A Meta-Bayesian Approach for Rapid Online Parametric Optimization for Wrist-based Interactions
dc.contributor | Aalto-yliopisto | fi |
dc.contributor | Aalto University | en |
dc.contributor.author | Liao, Yi-Chi | en_US |
dc.contributor.author | Desai, Ruta | en_US |
dc.contributor.author | Pierce, Alex M. | en_US |
dc.contributor.author | Taylor, Krista E. | en_US |
dc.contributor.author | Benko, Hrvoje | en_US |
dc.contributor.author | Jonker, Tanya R. | en_US |
dc.contributor.author | Gupta, Aakar | en_US |
dc.contributor.department | Department of Information and Communications Engineering | en |
dc.contributor.editor | Mueller, Florian Floyd | en_US |
dc.contributor.editor | Kyburz, Penny | en_US |
dc.contributor.editor | Williamson, Julie R. | en_US |
dc.contributor.editor | Sas, Corina | en_US |
dc.contributor.editor | Wilson, Max L. | en_US |
dc.contributor.editor | Toups Dugas, Phoebe | en_US |
dc.contributor.editor | Shklovski, Irina | en_US |
dc.contributor.groupauthor | User Interfaces | en |
dc.contributor.organization | Meta AI Research | en_US |
dc.contributor.organization | Meta Reality Labs | en_US |
dc.date.accessioned | 2024-05-22T05:51:01Z | |
dc.date.available | 2024-05-22T05:51:01Z | |
dc.date.issued | 2024-05-11 | en_US |
dc.description.abstract | Wrist-based input often requires tuning parameter settings in correspondence to between-user and between-session differences, such as variations in hand anatomy, wearing position, posture, etc. Traditionally, users either work with predefined parameter values not optimized for individuals or undergo time-consuming calibration processes. We propose an online Bayesian Optimization (BO)-based method for rapidly determining the user-specific optimal settings of wrist-based pointing. Specifically, we develop a meta-Bayesian optimization (meta-BO) method, differing from traditional human-in-the-loop BO: By incorporating meta-learning of prior optimization data from a user population with BO, meta-BO enables rapid calibration of parameters for new users with a handful of trials. We evaluate our method with two representative and distinct wrist-based interactions: absolute and relative pointing. On a weighted-sum metric that consists of completion time, aiming error, and trajectory quality, meta-BO improves absolute pointing performance by 22.92% and 21.35% compared to BO and manual calibration, and improves relative pointing performance by 25.43% and 13.60%. | en |
dc.description.version | Peer reviewed | en |
dc.format.extent | 38 | |
dc.format.mimetype | application/pdf | en_US |
dc.identifier.citation | Liao, Y-C, Desai, R, Pierce, A M, Taylor, K E, Benko, H, Jonker, T R & Gupta, A 2024, A Meta-Bayesian Approach for Rapid Online Parametric Optimization for Wrist-based Interactions . in F F Mueller, P Kyburz, J R Williamson, C Sas, M L Wilson, P Toups Dugas & I Shklovski (eds), CHI '24: Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems ., 410, ACM, ACM SIGCHI Annual Conference on Human Factors in Computing Systems, Honolulu, Hawaii, United States, 11/05/2024 . https://doi.org/10.1145/3613904.3642071 | en |
dc.identifier.doi | 10.1145/3613904.3642071 | en_US |
dc.identifier.isbn | 979-8-4007-0330-0 | |
dc.identifier.other | PURE UUID: 5d32137c-e16f-4a9b-8eed-220f0f3b3790 | en_US |
dc.identifier.other | PURE ITEMURL: https://research.aalto.fi/en/publications/5d32137c-e16f-4a9b-8eed-220f0f3b3790 | en_US |
dc.identifier.other | PURE LINK: http://www.scopus.com/inward/record.url?scp=85194880854&partnerID=8YFLogxK | en_US |
dc.identifier.other | PURE FILEURL: https://research.aalto.fi/files/146008506/3613904.3642071.pdf | en_US |
dc.identifier.uri | https://aaltodoc.aalto.fi/handle/123456789/127906 | |
dc.identifier.urn | URN:NBN:fi:aalto-202405223511 | |
dc.language.iso | en | en |
dc.relation.ispartof | CHI '24: Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems | |
dc.relation.ispartof | ACM SIGCHI Annual Conference on Human Factors in Computing Systems | en |
dc.rights | openAccess | en |
dc.subject.keyword | meta-learning | en_US |
dc.subject.keyword | target selection | en_US |
dc.subject.keyword | wrist-based interaction | en_US |
dc.subject.keyword | human-in-the-loop optimization | en_US |
dc.subject.keyword | meta-Bayesian optimization | en_US |
dc.subject.keyword | Bayesian optimization | en_US |
dc.subject.keyword | adaptive interface | en_US |
dc.subject.keyword | calibration | en_US |
dc.title | A Meta-Bayesian Approach for Rapid Online Parametric Optimization for Wrist-based Interactions | en |
dc.type | A4 Artikkeli konferenssijulkaisussa | fi |
dc.type.version | publishedVersion |