scholarly journals Low-cost SARS-CoV-2 vaccine homogenization system for Pfizer-BioNTech covid-19 vials

2021 ◽  
Vol 10 (4) ◽  
pp. 340
Author(s):  
Jose Lima ◽  
Luísa Rocha ◽  
Cláudia Rocha ◽  
Paulo Costa
Keyword(s):  
Embedded System ◽  
Hospital Pharmacy ◽  
Healthcare Workers ◽  
Saline Solution ◽  
Low Cost ◽  
Order Process ◽  
Repetitive Movement

<p>The current SARS-CoV-2 pandemic has been affecting all sectors worldwide, and efforts have been targeting the enhancement of people’s health and labour conditions of collaborators belonging to healthcare institutions. The recent vaccines emerging against covid-19 are seen as a solution to address the problem that has already killed up to two million people. The preparation of the Pfizer-BioNTech covid-19 vaccine requires a specific manipulation before its administration. A correct homogenization with saline solution is needed and, therefore, a manual process with a predefined protocol should be accomplished. This action can endanger the operators’ ergonomics due to the repetitive movement of the process. This paper proposes a low-cost prototype incorporating an arduino based embedded system actuating a servomotor to perform an autonomous vials’ homogenization allowing to redirect these healthcare workers to other tasks. Moreover, a contactless start order process was implemented to avoid contact with the operator and, consequently, the contamination. The prototype was successfully tested and recognised, and is being applied during the preparation of the covid-19 vaccines at the hospital pharmacy of <em>Centro Hospitalar de Vila Nova de Gaia/Espinho</em>, <em>E.P.E.</em>, Portugal. It can be easily replicated since the source files to assemble it are provided by the authors.</p>

scholarly journals Low-Cost Embedded System Using Convolutional Neural Networks-Based Spatiotemporal Feature Map for Real-Time Human Action Recognition

2021 ◽  
Vol 11 (11) ◽  
pp. 4940
Author(s):  
Jinsoo Kim ◽  
Jeongho Cho
Keyword(s):  
Embedded System ◽  
Real Time ◽  
Action Recognition ◽  
Processing Speed ◽  
Recognition Accuracy ◽  
Low Cost ◽  
Human Action Recognition ◽  
Human Action ◽  
Video Data ◽  
Feature Maps

The field of research related to video data has difficulty in extracting not only spatial but also temporal features and human action recognition (HAR) is a representative field of research that applies convolutional neural network (CNN) to video data. The performance for action recognition has improved, but owing to the complexity of the model, some still limitations to operation in real-time persist. Therefore, a lightweight CNN-based single-stream HAR model that can operate in real-time is proposed. The proposed model extracts spatial feature maps by applying CNN to the images that develop the video and uses the frame change rate of sequential images as time information. Spatial feature maps are weighted-averaged by frame change, transformed into spatiotemporal features, and input into multilayer perceptrons, which have a relatively lower complexity than other HAR models; thus, our method has high utility in a single embedded system connected to CCTV. The results of evaluating action recognition accuracy and data processing speed through challenging action recognition benchmark UCF-101 showed higher action recognition accuracy than the HAR model using long short-term memory with a small amount of video frames and confirmed the real-time operational possibility through fast data processing speed. In addition, the performance of the proposed weighted mean-based HAR model was verified by testing it in Jetson NANO to confirm the possibility of using it in low-cost GPU-based embedded systems.

scholarly journals Recurrent Neural Network for Human Activity Recognition in Embedded Systems Using PPG and Accelerometer Data

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1715
Author(s):  
Michele Alessandrini ◽  
Giorgio Biagetti ◽  
Paolo Crippa ◽  
Laura Falaschetti ◽  
Claudio Turchetti
Keyword(s):  
Neural Network ◽  
Embedded System ◽  
Activity Recognition ◽  
Human Activity ◽  
Recurrent Neural Network ◽  
Low Cost ◽  
Low Complexity ◽  
Human Activity Recognition ◽  
Motion Artifacts ◽  
Accelerometer Data

Photoplethysmography (PPG) is a common and practical technique to detect human activity and other physiological parameters and is commonly implemented in wearable devices. However, the PPG signal is often severely corrupted by motion artifacts. The aim of this paper is to address the human activity recognition (HAR) task directly on the device, implementing a recurrent neural network (RNN) in a low cost, low power microcontroller, ensuring the required performance in terms of accuracy and low complexity. To reach this goal, (i) we first develop an RNN, which integrates PPG and tri-axial accelerometer data, where these data can be used to compensate motion artifacts in PPG in order to accurately detect human activity; (ii) then, we port the RNN to an embedded device, Cloud-JAM L4, based on an STM32 microcontroller, optimizing it to maintain an accuracy of over 95% while requiring modest computational power and memory resources. The experimental results show that such a system can be effectively implemented on a constrained-resource system, allowing the design of a fully autonomous wearable embedded system for human activity recognition and logging.

