Background: Despite advancing automation, employees in many industrial and service occupations still have to perform physically intensive work that may have negative effects on the health of the musculoskeletal system. For targeted preventive measures, precise knowledge of the work postures and movements performed is necessary.
Methods: Prototype smart work clothes equipped with 15 inertial sensors were used to record reference body postures of 20 subjects. These reference postures were used to create a software-based posture classifier according to the Ovako Working Posture Analysing System (OWAS) by means of an evolutionary training algorithm.
Results: A total of 111,275 posture shots were recorded and used for training the classifier. The results show that smart workwear, with the help of evolutionary trained software classifiers, is in principle capable of detecting harmful postures of its wearer. The detection rate of the evolutionary trained classifier ([Formula: see text] for the postures of the back, [Formula: see text] for the arms, and [Formula: see text] for the legs) outperforms that of a TensorFlow trained classifying neural network.
Conclusions: In principle, smart workwear - as prototypically shown in this paper - can be a helpful tool for assessing an individual's risk for work-related musculoskeletal disorders. Numerous potential sources of error have been identified that can affect the detection accuracy of software classifiers required for this purpose.
Keywords: Inertial sensors; Neuroevolution; Non-neutral postures; Work-related musculoskeletal disorders.
© 2022. The Author(s).