Detecting Face-Touch Hand Moves Using Smartwatch Inertial Sensors and Convolutional Neural Networks

Sehirli, Eftal; Alesmaeil, Abdullah

doi:10.18201/ijisae.2022.275

Cited by 4 publications

(8 citation statements)

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“…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%

“…Wearable sensors were also used to detect face touch. Previous research studies proposed methods for face touch detection using an accelerometer, magnetometer, and acoustic-based system and studied the validity of these methods [27][28][29][30][31][32][33]. Marullo et al [27] proposed recurrent neural network (RNN) based methods to detect face touch and provide real-time feedback using accelerometer readouts collected from a smartwatch.…”

Section: Introductionmentioning

confidence: 99%

“…A DTW classifier was proposed by Chen et al [30] that was using accelerometer readout to detect face touch with 99% and 85% accuracy in userdependent and user-independent. In another studies, Michelin et al [31] and Alesmaeil et al [32] investigated the ability of IMU and Convolutional Neural Networks (CNN) to detect face touch. The method proposed by Michelin et al was validated on the data collected from 40 participants during sitting, standing, and walking trials and obtained an accuracy of 95.7%.…”

Section: Introductionmentioning

confidence: 99%

“…Their experimental setup was composed of an IMU, a processing unit (laptop), and a cable to connect the IMU to the laptop. 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].…”

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%

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

Fathian

Phan

et al. 2023

PLoS ONE

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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.

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Section: Plos Onementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Fathian

Phan

et al. 2023

PLoS ONE

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Recognition of gangues from color images using convolutional neural networks with attention mechanism

Liu

2023

Measurement

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Retracted: Object detection and recognition using deep learning‐based techniques

et al. 2022

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The above article from IET Communications, published online on 17 October 2022 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the Interim Editor‐in‐Chief, Jian Ren, the Institution of Engineering and Technology (the IET) and John Wiley and Sons Ltd. This article was published as part of a Guest Edited special issue. Following an investigation, the IET and the journal have determined that the article was not reviewed in line with the journal's peer review standards and there is evidence that the peer review process of the special issue underwent systematic manipulation. Accordingly, we cannot vouch for the integrity or reliability of the content. As such we have taken the decision to retract the article. The authors have been informed of the decision to retract.

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Detecting Face-Touch Hand Moves Using Smartwatch Inertial Sensors and Convolutional Neural Networks

Cited by 4 publications

References 0 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

Recognition of gangues from color images using convolutional neural networks with attention mechanism

Retracted: Object detection and recognition using deep learning‐based techniques

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