scholarly journals Novel repurposing of a Laerdal Airway trainer to simulate aerosolisation

2021 ◽  
pp. bmjstel-2020-000802
Author(s):  
Sven Peter Oman ◽  
Scott Helgeson ◽  
Philip Lowman ◽  
Pablo Moreno Franco ◽  
Jonathan Tomshine ◽  
...  
Keyword(s):  
Airway Management ◽  
Ultraviolet Light ◽  
Healthcare Workers ◽  
Saline Solution ◽  
Size Range ◽  
Ambient Air ◽  
Airborne Particles ◽  
Exposure Risk ◽  
Aerosol Model ◽  
The Usa

COVID-19 has claimed over 200 000 lives in the USA and put healthcare workers at risk. Healthcare workers have an increased exposure risk from aerosol-generating procedures such as endotracheal intubation. New barrier designs such as the acrylic box and horizontal plastic drape have emerged to reduce exposure to airborne particles. Particle generating models are needed to test aerosol generating procedure (AGP) barrier designs. To achieve this, an aerosol model that generates a visible and measurable increase in particles which SARS-CoV-2 could travel on and that can also be intubated was created. The model was created using a Laerdal Airway Management Trainer (Laerdal Medical, Stavanger, Norway) combined with a nebuliser and Ambu bag-valve resuscitator (Ambu, Columbia, Maryland, USA). Nebulised Glo Germ (Glo Germ, Moab, Utah, USA) dissolved in saline solution was moved through the tubing and out of the mannequin’s mouth with compression of the Ambu bag. This nebulisation was visualised under ultraviolet light and the quantity of particles between 0.3 and 10.0 μm was measured with a particle counter. Nebulisation was visible exiting the mouth of the mannequin. Nebulised Glo Germ was visualised under ultraviolet light moving in the ambient air. Particles in the size range of 0.3–0.5 µm increased by 20-fold and 1–10 µm increased by 10 252%. SARS-CoV-2 can travel on aerosol and droplet particles and particle generating models are needed to visualise and measure exposure areas and the path particles take during AGPs. We used existing medical and simulation supplies to create a particle simulator.

scholarly journals Design and Adoption of Low-Cost Point-of-Care Diagnostic Devices: Syrian Case

Micromachines ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 882
Author(s):  
M. Munzer Alseed ◽  
Hamzah Syed ◽  
Mehmet Cengiz Onbasli ◽  
Ali K. Yetisen ◽  
Savas Tasoglu
Keyword(s):  
Civil Wars ◽  
Healthcare Workers ◽  
New Technologies ◽  
Point Of Care ◽  
Low Cost ◽  
Healthcare Services ◽  
Efficient Solutions ◽  
Humanitarian Crises ◽  
Technology Experience ◽  
The Cost

Civil wars produce immense humanitarian crises, causing millions of individuals to seek refuge in other countries. The rate of disease prevalence has inclined among the refugees, increasing the cost of healthcare. Complex medical conditions and high numbers of patients at healthcare centers overwhelm the healthcare system and delay diagnosis and treatment. Point-of-care (PoC) testing can provide efficient solutions to high equipment cost, late diagnosis, and low accessibility of healthcare services. However, the development of PoC devices in developing countries is challenged by several barriers. Such PoC devices may not be adopted due to prejudices about new technologies and the need for special training to use some of these devices. Here, we investigated the concerns of end users regarding PoC devices by surveying healthcare workers and doctors. The tendency to adopt PoC device changes is based on demographic factors such as work sector, education, and technology experience. The most apparent concern about PoC devices was issues regarding low accuracy, according to the surveyed clinicians.

The Development of Embedded System for Energy Saving in Electro-Hydraulic System

2013 ◽  
Vol 418 ◽  
pp. 63-69
Author(s):  
Sema Patchim ◽  
Watcharin Po-Ngaen
Keyword(s):  
Embedded System ◽  
Low Power ◽  
Hydraulic System ◽  
Fuzzy Controller ◽  
Low Cost ◽  
Operational Performance ◽  
Functional Design ◽  
Power Utility ◽  
Load Sensing ◽  
Oil Flow

