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Jadhav, Mahesh
- A Data-Driven Approach to Predict Anthropometric Dimensions of Central Indian Women Workers via Principal Component and Factorial Analysis
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Authors
Affiliations
1 Mechanical Processing Division, ICAR-National Institute of Natural Fibre Engineering and Technology, Kolkata 700 040, India., IN
2 All India Coordinated Research Project-ESA Scheme, ICAR-Central Institute of Agriculture Engineering, Bhopal 462 038, India., IN
3 ICAR-Central Institute of Agriculture Engineering, Bhopal 462 038, India., IN
4 Dr Balasaheb Sawant Konkan Krishi Vidyapeeth, Dapoli 415 712, India., IN
1 Mechanical Processing Division, ICAR-National Institute of Natural Fibre Engineering and Technology, Kolkata 700 040, India., IN
2 All India Coordinated Research Project-ESA Scheme, ICAR-Central Institute of Agriculture Engineering, Bhopal 462 038, India., IN
3 ICAR-Central Institute of Agriculture Engineering, Bhopal 462 038, India., IN
4 Dr Balasaheb Sawant Konkan Krishi Vidyapeeth, Dapoli 415 712, India., IN
Source
Current Science, Vol 124, No 2 (2023), Pagination: 215-225Abstract
In India, the contribution of women workers in agriculture is steadily increasing daily, which governs a major share of the Indian agriculture sector. Hence farm tools and equipment must be designed by considering region-specific anthropometric data of women workers. However, measuring and recording anthropometric dimensions is time-consuming and economically taxable. In the present study, regression models have been developed to predict different anthropometric dimensions using anthropometric data of 79 body dimensions of 720 women workers in central India aged between 25 and 55 years. Principal component and factorial analysis techniques were employed to extract significant body dimensions. The major objective of this study was to predict various anthropometric dimensions by regression models so that the time and effort required for several body dimension measurements would be reduced.Keywords
Agriculture, Correlation, Factor Analysis, Prin-Cipal Component Analysis, Women Workers.Full Text
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