scholarly journals A Deep Learning-Based Camera Approach for Vital Sign Monitoring Using Thermography Images for ICU Patients

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1495 ◽  
Author(s):  
Simon Lyra ◽  
Leon Mayer ◽  
Liyang Ou ◽  
David Chen ◽  
Paddy Timms ◽  
...  
Keyword(s):  
Deep Learning ◽  
Respiratory Rate ◽  
Skin Temperature ◽  
Real Time ◽  
Vital Sign ◽  
Low Cost ◽  
Vital Signs ◽  
Contactless Method ◽  
Detector Performance ◽  
Computational Performance

Infrared thermography for camera-based skin temperature measurement is increasingly used in medical practice, e.g., to detect fevers and infections, such as recently in the COVID-19 pandemic. This contactless method is a promising technology to continuously monitor the vital signs of patients in clinical environments. In this study, we investigated both skin temperature trend measurement and the extraction of respiration-related chest movements to determine the respiratory rate using low-cost hardware in combination with advanced algorithms. In addition, the frequency of medical examinations or visits to the patients was extracted. We implemented a deep learning-based algorithm for real-time vital sign extraction from thermography images. A clinical trial was conducted to record data from patients on an intensive care unit. The YOLOv4-Tiny object detector was applied to extract image regions containing vital signs (head and chest). The infrared frames were manually labeled for evaluation. Validation was performed on a hold-out test dataset of 6 patients and revealed good detector performance (0.75 intersection over union, 0.94 mean average precision). An optical flow algorithm was used to extract the respiratory rate from the chest region. The results show a mean absolute error of 2.69 bpm. We observed a computational performance of 47 fps on an NVIDIA Jetson Xavier NX module for YOLOv4-Tiny, which proves real-time capability on an embedded GPU system. In conclusion, the proposed method can perform real-time vital sign extraction on a low-cost system-on-module and may thus be a useful method for future contactless vital sign measurements.

scholarly journals Application of Deep Learning in Integrated Pest Management: A Real-Time System for Detection and Diagnosis of Oilseed Rape Pests

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Yong He ◽  
Hong Zeng ◽  
Yangyang Fan ◽  
Shuaisheng Ji ◽  
Jianjian Wu
Keyword(s):  
Deep Learning ◽  
Integrated Pest Management ◽  
Pest Management ◽  
Real Time ◽  
Oilseed Rape ◽  
Data Augmentation ◽  
Low Cost ◽  
Response Speed ◽  
Original Model ◽  
Real Time System

In this paper, we proposed an approach to detect oilseed rape pests based on deep learning, which improves the mean average precision (mAP) to 77.14%; the result increased by 9.7% with the original model. We adopt this model to mobile platform to let every farmer able to use this program, which will diagnose pests in real time and provide suggestions on pest controlling. We designed an oilseed rape pest imaging database with 12 typical oilseed rape pests and compared the performance of five models, SSD w/Inception is chosen as the optimal model. Moreover, for the purpose of the high mAP, we have used data augmentation (DA) and added a dropout layer. The experiments are performed on the Android application we developed, and the result shows that our approach surpasses the original model obviously and is helpful for integrated pest management. This application has improved environmental adaptability, response speed, and accuracy by contrast with the past works and has the advantage of low cost and simple operation, which are suitable for the pest monitoring mission of drones and Internet of Things (IoT).

scholarly journals Multi-Parameter Vital Sign Telemedicine System Using Web Socket for COVID-19 Pandemics

Healthcare ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 285
Author(s):  
Chuchart Pintavirooj ◽  
Tanapon Keatsamarn ◽  
Treesukon Treebupachatsakul
Keyword(s):  
Real Time ◽  
Vital Sign ◽  
Vital Signs ◽  
Mobile Network ◽  
Cloud Platform ◽  
Telemedicine System ◽  
Web Based ◽  
The Real ◽  
Real Time Application ◽  
Commercial Platform

Telemedicine has become an increasingly important part of the modern healthcare infrastructure, especially in the present situation with the COVID-19 pandemics. Many cloud platforms have been used intensively for Telemedicine. The most popular ones include PubNub, Amazon Web Service, Google Cloud Platform and Microsoft Azure. One of the crucial challenges of telemedicine is the real-time application monitoring for the vital sign. The commercial platform is, by far, not suitable for real-time applications. The alternative is to design a web-based application exploiting Web Socket. This research paper concerns the real-time six-parameter vital-sign monitoring using a web-based application. The six vital-sign parameters are electrocardiogram, temperature, plethysmogram, percent saturation oxygen, blood pressure and heart rate. The six vital-sign parameters were encoded in a web server site and sent to a client site upon logging on. The encoded parameters were then decoded into six vital sign signals. Our proposed multi-parameter vital-sign telemedicine system using Web Socket has successfully remotely monitored the six-parameter vital signs on 4G mobile network with a latency of less than 5 milliseconds.

