Low Cost Sensor Based Hand Washing Solution for COVID-19 Prevention

Yadessa, Asrat Gedefa; Salau, Ayodeji Olalekan

doi:10.1109/3ict53449.2021.9581821

Cited by 7 publications

(6 citation statements)

References 14 publications

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“…This paper presents the design of a COVID-19 monitoring system using temperature, heartbeat sensors, and a microcontroller to effectively and efficiently manage the COVID-19 pandemic. The easy to use COVID-19 classroom monitoring system designed in this study comes with improved functionalities than other Internet of Things (IoT) COVID-19 detection systems [19,20,[23][24][25][26], even though it has rigorous control and monitoring functionalities. The undertaken measurements and testing have revealed that our proposed system functions admirably under all conditions.…”

Section: Discussionmentioning

confidence: 99%

“…For COVID-19 public safety, Yadessa and Salau [20] developed an IoT and hand washing system for monitoring and recording real-time occupancy. The approach is built on a decentralized traceability subsystem used to secure personal information and ensure user privacy by ensuring the availability, security, and immutability of the real-time data acquired.…”

Section: Related Workmentioning

confidence: 99%

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Innovative IoT-Based Wristlet for Early COVID-19 Detection and Monitoring Among Students

Frimpong¹,

Salau²,

Quansah³

et al. 2022

MMEP

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The current global issue of the COVID-19 pandemic has prompted the push and utilization of all available means to halt its spread. COVID-19 is a highly infectious disease, and continuously monitoring early symptoms could help avert catastrophic devastation. This paper proposes an innovative use of the Internet of Things (IoT) enabled system to efficiently and effectively detect early COVID-19 signs at a relatively low cost. This study adopted an experimental approach in designing and constructing a low-cost hardware system using a Wi-Fi enabled microcontroller, a temperature sensor, and a heart rate sensor for students. The proposed system detected and distinguished normal and abnormal temperature, regular and irregular heartbeat and constantly displayed the student's status in a mobile application. Consistent tests proved that the developed IoT-enabled system was reliable, responsive, and cost-effective. The mass production of this device will aid in the early detection of the disease, thereby mitigating the spread among students, particularly in underdeveloped countries. The paper's merit stems from the microcontroller's intelligence programming and the sensor's operation via the mobile application, which enables low-cost early identification of abnormal temperature and heartbeat irregularities.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Innovative IoT-Based Wristlet for Early COVID-19 Detection and Monitoring Among Students

Frimpong¹,

Salau²,

Quansah³

et al. 2022

MMEP

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“…AG. Yadessa et al 36 proposed an Arduino microcontroller and ultrasonic distance sensors for touch-free hand washing to prevent the spread of COVID-19 infections. T. Natnael et al 37 analyzed the use of different face masks among taxi drivers by multivariable logistic regression to prevent the spread of COVID-19 infections.…”

Section: Literature Reviewmentioning

confidence: 99%

SVM-RLF-DNN: A DNN with reliefF and SVM for automatic identification of COVID from chest X-ray and CT images

