PHYSICAL THERAPY TREATMENT IN POSTNATAL WOMEN

Background: The postnatal period starts at the time when a mother gives birth to a baby; all changes that occur during pregnancy come back to a normal state like hormonal levels, size of the uterus, and weight. According to WHO this is the most ignorant period for postnatal women and their children by the society which is a bitter reality that’s why death is common in many mothers and children during the postnatal period. In Physical therapy, Gynecological rehabilitation is an important part of treatment and also a topic of consideration for referrals to doctors in the postnatal period to improve the issues related to postnatal women. Objective: To enhance the knowledge regarding the role of Physical therapy in the postnatal period of women. Search Strategy: This is a review article with extracts from various search engines like PubMed, CDC, MP, MCHI, Google scholar, Sci-Hub, etc. It is used to increase the knowledge regarding various treatment options in physical therapy that are helpful for postnatal women without causing side effects. Conclusion: Physical therapy consists of many interventions that can be beneficial, and it improves the quality of life of postnatal women.

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.