Locability: An Ability-Based Ranking Model for Virtual Reality Locomotion Techniques
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866912632626216960 |
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| author | Franz, Rachel L. Wobbrock, Jacob O. |
| author_facet | Franz, Rachel L. Wobbrock, Jacob O. |
| contents | There are over a hundred virtual reality (VR) locomotion techniques that exist today, with new ones being designed as VR technology evolves. The different ways of controlling locomotion techniques (e.g., gestures, button inputs, body movements), along with the diversity of upper-body motor impairments, can make it difficult for a user to know which locomotion technique is best suited to their particular abilities. Moreover, trial-and-error can be difficult, time-consuming, and costly. Using machine learning techniques and data from 20 people with and without upper-body motor impairments, we developed a modeling approach to predict a ranked list of a user's fastest techniques based on questionnaire and interaction data. We found that a user's fastest technique could be predicted based on interaction data with 92% accuracy and that predicted locomotion times were within 12% of observed times. The model we trained could also rank six locomotion techniques based on speed with 61% accuracy and that predictions were within 8% of observed times. Our findings contribute to growing research in VR accessibility by taking an ability-based design approach to adapt systems to users' abilities. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_05679 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Locability: An Ability-Based Ranking Model for Virtual Reality Locomotion Techniques Franz, Rachel L. Wobbrock, Jacob O. Human-Computer Interaction There are over a hundred virtual reality (VR) locomotion techniques that exist today, with new ones being designed as VR technology evolves. The different ways of controlling locomotion techniques (e.g., gestures, button inputs, body movements), along with the diversity of upper-body motor impairments, can make it difficult for a user to know which locomotion technique is best suited to their particular abilities. Moreover, trial-and-error can be difficult, time-consuming, and costly. Using machine learning techniques and data from 20 people with and without upper-body motor impairments, we developed a modeling approach to predict a ranked list of a user's fastest techniques based on questionnaire and interaction data. We found that a user's fastest technique could be predicted based on interaction data with 92% accuracy and that predicted locomotion times were within 12% of observed times. The model we trained could also rank six locomotion techniques based on speed with 61% accuracy and that predictions were within 8% of observed times. Our findings contribute to growing research in VR accessibility by taking an ability-based design approach to adapt systems to users' abilities. |
| title | Locability: An Ability-Based Ranking Model for Virtual Reality Locomotion Techniques |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2510.05679 |