The Relationship Between Face Mask Use and Face-Touching Frequency in Public Areas: Naturalistic Study.

BACKGROUND: Throughout the COVID-19 pandemic in the United States, a major public health goal has been reducing the spread of the virus, with particular emphasis on reducing transmission from person to person. Frequent face touching can transmit viral particles from one infected person and subsequently infect others in a public area. This raises an important concern about the use of face masks and their relationship with face-touching behaviors. One concern discussed during the pandemic is that wearing a mask, and different types of masks, could increase face touching because there is a need to remove the mask to smoke, drink, eat, etc. To date, there have been few studies that have assessed this relationship between mask wearing and the frequency of face touching relative to face-touching behaviors. OBJECTIVE: This study aimed to compare the frequency of face touching in people wearing a mask versus not wearing a mask in high-foot traffic urban outdoor areas. The purpose of this study was to assess if mask wearing was associated with increased face touching. METHODS: Public webcam videos from 4 different cities in New York, New Jersey, Louisiana, and Florida were used to collect data. Face touches were recorded as pedestrians passed under the webcam. Adult pedestrians wearing masks were compared to those not wearing masks. Quantitative measures of frequency, duration, site of touch, and oral activities were recorded. Linear regression analysis was used to assess the association between mask use and face touching. RESULTS: Of the 490 observed subjects, 241 (49.2%) were wearing a mask properly and 249 (50.8%) were not. In the unmasked group, 33.7% (84/249) were wearing it improperly, covering the mouth only. Face touching occurred in 11.4% (56/490) of the masked group and 17.6% (88/490) in the unmasked group. Of those who touched their face, 61.1% (88/144) of people were not wearing a mask. The most common site of face touching was the perioral region in both groups. Both the masked and unmasked group had a frequency of face touching for 0.03 touches/s. Oral activities such as eating or smoking increased face touching in the unmasked group. CONCLUSIONS: Contrary to expectations, non-mask-wearing subjects touched their face more frequently than those who were wearing a mask. This finding is substantial because wearing a face mask had a negative association with face touching. When wearing a mask, individuals are less likely to be spreading and ingesting viral particles. Therefore, wearing a mask is more effective in preventing the spread of viral particles.

Arduino based Anti-Face-Touching Device and prediction model

Due of the fast pace at which COVID-19 may spread through respiratory illness, the terrible condition it was in heightened public tension. The WHO's primary recommendations advised against often touching your face in order to avoid the transmission of viruses through your lips, eyes, and nose. According to research, the typical person was discovered to touch their face about 20 times each hour since it is everyone's unconscious behavior. In order to cope with this, the study suggests a hardware model that recognizes hand motions that are made in the direction of the user's face and alerts them to such movements using both aural and visual sensory feedback modalities. In order to create a model for the prediction of facial touch motions, the study analyses deep learning architectures in more detail. The FaceGuard device, which is a deep learning-based prediction model used to determine whether or not a hand movement would result in face contact, is compared to the accuracy of the suggested hardware model in the paper 'FaceGuard: A Wearable System To Avoid Face Touching1.' It alerts the user through vibrotactile, aural, and visual sensory modalities. After investigation, it was discovered that the hardware model had less accuracy than the deep learning model and required shorter time to respond to vibro tactile sensory data. © 2022 IEEE.

Hyperparameter Tuning in Convolutional Neural Network for Face Touching Activity Recognition using Accelerometer Data

People have been encouraged to wear masks and avoid touching their faces in public as part of the new measures to prevent the spread of coronavirus 2019 (COVID-19). During the COVID-19 epidemic, few research have examined the effect of everyday living on the frequency of facial touch activity. To develop a face touching avoidance system, deep learning algorithms have been proposed and have demonstrated their amazing performance. However, an important drawback of deep learning is its extensive dependence on hyperparameters. The results of deep learning algorithms may vary depending on hyperparameters, such as the size of the filters, the number of filters, the batch size, the number of epochs, and the training optimization technique used. In this paper, we present an effective approach for hyperparameter tuning of convolutional neural networks (CNNs) for efficiently recognized face touching activities based on accelerometer data. Two hyperparameter tuning methods (Grid search and Bayesian optimization) were evaluated in order to construct the CNN with high performance. The experiment results show that Bayesian optimization can provide suitable hyperparameters for CNNs for face touching recognition with the highest accuracy of 96.61%. © 2022 IEEE.

Effectiveness of a Brief Mindfulness-Based Intervention of "STOP touching your face" During the COVID-19 Pandemic: a Randomized Controlled Trial.