scholarly journals Non-intrusive vital sign monitoring using an intelligent pillow based on a piezoelectric ceramic sensor

2020 ◽  
Vol 15 ◽  
pp. 155892502097726
Author(s):  
Wei Wang ◽  
Zhiqiang Pang ◽  
Ling Peng ◽  
Fei Hu
Keyword(s):  
Heart Rate ◽  
Real Time ◽  
Piezoelectric Ceramic ◽  
Low Cost ◽  
Body Movement ◽  
Vital Signs ◽  
High Heart Rate ◽  
Detection Accuracy ◽  
Processing Unit ◽  
Real Time Monitoring

Performing real-time monitoring for human vital signs during sleep at home is of vital importance to achieve timely detection and rescue. However, the existing smart equipment for monitoring human vital signs suffers the drawbacks of high complexity, high cost, and intrusiveness, or low accuracy. Thus, it is of great need to develop a simplified, nonintrusive, comfortable and low cost real-time monitoring system during sleep. In this study, a novel intelligent pillow was developed based on a low-cost piezoelectric ceramic sensor. It was manufactured by locating a smart system (consisting of a sensing unit i.e. a piezoelectric ceramic sensor, a data processing unit and a GPRS communication module) in the cavity of the pillow made of shape memory foam. The sampling frequency of the intelligent pillow was set at 1000 Hz to capture the signals more accurately, and vital signs including heart rate, respiratory rate and body movement were derived through series of well established algorithms, which were sent to the user’s app. Validation experimental results demonstrate that high heart-rate detection accuracy (i.e. 99.18%) was achieved in using the intelligent pillow. Besides, human tests were conducted by detecting vital signs of six elder participants at their home, and results showed that the detected vital signs may well predicate their health conditions. In addition, no contact discomfort was reported by the participants. With further studies in terms of validity of the intelligent pillow and large-scale human trials, the proposed intelligent pillow was expected to play an important role in daily sleep monitoring.

scholarly journals SOLUÇÃO DE BAIXO CUSTO PARA O MONITORAMENTO DE SINAIS VITAIS, EM TEMPO REAL, FAZENDO USO DE SENSORES E ARDUINO

2020 ◽  
Vol 12 (2) ◽  
pp. 102-118
Author(s):  
Alexandre dos Santos Gonsalves ◽  
Robson Augusto Siscoutto
Keyword(s):  
Real Time ◽  
Health Monitoring ◽  
Low Cost ◽  
Vital Signs ◽  
Chronic Illnesses ◽  
Wireless Sensor ◽  
Health Monitoring System ◽  
Physical Exercises ◽  
Time Observation ◽  
Real Time Observation

The health monitoring system has become indispensable in the treatment of patients, especially for those who have chronic illnesses and need real-time observation from doctors and specialists. This article presents a low-cost wireless solution for monitoring, in real time, vital signs such as cardiac beats, breathing and blood pressure, collecting and sending data to a remote computer. During development, a wireless sensor box was created, using Arduino Nano and bluetooh sensors, where this box is attached to the patient's body, respecting the patient's flexibility and mobility during physical exercises. During the monitoring, the captured data is transmitted via the bluetooh network. The box uses a battery for its food. After the evaluation, the solution obtained a performance and correctness of the data close to 100%, being considered fit for use. Several experiments were carried out to analyze, quantify and qualify the solution, being discussed and presented in this paper.

Advance Monitoring of Blood Pressure and Respiratory Rate Using De-Noising Auto Encoder

2021 ◽  
Author(s):  
Seung-Ho Park ◽  
Kyoung-Su Park
Keyword(s):  
Blood Pressure ◽  
Respiratory Rate ◽  
Vital Sign ◽  
Superficial Temporal Artery ◽  
Vital Signs ◽  
Pulse Signal ◽  
Temporal Artery ◽  
Sinus Arrhythmia ◽  
Vital Signs Monitoring ◽  
Vital Sign Measurement

