Evaluation of Perception and Awareness of Dental Injuries among Sports Children Aged 6-18 Years and their Coaches during Contact Sports

Introduction: Dental injuries are the most common form of orofacial injury suffered during sports participation. This injury can be severe, affecting appearance, voice, and ability to eat. Reputation of contact sports activities is growing day-by-day so the function of dental specialists has turn out to be extra essentials to prevent dental and orofacial injuries. Aim: To evaluate perception and awareness of dental injury in sports children and their coaches during contact sports. Materials and Methods: A cross-sectional study was conducted between August 2019 to October 2019 at three sports complexes in Vadodara, Gujarat. A structured questionnaire was used to assess the perception and awareness of oral injuries sustained during sports activities. Self-administered questionnaire with 20 closed ended questions for coaches and 18 closed ended questions for children were constructed with multiple choice or Yes-No format. Total of 50 coaches and 240 children were included in the study. Results: Most common sustained injury occurred on the face (41.7%), followed by lip/tongue/cheek injury (22.9%), teeth fracture (10.4%) and teeth avulsion (7.5%). Among all participants, 66 (27.5%) sports children and 27 (54.0%) coaches knew that it was possible to re-implant the teeth. A total of 118 (49.2%) sports children were aware that mouthguard can prevent dental injuries and 31(86%) coaches had advised children to use mouthguards while playing contact sports. Conclusion: The knowledge related to sports injury is poor and under-usage of protective devices requires education and motivation

Finding faces in photographs

Two new schemes are presented for Ending human faces in a photograph. The first scheme approximates the unknown distributions of the face and the face-like manifolds using higher order statistics (HOS). An HOS-based data clustering algorithm is also proposed, lit. the second scheme, the face to non-face and non-face to face transitions are learnt using a hidden Markov model (HMM). The HMM parameters are estimated corresponding to a given photograph and the faces are located by examining the optimal state sequence of the HMM. Experimental results are presented on the performance of both the schemes