ASSESSMENT OF LOWER LIMB MUSCLE STRENGTH IN ATHLETES BY USING HAND-HELD DYNAMOMETER: A RELIABILITY STUDY

Background and Aims: Muscle strength is the key area to measure the functional status of an individual. Different tools and techniques has been used to detect strength differences and deficits. Hand- held dynamometer is one of the most affordable and handy tools used for this purpose. This study was designed to determine intra-rater reliability of hand- held dynamometer to measure muscle strength in different muscle groups of lower extremity of young athletes. It will further explore the reliability of hand- held dynamometer. Methodology: In this cross- sectional study young players of squash and badminton in the age group of 18-26 years were selected. The participants were recruited by non- probability convenience sampling technique. The strength of major muscle groups of lower limb was measured by a single male tester twice with gap through isometric make test of dynamometer. The intra-class correlation coefficient was then calculated for two readings of each muscle group by using SPSS version 21. Results: The intra- class correlation coefficient showed good to excellent reliability. The hip abductors, hip adductors, hip extensors of left side, knee flexors and knee extensors showed excellent reliability. Whereas, hip flexors, ankle plantar- flexors and dorsi-flexors of both sides showed excellent reliability at 95 % confidence interval. Conclusion: The isometric make test of dynamometer is a reliable tool for the objectification of strength of lower limb in   young players participating in squash and badminton.

Recognizing Human Actions in Realistic and Complex Scenarios Using Bag of Expression Boe Model

Human action recognition (HAR) has emerged as a core research domain for video understanding and analysis, thus attracting many researchers. Although signi cant results have been achieved in simple scenarios, HAR is still a challenging task due to issues associated with view independence, occlusion and inter-class variation observed in realistic scenarios. In previous research e orts, the classical Bag of Words (BoW) approach, along with its variations, has been widely used. In this dissertation, we propose a novel feature representation approach for action representation in complex and realistic scenarios. We also present an approach to handle the inter and intraclass variation challenge present in human action recognition. The primary focus of this research is to enhance the existing strengths of the BoW approach like view independence, scale invariance and occlusion handling. The proposed Bag of Expressions (BoE) includes an independent pair of neighbors for building expressions; therefore it is tolerant to occlusion and capable of handling view independence up to some extent in realistic scenarios. We apply a class-speci c visual words extraction approach for establishing a relationship between these extracted visual words in both space and time dimensions. To improve classical BoW, we propose a Dynamic Spatio-Temporal Bag of Expressions (D-STBoE) model for human action recognition without compromising the strengths of the classical bag of visual words approach. Expressions are formed based on the density of a spatiotemporal cube of a visual word. To handle inter-class variation, we use class-speci c visual word representation for visual expressions generation. The formation of visual expressions is based on the density of spatiotemporal cube built around each visual word, as constructing neighborhoods with axed number of neighbors would include non-relevant information hence making a visual expression less discriminative in scenarios with occlusion and changing viewpoints. Thus, the proposed approach makes our model more robust to occlusion and changing viewpoint challenges present in realistic scenarios. Comprehensive experiments on publicly available datasets show that the proposed approach outperforms existing state-of-the-art human action recognition approaches.