Predicting Mid-Air Interaction Movements and Fatigue Using Deep Reinforcement Learning

Author:

Cheema Noshaba1,Frey-Law Laura A.2,Naderi Kourosh3,Lehtinen Jaakko4,Slusallek Philipp5,Hämäläinen Perttu3

Affiliation:

1. Max-Planck Institute for Informatics & German Research Center for Artificial Intelligence (DFKI), Saarbrücken, Germany

2. University of Iowa, Iowa City, IA, USA

3. Aalto University, Espoo, Finland

4. Aalto University & NVIDIA Research, Espoo & Helsinki, Finland

5. Saarland University & German Research Center for Artificial Intelligence (DFKI), Saarbrücken, Germany

Funder

ITEA3

Academy of Finland

Publisher

ACM

Cited by 43 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. NICER: A New and Improved Consumed Endurance and Recovery Metric to Quantify Muscle Fatigue of Mid-Air Interactions;ACM Transactions on Graphics;2024-07-19

2. MARLUI: Multi-Agent Reinforcement Learning for Adaptive Point-and-Click UIs;Proceedings of the ACM on Human-Computer Interaction;2024-06-17

3. Quantifying Wrist-Aiming Habits with A Dual-Sensor Mouse: Implications for Player Performance and Workload;Proceedings of the CHI Conference on Human Factors in Computing Systems;2024-05-11

4. Metrics of Motor Learning for Analyzing Movement Mapping in Virtual Reality;Proceedings of the CHI Conference on Human Factors in Computing Systems;2024-05-11

5. Real-time 3D Target Inference via Biomechanical Simulation;Proceedings of the CHI Conference on Human Factors in Computing Systems;2024-05-11

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