Version 1
: Received: 18 June 2024 / Approved: 19 June 2024 / Online: 21 June 2024 (05:09:19 CEST)
How to cite:
Oveisi, E. Machine Learning Approaches to Analyze the Effect of Physiological Measurements and Manual Handling on Psychomotor Skills. Preprints2024, 2024061339. https://doi.org/10.20944/preprints202406.1339.v1
Oveisi, E. Machine Learning Approaches to Analyze the Effect of Physiological Measurements and Manual Handling on Psychomotor Skills. Preprints 2024, 2024061339. https://doi.org/10.20944/preprints202406.1339.v1
Oveisi, E. Machine Learning Approaches to Analyze the Effect of Physiological Measurements and Manual Handling on Psychomotor Skills. Preprints2024, 2024061339. https://doi.org/10.20944/preprints202406.1339.v1
APA Style
Oveisi, E. (2024). Machine Learning Approaches to Analyze the Effect of Physiological Measurements and Manual Handling on Psychomotor Skills. Preprints. https://doi.org/10.20944/preprints202406.1339.v1
Chicago/Turabian Style
Oveisi, E. 2024 "Machine Learning Approaches to Analyze the Effect of Physiological Measurements and Manual Handling on Psychomotor Skills" Preprints. https://doi.org/10.20944/preprints202406.1339.v1
Abstract
Background: This study focuses on different manual handling methods and their effect on psychomotor skills concerning physical measurements (height, weight, age) and pinch strength.
Objective: To assess the effects of three manual handling techniques on psychomotor skills and to establish the prediction of pinch strength by physical measurements.
Methods: Three manual handling methods were tested: handling with a 90-degree elbow angle, handling with one hand, and handling with both hands. Psychomotor skills were quantified through handgrip strength, dexterity tests, and perceived exertion scales. Linear and random forest regression models were used to predict pinch strength from physical measurements.
Results: Handling with a 90-degree elbow angle significantly increased perceived exertion and error rates while decreasing post-task handgrip strength. The one-hand method showed the least negative effect on psychomotor abilities. Weight emerged as the most influential predictor of pinch strength, followed by height and age. The random forest regression model outperformed the linear regression model, indicating its suitability for capturing complex non-linear relationships among variables. The Random Forest Regression model excelled in predicting weight with a high R² score of 0.78, showing strong predictive power. It was moderately effective for height with an R² score of 0.42. However, it performed poorly for age prediction, with an R² score of 0.01, indicating ineffective features for predicting age.
Conclusion: The results emphasize the importance of ergonomic interventions and training programs to optimize manual handling practices, aiming to reduce physical strain and enhance worker productivity and safety. Maintaining muscle strength is crucial in older age groups due to the decline in pinch strength with age. These findings can inform intervention studies and the modification of occupational health and safety practices. Future research should aim to validate these results across diverse populations and explore additional factors influencing handgrip strength.
Engineering, Safety, Risk, Reliability and Quality
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
