CovidAlert - A Wristwatch-Based System to Alert Users from Face Touching

Roy, Mrinmoy; Seethi, Venkata Devesh Reddy; Bharti, Pratool

doi:10.1007/978-3-030-99194-4_30

Cited by 7 publications

(10 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…and finger motion with high accuracy, sensitivity, and specificity. Additionally, there are studies that specifically investigated the ability of wearable systems to recognize hand-to-face motion [27][28][29][30][31][32][33][34]. Studies that investigated face touch detection were tabulated and compared to our proposed method in Table 3.…”

Section: Plos Onementioning

confidence: 99%

“…Studies that investigated face touch detection were tabulated and compared to our proposed method in Table 3. The feasibility of using several methods in detecting and monitoring face touch was investigated in the mentioned studies [27][28][29][30][31][32][33][34]. These studies validated their developed methods using different metrics in the context of their experimental procedure.…”

Section: Plos Onementioning

confidence: 99%

“…Alesmaeil et al [32] validated their CNN-based method on 5 participants and obtained an accuracy of 97%. In addition, an IMU-based Random Forest algorithm was developed with an accuracy of 88.4% by Roy et al [34]. They included confounding gestures such as scratching the head, picking up an item from the floor, and reaching to a shelf, as well as sitting, standing, and walking.…”

Section: Introductionmentioning

confidence: 99%

“…In this study, 10 participants were instructed to perform face touch and confounding gestures during sitting, standing, and walking trials. Most of the methods available in the literature [27][28][29][30][31][32][33][34] showed high accuracy in detecting face touch during their designed experimental procedure.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Face touch monitoring using an instrumented wristband using dynamic time warping and k-nearest neighbours

Fathian

Phan

et al. 2023

PLoS ONE

View full text Add to dashboard Cite

One of the main factors in controlling infectious diseases such as COVID-19 is to prevent touching preoral and prenasal regions. Face touching is a habitual behaviour that occurs frequently. Studies showed that people touch their faces 23 times per hour on average. A contaminated hand could transmit the infection to the body by a facial touch. Since controlling this spontaneous habit is not easy, this study aimed to develop and validate a technology to detect and monitor face touch using dynamic time warping (DTW) and KNN (k-nearest neighbours) based on a wrist-mounted inertial measurement unit (IMU) in a controlled environment and natural environment trials. For this purpose, eleven volunteers were recruited and their hand motions were recorded in controlled and natural environment trials using a wrist-mounted IMU. Then the sensitivity, precision, and accuracy of our developed technology in detecting the face touch were evaluated. It was observed that the sensitivity, precision, and accuracy of the DTW-KNN classifier were 91%, 97%, and 85% in controlled environment trials and 79%, 92%, and 79% in natural environment trials (daily life). In conclusion, a wrist-mounted IMU, widely available in smartwatches, could detect the face touch with high sensitivity, precision, and accuracy and can be used as an ambulatory system to detect and monitor face touching as a high-risk habit in daily life.

show abstract

Section: Plos Onementioning

confidence: 99%

Section: Plos Onementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Face touch monitoring using an instrumented wristband using dynamic time warping and k-nearest neighbours

Fathian

Phan

et al. 2023

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…The increasing popularity of machine learning and its nature to extract patterns from data are directing researchers to incorporate several machine learning algorithms into health informatics. Especially during the Covid-19 pandemic era, different applications like restraining people from covid-19 spread [9], SARS-CoV-2 screening and treatment [10], lock-down control in case of high dimensional input [11] came into play, which made machine learning and healthcare systems inseparable. Overall, adapting, integrating, and developing deep learning-based applications on patients' information, medical reports, and audio-video feedback make the diagnosis process faster.…”

Section: Introductionmentioning

confidence: 99%

Hair and Scalp Disease Detection using Machine Learning and Image Processing

Roy

Protity

2023

COMPUTE

Self Cite

View full text Add to dashboard Cite

Almost 80 million Americans suffer from hair loss due to aging, stress, medication, or genetic makeup. Hair and scalp-related diseases often go unnoticed in the beginning. Sometimes, a patient cannot differentiate between hair loss and regular hair fall. Diagnosing hair-related diseases is time-consuming as it requires professional dermatologists to perform visual and medical tests. Because of that, the overall diagnosis gets delayed, which worsens the severity of the illness. Due to the image-processing ability, neural network-based applications are used in various sectors, especially healthcare and health informatics, to predict deadly diseases like cancers and tumors. These applications assist clinicians and patients and provide an initial insight into early-stage symptoms. In this study, we used a deep learning approach that successfully predicts three main types of hair loss and scalp-related diseases: alopecia, psoriasis, and folliculitis. However, limited study in this area, unavailability of a proper dataset, and degree of variety among the images scattered over the internet made the task challenging. 150 images were obtained from various sources and then preprocessed by denoising, image equalization, enhancement, and data balancing, thereby minimizing the error rate. After feeding the processed data into the 2D convolutional neural network (CNN) model, we obtained overall training accuracy of 96.2%, with a validation accuracy of 91.1%. The precision and recall score of alopecia, psoriasis, and folliculitis are 0.895, 0.846, and 1.0, respectively. We also created a dataset of the scalp images for future prospective researchers.

show abstract

Applications of Emerging Machine Learning Models in Healthcare Industry: A Comprehensive Review

Gour,

Randa

2024

Algorithms for Intelligent Systems

View full text Add to dashboard Cite

CovidAlert - A Wristwatch-Based System to Alert Users from Face Touching

Cited by 7 publications

References 21 publications

Face touch monitoring using an instrumented wristband using dynamic time warping and k-nearest neighbours

Face touch monitoring using an instrumented wristband using dynamic time warping and k-nearest neighbours

Hair and Scalp Disease Detection using Machine Learning and Image Processing

Applications of Emerging Machine Learning Models in Healthcare Industry: A Comprehensive Review

Contact Info

Product

Resources

About