Objectives: Avoiding touching the eyes, nose, and mouth (T-zone) is a strategy to reduce the spread of COVID-19. This study evaluated the effectiveness of a brief mindfulness-based intervention (MBI) named "STOP (Stop, Take a Breath, Observe, Proceed) touching your face" for reducing face-touching behavior. Methods: In this online-based, two-arm, wait-list, randomized controlled trial, eligible participants were randomly assigned to the intervention (n = 545) or control group (n = 545). The results of 60-min self-monitoring of face-touching behavior were reported before and after the intervention. Reduction of the percentage of T-zone touching was the primary outcome, and reduction of face-touching frequency was a key secondary outcome. Outcomes were analyzed on an intention-to-treat (ITT) basis with a complete case analysis (CCA). Results: ITT analysis revealed that the percentage of T-zone touching was significantly reduced by 8.1% in the intervention group (from 81.1 to 73.0%, RR = 0.901, OR = 0.631, RD = - 0.081, p = 0.002), and insignificantly reduced by 0.6% in the control group (from 80.0 to 79.4%, p = 0.821). Fewer participants performed T-zone touching in the intervention group than in the control group (73.0% vs. 79.4%, RR = 0.919, OR = 0.700, RD = - 0.064, p = 0.015) after the intervention, and there was a greater reduction of T-zone touching frequency in the intervention group than in the control group [mean ± SD: 1.7 ± 5.13 vs. 0.7 ± 3.98, mean difference (95% CI): 1.03 (0.48 to 1.58), p < 0.001, Cohen's d = - 0.218]. The above results were further confirmed by CCA. Conclusions: This brief mindfulness-based intervention was potentially effective at reducing the spread of COVID-19 and could be further investigated as an intervention for preventing other infectious diseases spread by hand-to-face touching. Trial Registration: ClinicalTrials.gov NCT04330352. Supplementary Information: The online version contains supplementary material available at 10.1007/s12671-022-02019-x.

Face masks can reduce the frequency of face touching: an observational crossover study.

OBJECTIVES: The purpose of this study was to analyze face-touching patterns with and without a face mask. METHODS: The behavior of face touching with and without a mask during an interview was assessed in 40 individuals. The frequency of touching in different areas of the face covered by the mask was compared with areas not covered by the face mask. RESULTS: There was an increase in the number of individuals who touched the hair and the eye when they were not wearing the mask. There was an increase in the number of touches on the lips and hair when individuals were not wearing the face mask. When analyzing the area covered by the face mask, no difference was observed in the number of touches while using or not using masks. However, when the area not covered by a face mask was analyzed, a higher number of touches in individuals without masks was observed when compared with individuals wearing masks. CONCLUSION: Using a face mask can reduce or change the face-touching patterns in normal individuals, especially in areas not covered by the mask. Using face masks can possibly reduce the chances of being infected by autoinoculation.

Bidirectional Gate Recurrent Unit Neural Network for Recognizing Face Touching Activities using Smartwatch Sensors

Globally, the COVID-19 pandemic has caused dev-Astation and continues to do so even a year after its first outbreak. Behavioral modifications could help to mitigate a mechanism for acquiring and spreading illnesses. Using wearable devices such as smartwatches to recognize face contact has the opportunity to decrease face touching and, therefore, the spread of respiratory disease through fomite transmission. The purpose of this paper is to demonstrate how we can utilize accelerometer data from wristwatch sensors to identify face touching actions using deep learning techniques. We proposed the BiGRU deep learning model for the high-performance recognition of hand-To-face actions. The Face Touching dataset is used as a benchmark for evaluating the recognition accuracy of deep learning networks, including our network model. The experimental findings indicate that the BiGRU surpasses other baseline deep learning models regarding accuracy (98.56%) and F1-score (98.56%). © 2021 IEEE.

The self-regulation of face touching - a preregistered experiment testing if-then plans as a means to promote COVID-19 prevention.

Objective: Reducing face touching could help slow COVID-19's spread. We tested whether implementation intentions, a simple-to-use behaviour change intervention, reduce face-touching behaviour effectively.Design: In this pre-registered online study, we utilised a novel way to collect behavioural data during a pandemic. We obtained video recordings of 156 adults while performing three engaging tasks for four minutes each. After the baseline task, participants formed the goal to avoid touching their faces; some participants also formed implementation intentions, targeting either the frequency or duration of face touching.Main Outcome Measures: The 468 videos were rated by two independent raters for face touching frequency and duration.Results: Face touching was widespread. Compared to the baseline, there was a slight reduction in the frequency of face touching after the experimental manipulations. We observed a significant decrease in the length of face touching only for participants with duration-focused implementation intentions.Conclusion: While implementation intentions have effectively downregulated other unwanted behaviours, they did not reduce the frequency of face-touching behaviour. Still, duration-focused implementation intentions appear to be a promising strategy for face-touching behaviour change. This highlights the need for further optimisation and field research to test the effectiveness of implementation intentions in everyday life contexts.

U can't touch this! Face touching behaviour whilst driving: implications for health, hygiene and human factors.

