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Mining of anthropometric data by clustering technique would categorically classify homogenous body size group. The objective of this study is to classify homogenous human body size according to anthropometric body composition indicators. Anthropometric data was measured from 382 men and 391 women of Orissa, India. Percent body fat was calculated from the skinfold parameters. Cluster analysis was then applied with self reported age, stature, weight and percent body fat. The clusters formed were tested statistically for their validity of formation. Multivariate analysis considering age, stature, weight and percent body fat revealed significant differences among men and women (p<0.001). Expectation Maximization (EM) estimated five clusters for men and women by age, stature, weight and percent body fat. Consequently, k-means cluster algorithm was used and it formed five clusters with cumulative increment in stature, weight and percent body fat, for both men and women. However, age individually, did not influence the body size indicators. The clusters were named small, medium, large, X-large and XX-large. Silhouette plot validation of clusters reveals that for both men and women, 95.5% and 98.7% of data, respectively were well-clustered. These cluster results further can generate classification rules to categorize subsequent unseen cases, and may aid in anthropometric database creation, nutritional status, body growth research, etc.

Keywords

Anthropometry, Cluster Analysis, K-Means, Silhouette Plot, Body Sizing.

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References

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Data Mining Approach to Pre-Screening of Manual Material Handlers Based on Hand Grip Strength

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Authors

Lokesh Kumar Sharma ¹, Joydeep Majumder ¹

Affiliations
1 ICMR-National Institute of Occupation Health, Ahmedabad – 380016, Gujarat, IN

Source

Journal of Ecophysiology and Occupational Health, Vol 17, No 1-2 (2017), Pagination: 57-62

Abstract

The general incidence of musculoskeletal disorders (MSD) is one the major causes of mobility and concerns about financial and physical losses. The more objective pre-employment selection and employee placement test methods can reduce the work related injuries and Employers in general prefer for post-offer pre-placement (POPP) testing as a tool for evaluating worker's physical proficiency towards essential functions within a job. This functions as an effective screening technique intended to identify vulnerable workers who may be at greater risk of developing future musculoskeletal injuries. In this study, data mining approaches to estimate the proper work group for a pre-defined specialized job is used. In the first phase, the cluster analysis for the grouping the young workers according to their physical parameters was applied and according to the characteristic of each cluster, particularly with increasing handgrip strength values, the clusters were coined as very poor, poor, fair, good and excellent. In the second phase, supervised learning methods are utilized to classify the worker based on the anthropometric variables.

Keywords

Bayesian Networks, Cluster Analysis, Data Mining, Handgrip Strength, K-Means, Multilayer Perceptron, Musculoskeletal Disorder Anthropometry, Supervised Learning.

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