Saha,

Nandi

2024

DIGITAL HEALTH

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Aim To develop an advanced determination technology for detecting COVID-19 patterns from chest X-ray and CT-scan films with distinct applications of deep learning and machine learning methods. Methods and Materials The newly enhanced proposed hybrid classification network (SVM-RLF-DNN) comprises of three phases: feature extraction, selection and classification. The in-depth features are extracted from a series of 3×3 convolution, 2×2 max polling operations followed by a flattened and fully connected layer of the deep neural network (DNN). ReLU activation function and Adam optimizer are used in the model. The ReliefF is an improved feature selection algorithm of Relief that uses Manhattan distance instead of Euclidean distance. Based on the significance of the feature, the ReliefF assigns weight to each extracted feature received from a fully connected layer. The weight to each feature is the average of k closest hits and misses in each class for a neighbouring instance pair in multiclass problems. The ReliefF eliminates lower-weight features by setting the node value to zero. The higher weights of the features are kept to obtain the feature selection. At the last layer of the neural network, the multiclass Support Vector Machine (SVM) is used to classify the patterns of COVID-19, viral pneumonia and healthy cases. The three classes with three binary SVM classifiers use linear kernel function for each binary SVM following a one-versus-all approach. The hinge loss function and L2-norm regularization are selected for more stable results. The proposed method is assessed on publicly available chest X-ray and CT-scan image databases from Kaggle and GitHub. The performance of the proposed classification model has comparable training, validation, and test accuracy, as well as sensitivity, specificity, and confusion matrix for quantitative evaluation on five-fold cross-validation. Results Our proposed network has achieved test accuracy of 98.48% and 95.34% on 2-class X-rays and CT. More importantly, the proposed model's test accuracy, sensitivity, and specificity are 87.9%, 86.32%, and 90.25% for 3-class classification (COVID-19, Pneumonia, Normal) on chest X-rays. The proposed model provides the test accuracy, sensitivity, and specificity of 95.34%, 94.12%, and 96.15% for 2-class classification (COVID-19, Non-COVID) on chest CT. Conclusion Our proposed classification network experimental results indicate competitiveness with existing neural networks. The proposed neural network assists clinicians in determining and surveilling the disease.

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“…The features were extracted using the discrete wavelet transform (DWT) algorithm, and the extracted features were identified using SVM and achieved a detection rate of 98.2 per cent. Yadessa et al [72] presented a microcontroller (Arduino) and ultrasonic-based sensing devices to construct a touch-free hand-washing system for COVID-19 illness prevention. Natnael et al [47] did cross-sectional research to evaluate the percentage of taxi drivers who utilize facemasks for the suggested precautionary efforts to manage viral transmission.…”

Section: Related Workmentioning

confidence: 99%

Cloud-based COVID-19 disease prediction system from X-Ray images using convolutional neural network on smartphone

Lanjewar

Shaikh

Parab

2022

Multimed Tools Appl

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COVID-19 has engulfed over 200 nations through human-to-human transmission, either directly or indirectly. Reverse Transcription-polymerase Chain Reaction (RT-PCR) has been endorsed as a standard COVID-19 diagnostic procedure but has caveats such as low sensitivity, the need for a skilled workforce, and is time-consuming. Coronaviruses show significant manifestation in Chest X-Ray (CX-Ray) images and, thus, can be a viable option for an alternate COVID-19 diagnostic strategy. An automatic COVID-19 detection system can be developed to detect the disease, thus reducing strain on the healthcare system. This paper discusses a real-time Convolutional Neural Network (CNN) based system for COVID-19 illness prediction from CX-Ray images on the cloud. The implemented CNN model displays exemplary results, with training accuracy being 99.94% and validation accuracy reaching 98.81%. The confusion matrix was utilized to assess the models’ outcome and achieved 99% precision, 98% recall, 99% F1 score, 100% training area under the curve (AUC) and 98.3% validation AUC. The same CX-Ray dataset was also employed to predict the COVID-19 disease with deep Convolution Neural Networks (DCNN), such as ResNet50, VGG19, InceptonV3, and Xception. The prediction outcome demonstrated that the present CNN was more capable than the DCNN models. The efficient CNN model was deployed to the Platform as a Service (PaaS) cloud.

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Low Cost Sensor Based Hand Washing Solution for COVID-19 Prevention

Cited by 7 publications

References 14 publications

Innovative IoT-Based Wristlet for Early COVID-19 Detection and Monitoring Among Students

Innovative IoT-Based Wristlet for Early COVID-19 Detection and Monitoring Among Students

SVM-RLF-DNN: A DNN with reliefF and SVM for automatic identification of COVID from chest X-ray and CT images

Cloud-based COVID-19 disease prediction system from X-Ray images using convolutional neural network on smartphone

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