of Thing (IoT) have been used for the prevention of various chronic diseases, continuous and real-time tracking system is a particularly important one. Wearable medical devices with sensor, health cloud and mobile applications have continuously generating a huge amount of data which is often called as streaming big data. Due to the higher speed of the data generation, it is difficult to collect, process and analyze such massive data in real-time in order to perform real-time actions in case of emergencies and extracting hidden value. using traditional methods which are limited and time-consuming. Therefore, there is a significant need to real-time big data stream processing to ensure an effective and scalable solution. In order to overcome this issue, this work proposes a new architecture for real-time health status prediction and analytics system using big data technologies. The system focus on applying distributed machine learning model on streaming health data events ingested to Spark streaming through Kafka topics. Firstly, we transform the standard decision tree (DT) (C4.5) algorithm into a parallel, distributed, scalable and fast DT using Spark instead of Hadoop MapReduce which becomes limited for real-time computing. Secondly, this model is applied to streaming data coming from distributed sources of various diseases to predict health status. Based on several input attributes, the system predicts health status, send an alert message to care providers and store the details in a distributed database to perform health data analytics and stream reporting. We measure the performance of Spark DT against traditional machine learning tools including Weka. Finally, performance evaluation parameters such as throughput and execution time are calculated to show the effectiveness of the proposed architecture. The experimental results show that the proposed system is able to effectively process and predict real-time and massive amount of medical data enabled by IoT from distributed and various diseases.

45. Education at distance: Broadcasting ECG rounds to Southeastern Ontario (BESO Project). An innovative approach for teaching electrocardiography

2007 ◽  
Vol 30 (4) ◽  
pp. 51 ◽  
Author(s):  
A. Baranchuk ◽  
G. Dagnone ◽  
P. Fowler ◽  
M. N. Harrison ◽  
L. Lisnevskaia ◽  
...  
Keyword(s):  
Health Care ◽  
Real Time ◽  
Digital Signal ◽  
Teaching Methodology ◽  
Care Providers ◽  
Long Distance ◽  
Medicine Clerkship ◽  
Time Problem ◽  
The Impact ◽  
Ecg Interpretation

Electrocardiography (ECG) interpretation is an essential skill for physicians as well as for many other health care professionals. Continuing education is necessary to maintain these skills. The process of teaching and learning ECG interpretation is complex and involves both deductive mechanisms and recognition of patterns for different clinical situations (“pattern recognition”). The successful methodologies of interactive sessions and real time problem based learning have never been evaluated with a long distance education model. To evaluate the efficacy of broadcasting ECG rounds to different hospitals in the Southeastern Ontario region; to perform qualitative research to determine the impact of this methodology in developing and maintaining skills in ECG interpretation. ECG rounds are held weekly at Kingston General Hospital and will be transmitted live to Napanee, Belleville, Oshawa, Peterborough and Brockville. The teaching methodology is based on real ECG cases. The audience is invited to analyze the ECG case and the coordinator will introduce comments to guide the case through the proper algorithm. Final interpretation will be achieved emphasizing the deductive process and the relevance of each case. An evaluation will be filled out by each participant at the end of each session. Videoconferencing works through a vast array of internet LANs, WANs, ISDN phone lines, routers, switches, firewalls and Codecs (Coder/Decoder) and bridges. A videoconference Codec takes the analog audio and video signal codes and compresses it into a digital signal and transmits that digital signal to another Codec where the signal is decompressed and retranslated back into analog video and audio. This compression and decompression allows large amounts of data to be transferred across a network at close to real time (384 kbps with 30 frames of video per second). Videoconferencing communication works on voice activation so whichever site is speaking has the floor and is seen by all the participating sites. A continuous presence mode allows each site to have the same visual and audio involvement as the host site. A bridged multipoint can connect between 8 and 12 sites simultaneously. This innovative methodology for teaching ECG will facilitate access to developing and maintaining skills in ECG interpretation for a large number of health care providers. Bertsch TF, Callas PW, Rubin A. Effectiveness of lectures attended via interactive video conferencing versus in-person in preparing third-year internal medicine clerkship students for clinical practice examinations. Teach Learn Med 2007; 19(1):4-8. Yellowlees PM, Hogarth M, Hilty DM. The importance of distributed broadband networks to academic biomedical research and education programs. Acad Psychaitry 2006;30:451-455

Delivering Interventions Via Telehealth: Functional Communication Training with a Child with Autism as a Case Example

2020 ◽  
pp. 154079692098045
Author(s):  
Matthew J. O’Brien ◽  
Kelly M. Schieltz ◽  
Wendy K. Berg ◽  
Jennifer J. McComas ◽  
David P. Wacker
Keyword(s):  
Real Time ◽  
Adaptive Behavior ◽  
Functional Communication Training ◽  
Communication Training ◽  
Functional Communication ◽  
Care Providers ◽  
Widespread Application ◽  
Behavior Analysts

In this article, we provide a case example of how telehealth can be used by care providers in their homes to access empirically validated procedures such as functional communication training. As shown in the case example, complex assessment and intervention procedures were implemented successfully by care providers in their homes while receiving real-time coaching by behavior analysts who were located in a hospital in a different city. This case example is representative of the results we obtained thus far; substantial improvements in challenging and adaptive behavior occurred. Given these results obtained to date with telehealth, in terms of both outcomes of interventions and rated acceptability of the procedures by care providers, further and more widespread application of telehealth is warranted.

scholarly journals Real-Time Identification of Patients Included in the CMS Bundled Payment Care Improvement (BPCI) Program

2020 ◽  
Vol 41 (S1) ◽  
pp. s367-s368
Author(s):  
Michael Korvink ◽  
John Martin ◽  
Michael Long
Keyword(s):  
Performance Evaluation ◽  
Real Time ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Care Providers ◽  
Meta Model ◽  
Bundled Payment ◽  
Clinical Cohort ◽  
Real Time Identification ◽  
Care Improvement

