Informatics Publishing Limited

Abhishek Sharma , Arpita Shah , Sushant Dey , Urvi Sharma

Abstract

Objective: Endurance is the ability to work for prolonged period of time. Individuals of all ages can improve their general fitness & Endurance by participating in activities that include walking, biking, cycling, running, swimming, stair climbing, and weight training etc2. In rehabilitation centers we come across several patients for improvement of their general fitness & endurance. We used to give them training on Treadmill & Cycle Ergometer. The aim of the study was to compare the effectiveness of treadmill with cycle ergometer on endurance in normal subjects.

Material and Method: Total 20 subjects divided in group A & group B (10 subjects in each group) Training using treadmill and cycle ergometer respectively was for continuous 30 days with progression regularly. 12 minute walk test walk distance was used to measure the endurance after training.

Results: There is no significant difference in effectiveness of treadmill & cycle ergometer on endurance in normal subjects. The value of tc obtained for walk distance for treadmill & cycle trainee is 0.1998 0.2

Conclusion: The study concluded that Bicycle Ergo meter is appropriate alternative to Treadmill for training in healthy volunteers. Training programme should be individualized.

Keywords

Endurance, Treadmill, Cycle Ergometer

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Amit Thakkar ¹, Nikita Bhatt ¹, Amit Ganatra ¹, Arpita Shah ¹

Affiliations
1 Charotar Institute of Technology Changa, Nadiad, Gujarat, IN

Abstract

Clustering is the process of finding groups of objects such that the objects in a group will be similar to one another and different from the objects in other groups. K-means is a well known partitioning based clustering technique that attempts to find a user specified number of clusters represented by their centroid. K-means clustering algorithm often does not work well for high dimension; hence, to improve the efficiency, we apply PCA, dimensionality reduction technique, on data set and obtain a reduced dataset containing possibly uncorrelated variables. The challenging task for any clustering method is to determine the number of clusters beforehand. To find the number of cluster, we apply EM method that finds number of clusters user should choose by determining a mixture of Gaussians that fit a given data set. Finally the experiment results shows that the use of techniques such as PCA and EM, improve the efficiency of K-means clustering.

Keywords

Cluster, EM, K-Mean, PCA.

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