In last decade, energy efficiency of hydraulic actuators systems has been especially important in industrial machinery applications [1-. And an advanced electronics world most of the applications are developed by microcontroller based embedded system. Energy processor based variable oil flow of hydraulic controller was presented to improve the efficiency of the motor by maintaining with the load sensing. These PIC processor combined with fuzzy controller were help to design efficient optimal power hydraulic machine controller. A functional design of processor and in this system was completed by using load sensing signal to control oil flow. The advantage of the proposed system was optimized operational performance and low power utility. Without having the architectural concept of any motor we can control it by using this method. This is a low cost low power controller and easy to use. The experiment results verified its validity.

scholarly journals Prophylactic role of ivermectin in SARS-CoV-2 infection among healthcare workers

2021 ◽  
Author(s):  
Priyamadhaba Behera ◽  
Binod Kumar Patro ◽  
Biswa Mohan Padhy ◽  
Prasanta Raghab Mohapatra ◽  
Shakti Kumar Bal ◽  
...  
Keyword(s):  
Lower Risk ◽  
Healthcare Workers ◽  
Absolute Risk ◽  
Low Cost ◽  
Absolute Risk Reduction ◽  
Healthcare Services ◽  
Adjusted Relative Risk ◽  
Kaplan Meier

Abstract Background Healthcare workers (HCWs) are vulnerable to getting infected withSARS-CoV-2. Preventing HCWs from getting infected is a priority to maintain healthcare services. The therapeutic and preventive role of ivermectin in COVID-19 is being investigated. Based on promising results of in vitro studies of oral ivermectin, this study was conducted with the aim to demonstrate the prophylactic role of oral ivermectin in preventing SARS-CoV-2 infectionamong HCWs at All India Institute of Medical Sciences (AIIMS), Bhubaneswar.Methods A prospective cohort study was conducted at AIIMS Bhubaneswar, which provides both COVID and Non-COVID care since March 2020. All employees and students of the institute who provided written informed consent participated in the study.Uptake of two-doses of oral ivermectin (300 μg/kg at a gap of 72 hours) was considered as exposure. The primary outcome of the study was COVID-19 infection in the following month of ivermectin consumption diagnosed by RTPCR as per Government of India testing criteria guidelines.The log-binomial model was used to estimate adjusted relative risk, and the Kaplan-Meier failure plot was used to estimate the probability of COVID-19 infection with follow-up time.Results Of 3892 employees, 3532 (90.8%) participated in the study. The ivermectin uptake was 62.5% and 5.3% for two-doses and single-dose, respectively. Participants who took ivermectin prophylaxis had a lower risk of getting symptoms suggestive of SARS-CoV-2 infection(6% vs 15%). HCWs who had taken two-doses of oral ivermectin have a significantly lower risk of contracting COVID-19 disease during the following month (ARR 0.17; 95% CI, 0.12-0.23). Females had a lower risk of contracting COVID-19 than males (ARR 0.70 95% CI, 0.52-0.93). The absolute risk reduction of SARS-CoV-2 infection was 9.7%. Only 1.8% of the participants reported adverse events, which were mild and self-limiting.Conclusion and relevance Two-doses of oral ivermectin (300 μg/kg given 72 hours apart) as chemoprophylaxis among HCWs reduces the risk of COVID-19 infection by 83% in the following month. Safe, effective, and low-cost chemoprophylaxis have relevance in the containment of pandemic alongside vaccine.

Computer Intelligence in Healthcare

2013 ◽  
pp. 716-715 ◽  
Author(s):  
Pramit Ghosh ◽  
Debotosh Bhattacharjee ◽  
Mita Nasipuri ◽  
Dipak Kumar Basu
Keyword(s):  
Embedded System ◽  
Room Temperature ◽  
Cooling System ◽  
Low Cost ◽  
Environmental Parameters ◽  
Training Data ◽  
Data Sets ◽  
Air Conditioner ◽  
Time Data ◽  
Full Speed

Low cost solutions for the development of intelligent bio-medical devices that not only assist people to live in a better way but also assist physicians for better diagnosis are presented in this chapter. Two such devices are discussed here, which are helpful for prevention and diagnosis of diseases. Statistical analysis reveals that cold and fever are the main culprits for the loss of man-hours throughout the world, and early pathological investigation can reduce the vulnerability of disease and the sick period. To reduce this cold and fever problem a household cooling system controller, which is adaptive and intelligent in nature, is designed. It is able to control the speed of a household cooling fan or an air conditioner based on the real time data, namely room temperature, humidity, and time for which system is active, which are collected from environment. To control the speed in an adaptive and intelligent manner, an associative memory neural network (Kramer) has been used. This embedded system is able to learn from training set; i.e., the user can teach the system about his/her feelings through training data sets. When the system starts up, it allows the fan to run freely at full speed, and after certain interval, it takes the environmental parameters like room temperature, humidity, and time as inputs. After that, the system takes the decision and controls the speed of the fan.

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