Wearable Technology based Face Alert System to Curb the Spread of Covid-19

Increasing people's perception of their habitual face-touching behaviour and ameliorating their acknowledgment of self-inoculation as a medium of transmission may assist to curb the spread of novel coronavirus (COVID-19). On average, human beings generally touch their faces 23 times per hour. Therefore, hand hygiene is an essential preventive measure to stop the spread of COVID-19. This motivates to introduce an alert mechanis m using wearable technology that aims to alert a person whenever he/she brings his/her hands close to the face. The proposed face alert system is based upon deep learning technique to forecast hand movements followed by face touching and imparts sensory response to alert end-user to stop the face touching activities. The proposed system employs IMU to get features belonging to different hand movements resulting in face touching. The data can be effectively classified using CNN where the filters help in extracting temporal features from IMU data. The prediction model based upon CNN is developed with training data from four thousand eight hundred trials recorded from forty participants. The trained dataset of hand movements activities is collected during day-to-day activities, e.g., walking, sitting, etc. Results demonstrated a forecast accuracy of 90% is obtained with 550ms of IMU data. In a research study, the psychophysical experiment is conducted to compare the response time for sensational observation methods, e.g., auditory, visual and vibrotactile. It has been observed that the response time is remarkably higher for visual (VF) and auditory feedback (AF) in comparison to vibrotactile feedback (VTF). Moreover, the rate of success is analytically lesser for visual feedback compared to vibrotactile and auditory feedback. Practically, results indicate a prediction of the movement of hand, and timely generation of sensational response in less than a second, so that one does not touch the face, and thus curbing of the spread of COVID-19.