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Context: Dental fear is a common and imperative emotion that develop as a response to the stressful situation, which raises children's anxiety level and resulting in avoidance behavior. Aims: The aim of this study is to evaluate and compare the tell-show-do technique with and without the aid of a virtual tool in the management of pediatric dental patients. Settings and Design: Department of Pediatric and Preventive Dentistry, nonrandomized clinical trial. Subjects and Methods: A total of 90 children of the age group of 6–9 years who were in the need of restorative treatment without using local anesthesia were assigned into two groups: Control group, where tell-show-do was applied as behavior management technique and intervention group in which tell-show-do with the aid of a virtual tool was applied. Child anxiety level was assessed using a combination of anxiety rating parameters before and after the procedure. Three physiological parameters (heart rate, oxygen saturation, and respiratory rate) and two behavioral parameters (Wright's modification of Frankl' behavior Rating Scale and Facial Image Scale) were recorded. Statistical Analysis Used: Physiological parameters were analyzed using the independent sample t-test and behavioral parameters using the Mann–Whitney U-test (P < 0.05). Results: A significant difference in all five parameters was observed between the control group and intervention group. Conclusions: Virtual tool offers a new concept of virtual distraction aid in pediatric dentistry, and it was found to be very effective in managing anxious pediatric patients. This promising method diminishes the unpleasantness often associated with dental procedures and offers a relaxed state in children.

The problem of estimating the heart rate (HR) from a racial video is considered. A typical approach for this problem is to use independent component analysis (ICA) on the red, blue, green intensity prof iles averaged over the facial region. This provides estimates of the underlying source signals, whose spectral peaks are used to predict HR in every analysis window. In this work, we propose a maximum likelihood formulation to optimally select a source signal in each window such that the predicted HR trajectory not only corresponds to the most likely spectral peaks but also ensures a realistic HR variability (HRV) across analysis windows. The likelihood function is efficiently optimized using dynamic programming in a manner similar to Viterbi decoding. The proposed scheme for HR estimation is denoted by vICA. The performance of vICA is compared with a typical ICA approach as well as a recently proposed sparse spectral peak tracking (SSPT) technique that ensures that the predicted HR does not vary drastically across analysis windows. Experiments are performed in a five fold setup using racial videos of 15 subjects recorded using two types of smartphones (Samsung Galaxy and iPhone) at three different distances (6inches, lfoot, 2feet) between the phone camera and the subject. Mean absolute error (MAE) between the original and predicted HR reveals that the proposed vICA scheme performs better than the best of the baseline schemes, namely SSPT by -8.69%, 52.77% and 8.00% when Samsung Galaxy phone was used at a distance of 6inches, lfoot, and 2feet respectively. These improvements are 12.13%, 13.59% and 18.34% when iPhone was used. This, in turn, suggests that the HR predicted from a racial video becomes more accurate when the smoothness of HRV is utilized in predicting the HR trajectory as done in the proposed vICA.