Grip strength prediction for Malaysian industrial workers using artificial neural networks

a:2:{i:0;s:1082:"Data and model of grip strength is crucial in designing hand tools. Hand tools design that properly fit the user will help reduce carpal tunnel syndrome and cumulative trauma disorders (CTDs) in tasks that involve gripping in manual handling. The primary objective of this study is to obtain grip strength data and develop models to predict of grip strength of Malaysian industrial workers. The model was developed using artificial neural networks. Data that were collected from a survey on Malaysian industrial workers consist of 42 females (light industries), 72 males (light industries), and 32 males (heavy industries). Furthermore, this data was used to train the neural network. The data obtained were divided into two sets-training set and test set. By using a pairwise t-test method, the results indicated that there was no significant difference between actual measurements and predicted values at the alpha value of 0.05 (p > 0.05). It was found that the neural network predicts human grip strength accurately based on hand dimensions, age, wrist circumference and weight.";i:1;s:421:"Relevance to industry: The design of safe and comfortable hand tools is critical to avoid CTDs such as carpal tunnel syndrome. In Malaysian industries, many hand tools are not designed for Malaysian dimensions both in terms of strength and physical dimensions. In this paper, the parameters critical for gripping are made available for designing hand tools for manual handling. (c) 2005 Elsevier B.V. All rights reserved.";}