Musculoskeletal disorders prediagnosis by infrared thermography in CNC machinery operators: Regression models approaches

Abstract

BACKGROUND: Musculoskeletal System Disorders (MSDs) are a group of injuries that represent common occupational diseases and should be evaluated for prevention purposes because an increase has been observed due to the repetitive movements performed in the industry. This research was carried out in a manufacturing industry where metal parts are manufactured, and workers experience back and wrist pain. OBJECTIVE: To prediagnose Musculoskeletal System Disorders (MSDs) and examine the relationship between temperature, demographic, and physiological factors in workers through predictive models, contributing to MSD prevention. METHODS: Information from 36 operators was used to obtain vital signs and somatometry data, and thermograms of their hands in the dorsal, palmar, and back areas were collected and analyzed to determine the relationship between temperature and demographic and physiological factors. RESULTS: The ergonomic evaluations proved that the operators were at high risk owing to repetitive movements and postures adopted during work. Eighty-six percent of cases with injuries were identified using infrared thermograms, proving their high level of effectiveness. When studying the relationship between temperature behavior during recovery from repetitive activities and demographic and physiological factors, it was determined that age, dominant hand, respiratory frequency, and BMI were the most significant. CONCLUSIONS: Nine regression models were obtained, with coefficients of determination between 0.17 and 0.71. The significant factors for worker injuries were age, dominant hand, respiratory rate, and BMI. However, the sample size and variability in work activities should be extended to generalize the findings.