A system for predicting musculoskeletal disorders among dental students

Abstract

Objective: This study aimed to develop a system for predicting work-related musculoskeletal disorders (WMSD) among dental students.

Materials and methods: The system comprised 2 accelerometer sensors to register neck and upper back postures and movements, and software developed to collect and process the data. Hidden Markov models (HMMs) were used to predict the likelihood of WMSD in dental students by comparing their neck and upper back movement patterns with WMSD and non-WMSD HMMs learned from previous data. To evaluate the performance of the system, 16 participants were randomly assigned into a 2 × 2 crossover trial scheduled for each sequence of working: receiving feedback or no-feedback from the system. The primary outcome measure was the extension of the neck and upper back, before (pre-test) and after (posttest) receiving feedback or no-feedback from the system. The secondary outcome measure was the log likelihood of classifying the movements as WMSD.

Results and discussion: The results showed that in the group that received feedback, the extension of the neck in the y axis and of the upper back in the y axis decreased significantly (t test, p < .05) on the post-test.

Conclusion: The system for predicting and preventing WMSD aids the correction of the extension of the neck and upper back in the y axis.