Key result
A virtual reality Exergame application engaged individuals in low-intensity exercises that increased heart rate, and a machine learning model predicted exercise activity level with 76.67% accuracy.
Why the study?
Existing applications to prevent work-related musculoskeletal disorders often lack immersivity or focus primarily on education rather than helping workers warm-up or stretch.
A VR exergame can engage workers in low-intensity physical activity that increases heart rate, and machine learning can predict exercise intensity with moderate accuracy.
May support VR exergames for low-intensity activity; leaves open clinical validation of ML prediction models.
This work presents a virtual reality (VR) Exergame application designed to prevent work-related musculoskeletal disorders (WMSDs). Moreover, to help adapt the tasks of the exergame, a machine learning model that predicts users’ exercise intensity level is presented. WMSDs are an important issue that can have a direct economic impact to an organization. Exercise and stretching is one method that can benefit workers and help prevent WMSDs. While several applications have been developed to prevent WMSDs, most of them suffer from a lack of immersivity or they just focus on education and not necessarily on helping workers warm-up or stretch. In light of this, an Exergame application that leverages VR and Depth-sensor technology to help provide users with an immersive first-person experience that engages them in physical activities is introduced in this work. The objective of the Exergame is to motivate users to perform full-body movements in order to pass through a series of obstacles. While in the game, users can visualize their motions by controlling the virtual avatar with their body movements. It is expected that this immersivity will motivate and encourage the users. Initial findings show the positive effects that the base exergame has on individuals’ motivation and physical activity. The results indicate that the application was able to engage individuals in low-intensity exercises that produced significant and consistent increases in their heart rate. Lastly, the results show that the machine learning model predicted users’ exercise activity level with an accuracy of 76.67%.
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Stranick et al. (2021) studied Work-related musculoskeletal disorders (WMSDs). Virtual reality (VR) Exergame application was evaluated on Exercise activity level prediction accuracy. A virtual reality Exergame application engaged individuals in low-intensity exercises that increased heart rate, and a machine learning model predicted exercise activity level with 76.67% accuracy.
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