Designing a model of cultural barriers for women's participation in leisure sports activities in sports recreation centers

Purpose: Leisure is one of the dimensions of social life that if it contradicts the common culture of society, it can become a social problem or dilemma; Therefore, the present study was conducted with the aim of designing a model of cultural barriers to women's participation in leisure sports activities in sports recreation centers.Methodology: The research method in terms of type and data analysis was qualitative with a data theorizing approach. The statistical population of the study consisted of senior managers, officials and faculty members aware of the field of research, which in order to select the sample people, purposeful sampling method based on a theoretical approach was used. The required data were collected using in-depth interviews and analyzed in three stages of open, axial and selective coding.Results: Based on the obtained results, causal conditions include the existence of non-indigenous culture, patriarchal perspective, weakness in the capital of women's sports culture, lack of social support and motivational barriers; Underlying conditions including economic factors, facility and facility factors and managerial factors and intervening conditions including individual factors and religious tendencies were identified. Strategies were introduced in three areas of needs assessment and policy-making, localization and alignment of relevant bodies, and after presenting the consequences, a paradigm model was presented.Conclusion: Needs assessment and policy-making, alignment of organs to achieve goals and localization of leisure activities have desirable results in order to remove cultural barriers to the presence of women in sports and recreation centers and use the benefits, Will bring

GSR Analysis for Stress: Development and Validation of an Open Source Tool for Noisy Naturalistic GSR Data

The stress detection problem is receiving great attention in related research communities. This is due to its essential part in behavioral studies for many serious health problems and physical illnesses. There are different methods and algorithms for stress detection using different physiological signals. Previous studies have already shown that Galvanic Skin Response (GSR), also known as Electrodermal Activity (EDA), is one of the leading indicators for stress. However, the GSR signal itself is not trivial to analyze. Different features are extracted from GSR signals to detect stress in people like the number of peaks, max peak amplitude, etc. In this paper, we are proposing an open-source tool for GSR analysis, which uses deep learning algorithms alongside statistical algorithms to extract GSR features for stress detection. Then we use different machine learning algorithms and Wearable Stress and Affect Detection (WESAD) dataset to evaluate our results. The results show that we are capable of detecting stress with the accuracy of 92 percent using 10-fold cross-validation and using the features extracted from our tool.Comment: 6 pages and 5 figures. Link to the github of the tool: https://github.com/HealthSciTech/pyED