Abstract As the importance of continuous vital signs monitoring increases, the need for wearable devices to measure vital sign is increasing. In this study, the device is designed to measure blood pressure (BP), respiratory rate (RR), and heartrate (HR) with one sensor. The device is in earphone format and is manufactured as wireless type using Arduino-based bluetooth module. The device measures pulse signal in the Superficial temporal artery using Photoplethysmograghy (PPG) sensor. The device uses the Auto Encoder to remove noise caused by movement, etc., contained in the pulse signal. Extract the feature from the pulse signal and use them for the vital sign measurement. The device is measured using Slope transit time (STT) method for BP and Respiratory sinus arrhythmia (RSA) method for RR. Finally, the accuracy is determined by comparing the vital signs measured through the device with the reference vital signs measured simultaneously.

scholarly journals Vital Block and Vital Sign Server for ECG and Vital Sign Monitoring in a Portable u-Vital System

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1089
Author(s):  
Tae Wuk Bae ◽  
Kee Koo Kwon ◽  
Kyu Hyung Kim
Keyword(s):  
Real Time ◽  
Vital Sign ◽  
Vital Signs ◽  
Detection Algorithm ◽  
Smart Device ◽  
Blood Oxygen Saturation ◽  
Real Time Processing ◽  
Non Invasive ◽  
Wireless Protocols ◽  
Point Detection

An important function in the future healthcare system involves measuring a patient’s vital signs, transmitting the measured vital signs to a smart device or a management server, analyzing it in real-time, and informing the patient or medical staff. Internet of Medical Things (IoMT) incorporates information technology (IT) into patient monitoring device (PMD) and is developing traditional measurement devices into healthcare information systems. In the study, a portable ubiquitous-Vital (u-Vital) system is developed and consists of a Vital Block (VB), a small PMD, and Vital Sign Server (VSS), which stores and manages measured vital signs. Specifically, VBs collect a patient’s electrocardiogram (ECG), blood oxygen saturation (SpO2), non-invasive blood pressure (NiBP), body temperature (BT) in real-time, and the collected vital signs are transmitted to a VSS via wireless protocols such as WiFi and Bluetooth. Additionally, an efficient R-point detection algorithm was also proposed for real-time processing and long-term ECG analysis. Experiments demonstrated the effectiveness of measurement, transmission, and analysis of vital signs in the proposed portable u-Vital system.

scholarly journals A Portable Fuzzy Driver Drowsiness Estimation System

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4093
Author(s):  
Alimed Celecia ◽  
Karla Figueiredo ◽  
Marley Vellasco ◽  
René González
Keyword(s):  
Real Time ◽  
Traffic Accidents ◽  
Fuzzy Inference ◽  
Low Cost ◽  
Raspberry Pi ◽  
Inference System ◽  
Visual Occlusion ◽  
Computational Performance ◽  
Estimation System ◽  
Definition Of

The adequate automatic detection of driver fatigue is a very valuable approach for the prevention of traffic accidents. Devices that can determine drowsiness conditions accurately must inherently be portable, adaptable to different vehicles and drivers, and robust to conditions such as illumination changes or visual occlusion. With the advent of a new generation of computationally powerful embedded systems such as the Raspberry Pi, a new category of real-time and low-cost portable drowsiness detection systems could become standard tools. Usually, the proposed solutions using this platform are limited to the definition of thresholds for some defined drowsiness indicator or the application of computationally expensive classification models that limits their use in real-time. In this research, we propose the development of a new portable, low-cost, accurate, and robust drowsiness recognition device. The proposed device combines complementary drowsiness measures derived from a temporal window of eyes (PERCLOS, ECD) and mouth (AOT) states through a fuzzy inference system deployed in a Raspberry Pi with the capability of real-time response. The system provides three degrees of drowsiness (Low-Normal State, Medium-Drowsy State, and High-Severe Drowsiness State), and was assessed in terms of its computational performance and efficiency, resulting in a significant accuracy of 95.5% in state recognition that demonstrates the feasibility of the approach.

scholarly journals Solar-Powered Deep Learning-Based Recognition System of Daily Used Objects and Human Faces for Assistance of the Visually Impaired

Energies ◽  
2020 ◽  
Vol 13 (22) ◽  
pp. 6104
Author(s):  
Bernardo Calabrese ◽  
Ramiro Velázquez ◽  
Carolina Del-Valle-Soto ◽  
Roberto de Fazio ◽  
Nicola Ivan Giannoccaro ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Visually Impaired ◽  
High Speed ◽  
Low Cost ◽  
Recognition Rate ◽  
Object Identification ◽  
Time Interval ◽  
Solar Powered ◽  
Human Faces

