A Method for Detecting Non-Mask Wearers Based on Regression Analysis

A novel practical and universal method of mask-wearing detection has been proposed to prevent viral respiratory infections.The proposed method quickly and accurately detects mask and facial regions using welltrained You Only Look Once(YOLO)detector,then applies image coordinates of the detected bounding box(bbox).First,the data that is used to train our model is collected under various circumstances such as light disturbances,distances,time variations,and different climate conditions.It also contains various mask types to detect in general and universal application of the model.To detect mask-wearing status,it is important to detect facial and mask region accurately and we created our own dataset by taking picture of images.Furthermore,the Convolutional Neural Network(CNN)model is trained with both our own dataset and open dataset to detect under heavy foot-traffic(Indoors).To make the model robust and reliable in various environment and situations,we collected various sample data in different distances.And through the experiment,we found out that there is a particular gradient according to the mask-wearing status.The proposed method searches the point where the distance between the gradient for each state and the coordinate information of the detected object is the minimum.Then it carry out the classification of mask-wearing status of detected object.Lastly,we defined and classified three different mask-wearing states according to the mask’s position(With mask,Wear a mask around chin and Without mask).The gradient according to the mask-wearing status,is analyzed through linear regression.The regression interpretation is based on coordinate information of mask-wearing status and the sample data collected in simulated environment that considering distances between objects and the camera in the World Coordinate System.Through the experiments,we found out that linear regression analysis is more suitable than logistic regression analysis for classification of people wearing masks in general-purpose environments.And the proposed method,through linear regression analysis,classifies in a very concise way than the others.

Effects of short-term adaptation to new complete dentures on perception thresholds for interocclusal thickness in experienced and non-experienced denture wearers

Background The ability of patients to discriminate thickness can change with time after the insertion of new complete dentures, and this adaptation may differ according to the experiences of the patients If so, an education program to reinforce the oral functions may be needed This study was to evaluate patients' ability to discriminate thickness by comparing experienced and non-experienced denture wearers after the insertion of new complete dentures Methods Forty edentulous patients with a skeletal Class Ⅰ jaw relationship without any temporomandibular disturbances were in the study After insertion of new complete dentures, all patients were tested for discrimination threshold for interocclusal thickness The thickness perception test was repeated on days 1, 7, and 30 after the insertion of new dentures The discriminatory ability was assessed with steel and aluminium foils placed between the upper and lower central incisor teeth The records included the duration of wearing dentures The data were subjected to statistical analysis of two-way ANOVA, multiple comparison test and Student's t test Results Discrimination of tactile ability significantly increased over 30 days in experienced denture wearers as compared with non-experienced patients ( P <0 001) The difference in thickness perception threshold was statistically significant between the two groups ( P <0 001) Conclusion Adaptation and denture experience can affect thickness discrimination of complete denture wearers