Analysis of thirty-one hours of video-data documenting 36 experienced drivers highlighted the prevalence of face-touching, with 819 contacts identified (mean frequency: 26.4 face touches/hour (FT/h); mean duration: 3.9-seconds). Fewer face-touches occurred in high primary workload conditions (where additional physical/cognitive demands were placed on drivers), compared to low workload (4.4 and 26.1 FT/h, respectively). In 42.5% of touches (or 11.2 FT/h), mucous membrane contact was made, with fingertips (33.1%) and thumbs (35.6%) most commonly employed. Individual behaviours differed (ranging from 5.1 to 90.7 FT/h), but there were no significant differences identified between genders, age-groups or hand used. Results are of relevance from an epidemiological/hygiene perspective within the context of the COVID-19 pandemic (and can therefore inform the design of practical solutions and encourage behavioural change to reduce the risk of self-inoculation while driving), but they also help to elucidate how habitual human behaviours are imbricated with the routine accomplishment of tasks.

Practitioner summary: The study highlights the propensity of face touching whilst driving through the analysis of on-road video datasets. Results have implications for the design of technological interventions (such as touchless interfaces and driver monitoring systems) and can inform awareness campaigns to reduce the risk of self-inoculation and infection transmission while driving.

Using Smartwatches to Detect Face Touching.

Frequent spontaneous facial self-touches, predominantly during outbreaks, have the theoretical potential to be a mechanism of contracting and transmitting diseases. Despite the recent advent of vaccines, behavioral approaches remain an integral part of reducing the spread of COVID-19 and other respiratory illnesses. The aim of this study was to utilize the functionality and the spread of smartwatches to develop a smartwatch application to identify motion signatures that are mapped accurately to face touching. Participants (n = 10, five women, aged 20-83) performed 10 physical activities classified into face touching (FT) and non-face touching (NFT) categories in a standardized laboratory setting. We developed a smartwatch application on Samsung Galaxy Watch to collect raw accelerometer data from participants. Data features were extracted from consecutive non-overlapping windows varying from 2 to 16 s. We examined the performance of state-of-the-art machine learning methods on face-touching movement recognition (FT vs. NFT) and individual activity recognition (IAR): logistic regression, support vector machine, decision trees, and random forest. While all machine learning models were accurate in recognizing FT categories, logistic regression achieved the best performance across all metrics (accuracy: 0.93 ± 0.08, recall: 0.89 ± 0.16, precision: 0.93 ± 0.08, F1-score: 0.90 ± 0.11, AUC: 0.95 ± 0.07) at the window size of 5 s. IAR models resulted in lower performance, where the random forest classifier achieved the best performance across all metrics (accuracy: 0.70 ± 0.14, recall: 0.70 ± 0.14, precision: 0.70 ± 0.16, F1-score: 0.67 ± 0.15) at the window size of 9 s. In conclusion, wearable devices, powered by machine learning, are effective in detecting facial touches. This is highly significant during respiratory infection outbreaks as it has the potential to limit face touching as a transmission vector.

Can Face- and Smartphone-Touching Behaviors Be Altered with Personal Hygiene Reminders during the COVID-19 Pandemic Period? An Observational Study.

As part of the new measures to prevent the spread of the 2019 coronavirus disease (COVID-19), medical students were advised to wear a mask in class and avoid touching their faces. Few studies have analyzed the influence of health education on the frequency of face- and smartphone-touching behaviors during the COVID-19 pandemic. This research compared the frequency of in-class face- and smartphone-touching behaviors of medical students before and after the delivery of personal hygiene education during the COVID-19 pandemic. A behavioral observational study was conducted involving medical students at Taipei Medical University. Eighty medical students were recruited during a lecture on otorhinolaryngology. All medical students were required to wear a mask. Their face- and smartphone-touching behavior was observed by viewing the 4 k resolution video tape recorded in class. The recording lasted for 2 h, comprising 1 h prior to the health educational reminder and 1 h afterwards. The frequencies of hand-to-face contact and hand-to-smartphone contact were analyzed before and after the delivery of health education emphasizing personal hygiene. Comprehensive health education and reminders effectively reduce the rate of face- and smartphone-touching behaviors.

Is Face and Mask Touching a Real Struggle During the COVID-19 Pandemic? A Prospective Study Among Medical Students.

There are limited data in the literature on the frequency of face- and mask-touching behavior as a potential vector for the self-inoculation and transmission of the novel coronavirus. In this prospective study, we assessed the facial touching behavior of 204 medical students. One hundred thirty-four subjects (65.68%) during the 15-min observation at least once touched the area of the mask (38.23%), eyes (38.23%), or other parts of the facial zone (49.02%). The mean number of touches was 11.98 ± 16.33 per hour. The results of our study reveal that there is no significant association between mask wearing and gender; however, there might be a tendency for people with eyeglasses to touch the area near the eyes more often.