This paper introduces a novel low-cost solar-powered wearable assistive technology (AT) device, whose aim is to provide continuous, real-time object recognition to ease the finding of the objects for visually impaired (VI) people in daily life. The system consists of three major components: a miniature low-cost camera, a system on module (SoM) computing unit, and an ultrasonic sensor. The first is worn on the user’s eyeglasses and acquires real-time video of the nearby space. The second is worn as a belt and runs deep learning-based methods and spatial algorithms which process the video coming from the camera performing objects’ detection and recognition. The third assists on positioning the objects found in the surrounding space. The developed device provides audible descriptive sentences as feedback to the user involving the objects recognized and their position referenced to the user gaze. After a proper power consumption analysis, a wearable solar harvesting system, integrated with the developed AT device, has been designed and tested to extend the energy autonomy in the different operating modes and scenarios. Experimental results obtained with the developed low-cost AT device have demonstrated an accurate and reliable real-time object identification with an 86% correct recognition rate and 215 ms average time interval (in case of high-speed SoM operating mode) for the image processing. The proposed system is capable of recognizing the 91 objects offered by the Microsoft Common Objects in Context (COCO) dataset plus several custom objects and human faces. In addition, a simple and scalable methodology for using image datasets and training of Convolutional Neural Networks (CNNs) is introduced to add objects to the system and increase its repertory. It is also demonstrated that comprehensive trainings involving 100 images per targeted object achieve 89% recognition rates, while fast trainings with only 12 images achieve acceptable recognition rates of 55%.

Pulse oximetry and the enduring neglect of respiratory rate assessment: a commentary on patient surveillance

2019 ◽  
Vol 28 (19) ◽  
pp. 1256-1259
Author(s):  
Malcolm Elliott ◽  
Jill Baird
Keyword(s):  
Pulse Oximetry ◽  
Respiratory Rate ◽  
Vital Sign ◽  
Vital Signs ◽  
Clinical Tool ◽  
Vital Signs Monitoring ◽  
Poor Understanding ◽  
Clinical Surveillance ◽  
The Common ◽  
Assessment Research

Clinical surveillance provides essential data on changes in a patient's condition. The common method for performing this surveillance is the assessment of vital signs. Despite the importance of these signs, research has found that vital signs are not rigorously assessed in clinical practice. Respiratory rate, arguably the most important vital sign, is the most neglected. Poor understanding might contribute to nurses incorrectly valuing oxygen saturation more than respiratory rate. Nurses need to understand the importance of respiratory rate assessment as a vital sign and the benefits and limitations of pulse oximetry as a clinical tool. By better understanding pulse oximetry and respiratory rate assessment, nurses might be more inclined to conduct rigorous vital signs' assessment. Research is needed to understand why many nurses do not appreciate the importance of vital signs' monitoring.

scholarly journals Vital-SCOPE: Design and Evaluation of a Smart Vital Sign Monitor for Simultaneous Measurement of Pulse Rate, Respiratory Rate, and Body Temperature for Patient Monitoring

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Guanghao Sun ◽  
Takemi Matsui ◽  
Yasuyuki Watai ◽  
Seokjin Kim ◽  
Tetsuo Kirimoto ◽  
...  
Keyword(s):  
Body Temperature ◽  
Respiratory Rate ◽  
Correlation Coefficient ◽  
Pulse Rate ◽  
Simultaneous Measurement ◽  
Vital Sign ◽  
Vital Signs ◽  
Pearson’S Correlation ◽  
Pearson's Correlation ◽  
The Mean

Consistent vital sign monitoring is critically important for early detection of clinical deterioration of patients in hospital settings. Mostly, nurses routinely measure and document the primary vital signs of all patients 2‐3 times daily to assess their condition. To reduce nurse workload and thereby improve quality of patient care, a smart vital sign monitor named “Vital‐SCOPE” for simultaneous measurement of vital signs was developed. Vital-SCOPE consists of multiple sensors, including a reflective photo sensor, thermopile, and medical radar, to be used in simultaneous pulse rate, respiratory rate, and body temperature monitoring within 10 s. It was tested in laboratory and hospital settings. Bland-Altman and Pearson’s correlation analyses were used to compare the Vital-SCOPE results to those of reference measurements. The mean difference of the respiratory rate between respiratory effort belt and Vital-SCOPE was 0.47 breaths per minute with the 95% limit of agreement ranging from −7.4 to 6.5 breaths per minute. The Pearson’s correlation coefficient was 0.63 (P<0.05). Moreover, the mean difference of the pulse rate between electrocardiogram and Vital-SCOPE was 3.4 beats per minute with the 95% limit of agreement ranging from −13 to 5.8 beats per minute; the Pearson’s correlation coefficient was 0.91 (P<0.01), indicating strong linear relationship.

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