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%.
Original language | English |
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Title of host publication | CHI '24: Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems |
Editors | Florian Floyd Mueller, Penny Kyburz, Julie R. Williamson, Corina Sas, Max L. Wilson, Phoebe Toups Dugas, Irina Shklovski |
Publisher | ACM |
Number of pages | 38 |
ISBN (Electronic) | 979-8-4007-0330-0 |
DOIs | |
Publication status | Published - 11 May 2024 |
MoE publication type | A4 Conference publication |
Event | ACM SIGCHI Annual Conference on Human Factors in Computing Systems - Honolulu, USA, Honolulu, United States Duration: 11 May 2024 → 16 May 2024 https://chi2024.acm.org/ |
Conference
Conference | ACM SIGCHI Annual Conference on Human Factors in Computing Systems |
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Abbreviated title | ACM CHI |
Country/Territory | United States |
City | Honolulu |
Period | 11/05/2024 → 16/05/2024 |
Internet address |
Keywords
- meta-learning
- target selection
- wrist-based interaction
- human-in-the-loop optimization
- meta-Bayesian optimization
- Bayesian optimization
- adaptive interface
- calibration