{'fraction_non_alphanumeric': 0.017889087656529516, 'fraction_numerical': 0.002385211687537269, 'mean_word_length': 5.124087591240876, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Adaptive Virtual Reality Exergame: Promoting Physical Activity Among Workers


 This work presents a Virtual Reality 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 engage 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%.