Semi-Supervised Learning Based Prediction of Musculoskeletal Disorder Risk

Document Type : Technical note


1 Mechanical Engineering Department, National Institute of Technology, Kurukshetra 136119, Haryana, INDIA

2 Civil Engineering Department, National Institute of Technology, Kurukshetra 136119, Haryana, INDIA


This study explores a semi-supervised classification approach using random forest as a base classifier to classify the low-back disorders (LBDs) risk associated with the industrial jobs. Semi-supervised classification approach uses unlabeled data together with the small number of labelled data to create a better classifier. The results obtained by the proposed approach are compared with those obtained by a backpropagation neural network. Comparison indicates an improved performance by the semi-supervised approach over the random forest classifier as well as neural network approach. Highest classification accuracy of 78.20% was achieved by the used semi-supervised approach with random forest as base classifier in comparison to an accuracy of 72.4% and 74.7% obtained by random forest and back propagation neural network approaches respectively. Thus results suggest that the proposed approach can successfully classify jobs into the low and high risk categories of low-back disorders based on lifting task characteristics.


Main Subjects

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