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Background: Osteoarthritis is the eighth leading cause of disability and a degenerative disease that worsens over time. Hence, early diagnosis and treatment remains the key in the management of Osteoarthritis. The aim and objective of the study was to evaluate the usefulness of WOMAC index for screening Osteoarthritis among the patients older than 50 years of age attending a Rural Health Centre.Methods: A cross sectional study was done among patients complaining of knee pain who visited a rural centre between June-August 2016. Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) was applied to all participants with knee pain to assess risk for OA. Also, American College of Rheumatology criteria (ACR) was used as a standard. Descriptive statistics were used. Chi-square test with Odds Ratio was calculated to find out of the strength of association. To test the agreement of WOMAC score with ACR Clinical Criteria, McNemar’s test analysis was done.Results: Out of 103 study subjects 45 were males and 58 were females. According to WOMAC Index Scores, 20 (19.4%) subjects belonged to high risk (score ≥81) and 38 (39.6%) subjects belonged to moderate risk (score 60 - 80). The Mean±SD WOMAC Score was 64.40±15.2. Age group [OR 2.85; 95% CI 1.25-6.48) and gender [OR 2.29; 95% CI 1.01-5.24) were significantly associated with WOMAC score percentage. Comparing WOMAC score percentages with ACR criteria for knee OA revealed statistically significant agreement (p-value, 0.009) which indicated the diagnostic accuracy of WOMAC index.Conclusions: WOMAC Index can be a useful screening tool for people at risk for Osteoarthritis and will help in identifying the disease early.

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In an E-learning classroom, students and professors are at different geographic locations. In the present E-learning system, the professor and the student interact with each other through simple internet applications. The disadvantage in this type of classroom set-up is that students in a remote location will not receive the same attention as those ones in a real classroom. Students in a remote location will only be seen on a small screen and the clarity of the video might be poor. Furthermore, the professor in a real classroom might focus only on students who are interacting face-to-face with him. The professor may not know the details of the remote students and how well they understand his class lecture. To solve this problem, this paper describes a system, based on Kinect TM , to make the E-learning class lecture more interactive for the remote students. For example, when a student in a remote location, raises his/her hand to talk to a professor, the Kinect camera detects the student, identifies the gesture of the remote student, zooms onto the remote student, and displays details such as his/her name and background of study. Thus the professor can pay equal attention to both the remote and local students.