OCCURRENCE OF LOWER EXTREMITY MUSCULOSKELETAL INJURIES DURING THE LOCKDOWN IN ATHLETES

Background of the Study: Lockdown was implemented worldwide to limit the spread of COVID-19. This sudden implementation of lockdown causes significant lifestyle changes for every individual. Along with the general population, it also has psychological, behavioral, and physical consequences on athletes. The study objective is to determine the occurrence of lower extremity musculoskeletal injuries during the COVID-19 lockdown in athletes. Methodology: Retrospective cross-sectional study design was used, and participants were recruited by a non-probability convenient sampling technique. A sample size of 147 was taken as calculated by the Raosoft software, and the study was completed 6 months. Both male and female athletes between the age group of 18-35 years, participants who did not participate in any official training session during the lockdown and registered at domestic level for at least 2 years were recruited from Pakistan Sports Board and Wapda Sports Complex Lahore. Data was collected using a semi-structured questionnaire. Nordic Musculoskeletal Questionnaire was used to identify the problematic painful areas of body. Data entry, analysis, and interpretation were done by using SPSS software version 22.0. Results: The mean age and BMI of participants were 25.6531±4.49 (years) and 23.28±3.24 (kg/m2) respectively. From the total, 39.5% of participants reported lower extremity musculoskeletal injuries. And most reported problematic areas include lower back and knee. 75% of participants continue to do workouts at home as a prevention strategy against injury occurrence. Conclusion: This concluded that the occurrence of lower extremity musculoskeletal injuries during the lockdown was moderate.

Developing Genetic Programming Based Image Denoising Systems

During acquisition or transmission, the visual quality of digital images is deteriorated due to the occurrence of impulse and speckle noises. These noises adversely effect various applications in image processing, pattern recognition, computer vision and medical imaging. Due to emerging imaging applications, recent trend is to develop application specific denoising systems. In this thesis, genetic programming (GP) based various denoising systems are developed for impulse and speckle noises. The proposed GP based evolutionary systems have effectively developed the domain specific denoising models that select the optimal informative features from the corrupted images. In the first phase of research, the genetic programming based mixed impulse denoising (GP-MID) system is developed to improve the visual quality of corrupted digital images. In this system, GP has optimally/near-optimally selects suitable statistical features to remove noise. In the second phase, the genetic programming based multi-type impulse denoising (GP-MuID) system is developed for corrupted digital images. This system has successfully removed salt & pepper, uniform impulse, mixed impulse and impulse burst noises, simultaneously. In the third phase, an advanced version of multi-gene genetic programming (MGGP) based biomedical image denoising (MGGP-BmID) system is developed to improve the visual quality of biomedical images. In the last phase, the multi-gene genetic programming based ultrasound image denoising (MGGP-UsID) system is developed to denoise speckle from ultrasound images. The improved performance of the GP based systems is obtained for diverse types of natural and biomedical images. The comparative analysis with existing approaches highlights the effectiveness of the proposed GP based evolutionary denoising systems. The improved denoising performance is achieved by the proposed GP based systems. It is because, during evolutionary learning process, the useful statistical features and primitive functions from a wider solution space are optimally/near-optimally combined to develop GP based intelligent noise detectors and estimators for image denoising problems.