Face-touching behaviour as a possible correlate of mask-wearing: A video observational study of public place incidents during the COVID-19 pandemic.

Most countries in the world have recommended or mandated face masks in some or all public places during the COVID-19 pandemic. However, mask use has been thought to increase people's face-touching frequency and thus risk of self-inoculation. Across two studies, we video-observed the face-touching behaviour of members of the public in Amsterdam and Rotterdam (the Netherlands) during the first wave of the pandemic. Study 1 (n = 383) yielded evidence in favour of the absence of an association between mask-wearing and face-touching (defined as touches of face or mask), and Study 2 (n = 421) replicated this result. Secondary outcome analysis of the two studies-analysed separately and with pooled data sets-evidenced a negative association between mask-wearing and hand contact with the face and its t-zone (i.e. eyes, nose and mouth). In sum, the current findings alleviate the concern that mask-wearing has an adverse face-touching effect.

FaceGuard: A Wearable System To Avoid Face Touching.

Most people touch their faces unconsciously, for instance to scratch an itch or to rest one's chin in their hands. To reduce the spread of the novel coronavirus (COVID-19), public health officials recommend against touching one's face, as the virus is transmitted through mucous membranes in the mouth, nose and eyes. Students, office workers, medical personnel and people on trains were found to touch their faces between 9 and 23 times per hour. This paper introduces FaceGuard, a system that utilizes deep learning to predict hand movements that result in touching the face, and provides sensory feedback to stop the user from touching the face. The system utilizes an inertial measurement unit (IMU) to obtain features that characterize hand movement involving face touching. Time-series data can be efficiently classified using 1D-Convolutional Neural Network (CNN) with minimal feature engineering; 1D-CNN filters automatically extract temporal features in IMU data. Thus, a 1D-CNN based prediction model is developed and trained with data from 4,800 trials recorded from 40 participants. Training data are collected for hand movements involving face touching during various everyday activities such as sitting, standing, or walking. Results showed that while the average time needed to touch the face is 1,200 ms, a prediction accuracy of more than 92% is achieved with less than 550 ms of IMU data. As for the sensory response, the paper presents a psychophysical experiment to compare the response time for three sensory feedback modalities, namely visual, auditory, and vibrotactile. Results demonstrate that the response time is significantly smaller for vibrotactile feedback (427.3 ms) compared to visual (561.70 ms) and auditory (520.97 ms). Furthermore, the success rate (to avoid face touching) is also statistically higher for vibrotactile and auditory feedback compared to visual feedback. These results demonstrate the feasibility of predicting a hand movement and providing timely sensory feedback within less than a second in order to avoid face touching.

Smart homes that detect sneeze, cough, and face touching.

Coughing, sneezing, and face touching activities are three primary ways of spreading disease. With the onset of COVID-19 it is paramount to monitor these activities at home and practice good hygiene. To help stop the spread of disease, we have developed a wireless sensing system capable of detecting voluntary coughs, sneezes, and face touching with alert based notifications sent to a mobile application. Our system uses radio frequency technology to capture motion, speed, direction, and range information from human activities. It does this by using a combination of a continuous wave Doppler and frequency modulated continuous wave radar. By observing a set of features related to the sensed motion, we designed a set of fuzzy logic IF-THEN rules that can differentiate each activity from each other with an overall accuracy of 96%. In addition, our system enables smart homes to detect and localize these activities at different distances up to 2.74 m, through walls, and with multiple people. We envision our system helping not only with prevention of COVID-19, but supporting contact tracing efforts by monitoring people's activities at home.

Reducing risky behavior with habit reversal: A review of behavioral strategies to reduce habitual hand-to-head behavior.

Habit reversal training (HRT) has been a mainstay of behavior analysts' repertoire for nearly the last 50 years. HRT has been effective in treating a host of repetitive behavior problems. In the face of the current coronavirus pandemic, HRT has practical public health importance as a possible intervention for reducing hand-to-head behaviors that increase the risk of viral infection. The current paper provides a brief review of HRT for hand-to-head habits that is designed for a broad audience and concludes with practical suggestions, based on HRT, for reducing face-touching behaviors.

The use of facemasks may not lead to an increase in hand-face contact.

Advocacy of the use of facemasks by the public as a measure against the spread of COVID-19 is controversial, with some healthcare professionals arguing that the use of a face mask may increase the rate at which people touch their faces, due to readjusting the mask. We assessed the facial touching behaviour of bus passengers in China before and after the outbreak of COVID-19 and found that wearing a face mask does not increase the number of hand-face contacts and is likely, therefore, to have a positive beneficial effect on suppressing the spread of COVID-19 within populations when used in conjunction with social distancing measures.