Background: The Bundled Payment Care Improvement Program is a CMS initiative designed to encourage greater collaboration across settings of care, especially as it relates to an initial set of targeted clinical episodes, which include sepsis and pneumonia. As with many CMS incentive programs, performance evaluation is retrospective in nature, resulting in after-the-fact changes in operational processes to improve both efficiency and quality. Although retrospective performance evaluation is informative, care providers would ideally identify a patient’s potential clinical cohort during the index stay and implement care management procedures as necessary to prevent or reduce the severity of the condition. The primary challenges for real-time identification of a patient’s clinical cohort are CMS-targeted cohorts are based on either MS-DRG (grouping of ICD-10 codes) or HCPCS coding—coding that occurs after discharge by clinical abstractors. Additionally, many informative data elements in the EHR lack standardization and no simple and reliable heuristic rules can be employed to meaningfully identify those cohorts without human review. Objective: To share the results of an ensemble statistical model to predict patient risks of sepsis and pneumonia during their hospital (ie, index) stay. Methods: The predictive model uses a combination of Bernoulli Naïve Bayes natural language processing (NLP) classifiers, to reduce text dimensionality into a single probability value, and an eXtreme Gradient Boosting (XGBoost) algorithm as a meta-model to collectively evaluate both standardized clinical elements alongside the NLP-based text probabilities. Results: Bernoulli Naïve Bayes classifiers have proven to perform well on short text strings and allow for highly explanatory unstructured or semistructured text fields (eg, reason for visit, culture results), to be used in a both comparative and generalizable way within the larger XGBoost model. Conclusions: The choice of XGBoost as the meta-model has the benefits of mitigating concerns of nonlinearity among clinical features, reducing potential of overfitting, while allowing missing values to exist within the data. Both the Bayesian classifier and meta-model were trained using a patient-level integrated dataset extracted from both a patient-billing and EHR data warehouse maintained by Premier. The data set, joined by patient admission-date, medical record number, date of birth, and hospital entity code, allows the presence of both the coded clinical cohort (derived from the MS-DRG) and the explanatory features in the EHR to exist within a single patient encounter record. The resulting model produced F1 performance scores of .65 for the sepsis population and .61 for the pneumonia population.Funding: NoneDisclosures: None

scholarly journals A Deep Learning Approach to Downscale Geostationary Satellite Imagery for Decision Support in High Impact Wildfires

Forests ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 294
Author(s):  
Nicholas F. McCarthy ◽  
Ali Tohidi ◽  
Yawar Aziz ◽  
Matt Dennie ◽  
Mario Miguel Valero ◽  
...  
Keyword(s):  
Deep Learning ◽  
Decision Support ◽  
Real Time ◽  
Forest Fires ◽  
Wildland Fire ◽  
Fire Spread ◽  
Low Earth Orbit ◽  
Practical Implementation ◽  
Spatiotemporal Resolution ◽  
Active Fire

Scarcity in wildland fire progression data as well as considerable uncertainties in forecasts demand improved methods to monitor fire spread in real time. However, there exists at present no scalable solution to acquire consistent information about active forest fires that is both spatially and temporally explicit. To overcome this limitation, we propose a statistical downscaling scheme based on deep learning that leverages multi-source Remote Sensing (RS) data. Our system relies on a U-Net Convolutional Neural Network (CNN) to downscale Geostationary (GEO) satellite multispectral imagery and continuously monitor active fire progression with a spatial resolution similar to Low Earth Orbit (LEO) sensors. In order to achieve this, the model trains on LEO RS products, land use information, vegetation properties, and terrain data. The practical implementation has been optimized to use cloud compute clusters, software containers and multi-step parallel pipelines in order to facilitate real time operational deployment. The performance of the model was validated in five wildfires selected from among the most destructive that occurred in California in 2017 and 2018. These results demonstrate the effectiveness of the proposed methodology in monitoring fire progression with high spatiotemporal resolution, which can be instrumental for decision support during the first hours of wildfires that may quickly become large and dangerous. Additionally, the proposed methodology can be leveraged to collect detailed quantitative data about real-scale wildfire behaviour, thus supporting the development and validation of fire spread models.

scholarly journals Software Architecture of a Fog Computing Node for Industrial Internet of Things

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3715
Author(s):  
Ioan Ungurean ◽  
Nicoleta Cristina Gaitan
Keyword(s):  
Internet Of Things ◽  
Real Time ◽  
Development Process ◽  
Fog Computing ◽  
Low Latency ◽  
Practical Implementation ◽  
Industrial Internet Of Things ◽  
Industrial Internet ◽  
Starting Point ◽  
Hard Real Time

In the design and development process of fog computing solutions for the Industrial Internet of Things (IIoT), we need to take into consideration the characteristics of the industrial environment that must be met. These include low latency, predictability, response time, and operating with hard real-time compiling. A starting point may be the reference fog architecture released by the OpenFog Consortium (now part of the Industrial Internet Consortium), but it has a high abstraction level and does not define how to integrate the fieldbuses and devices into the fog system. Therefore, the biggest challenges in the design and implementation of fog solutions for IIoT is the diversity of fieldbuses and devices used in the industrial field and ensuring compliance with all constraints in terms of real-time compiling, low latency, and predictability. Thus, this paper proposes a solution for a fog node that addresses these issues and integrates industrial fieldbuses. For practical implementation, there are specialized systems on chips (SoCs) that provides support for real-time communication with the fieldbuses through specialized coprocessors and peripherals. In this paper, we describe the implementation of the fog node on a system based on Xilinx Zynq UltraScale+ MPSoC ZU3EG A484 SoC.

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