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Abstract
Selecting a target in a 3D environment is often challenging, especially with small/distant targets or when sensor noise is high. To facilitate selection, target-inference methods must be accurate, fast, and account for noise and motor variability. However, traditional data-free approaches fall short in accuracy since they ignore variability. While data-driven solutions achieve higher accuracy, they rely on extensive human datasets so prove costly, time-consuming, and transfer poorly. In this paper, we propose a novel approach that leverages biomechanical simulation to produce synthetic motion data, capturing a variety of movement-related factors, such as limb configurations and motor noise. Then, an inference model is trained with only the simulated data. Our simulation-based approach improves transfer and lowers cost; variety-rich data can be produced in large quantities for different scenarios. We empirically demonstrate that our method matches the accuracy of human-data-driven approaches using data from seven users. When deployed, the method accurately infers intended targets in challenging 3D pointing conditions within 5–10 milliseconds, reducing users’ target-selection error by 71% and completion time by 35%.
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 | 18 |
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
- target inference
- Target selection
- biomechanical simulation
- amortized inference
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Subjective Functions: Subjective Functions
Oulasvirta, A. (Principal investigator)
01/09/2023 → 31/08/2027
Project: RCF Other
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HEALED/Kaski S.: Human-steered next-generation machine learning for reviving drug design (HEALED)
Kaski, S. (Principal investigator)
01/09/2021 → 31/08/2025
Project: RCF Academy Project
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MAMAA /Kaski S.: Maximally Autonomous AI Assistant/Kaski S.
Kaski, S. (Principal investigator)
01/01/2022 → 31/12/2024
Project: RCF Academy Project targeted call