ABSTRACT
Smartphones have limited input vocabularies compared with PCs. In this paper, we propose a method for estimating the stylus angle of a passive brush from capacitive images. The technique allows us to expand the input vocabulary without complicating the smartphone or the stylus. We conducted experiments that showed the estimation error (MAE) was 6.63° for pitch and 5.97° for roll. Two applications were implemented to show the feasibility of the technique.
Supplemental Material
- Ke He, Yongjie Duan, Jianjiang Feng, and Jie Zhou. 2022. Estimating 3D Finger Angle via Fingerprint Image. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 6, 1, Article 14 (mar 2022), 22 pages. https://doi.org/10.1145/3517243Google ScholarDigital Library
- Sungjae Hwang, Andrea Bianchi, Myungwook Ahn, and Kwangyun Wohn. 2013. MagPen: Magnetically Driven Pen Interactions on and around Conventional Smartphones. In Proceedings of the 15th International Conference on Human-Computer Interaction with Mobile Devices and Services (Munich, Germany) (MobileHCI ’13). Association for Computing Machinery, New York, NY, USA, 412–415. https://doi.org/10.1145/2493190.2493194Google ScholarDigital Library
- Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980(2014).Google Scholar
- Kyosuke Kondo, Tsutomu Terada, and Masahiko Tsukamoto. 2019. A Pen-Grip Shaped Device for Estimating Writing Pressure and Altitude. In 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC), Vol. 2. 245–250. https://doi.org/10.1109/COMPSAC.2019.10214Google ScholarCross Ref
- Sven Kratz, Patrick Chiu, and Maribeth Back. 2013. PointPose: Finger Pose Estimation for Touch Input on Mobile Devices Using a Depth Sensor. In Proceedings of the 2013 ACM International Conference on Interactive Tabletops and Surfaces (St. Andrews, Scotland, United Kingdom) (ITS ’13). Association for Computing Machinery, New York, NY, USA, 223–230. https://doi.org/10.1145/2512349.2512824Google ScholarDigital Library
- Huy Viet Le, Thomas Kosch, Patrick Bader, Sven Mayer, and Niels Henze. 2018. PalmTouch: Using the Palm as an Additional Input Modality on Commodity Smartphones. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (Montreal QC, Canada) (CHI ’18). Association for Computing Machinery, New York, NY, USA, 1–13. https://doi.org/10.1145/3173574.3173934Google ScholarDigital Library
- Huy Viet Le, Sven Mayer, and Niels Henze. 2018. InfiniTouch: Finger-Aware Interaction on Fully Touch Sensitive Smartphones. In Proceedings of the 31st Annual ACM Symposium on User Interface Software and Technology(Berlin, Germany) (UIST ’18). Association for Computing Machinery, New York, NY, USA, 779–792. https://doi.org/10.1145/3242587.3242605Google ScholarDigital Library
- Huy Viet Le, Sven Mayer, and Niels Henze. 2019. Investigating the Feasibility of Finger Identification on Capacitive Touchscreens Using Deep Learning. In Proceedings of the 24th International Conference on Intelligent User Interfaces (Marina del Ray, California) (IUI ’19). Association for Computing Machinery, New York, NY, USA, 637–649. https://doi.org/10.1145/3301275.3302295Google ScholarDigital Library
- Fabrice Matulic, Riku Arakawa, Brian Vogel, and Daniel Vogel. 2020. PenSight: Enhanced Interaction with a Pen-Top Camera. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (Honolulu, HI, USA) (CHI ’20). Association for Computing Machinery, New York, NY, USA, 1–14. https://doi.org/10.1145/3313831.3376147Google ScholarDigital Library
- Sven Mayer, Huy Viet Le, and Niels Henze. 2017. Estimating the Finger Orientation on Capacitive Touchscreens Using Convolutional Neural Networks. In Proceedings of the 2017 ACM International Conference on Interactive Surfaces and Spaces (Brighton, United Kingdom) (ISS ’17). Association for Computing Machinery, New York, NY, USA, 220–229. https://doi.org/10.1145/3132272.3134130Google ScholarDigital Library
- Sven Mayer, Xiangyu Xu, and Chris Harrison. 2021. Super-Resolution Capacitive Touchscreens. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (Yokohama, Japan) (CHI ’21). Association for Computing Machinery, New York, NY, USA, Article 12, 10 pages. https://doi.org/10.1145/3411764.3445703Google ScholarDigital Library
- Martin Schmitz, Florian Müller, Max Mühlhäuser, Jan Riemann, and Huy Viet Viet Le. 2021. Itsy-Bits: Fabrication and Recognition of 3D-Printed Tangibles with Small Footprints on Capacitive Touchscreens. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (Yokohama, Japan) (CHI ’21). Association for Computing Machinery, New York, NY, USA, Article 419, 12 pages. https://doi.org/10.1145/3411764.3445502Google ScholarDigital Library
- Martin Schmitz, Jürgen Steimle, Jochen Huber, Niloofar Dezfuli, and Max Mühlhäuser. 2017. Flexibles: Deformation-Aware 3D-Printed Tangibles for Capacitive Touchscreens. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (Denver, Colorado, USA) (CHI ’17). Association for Computing Machinery, New York, NY, USA, 1001–1014. https://doi.org/10.1145/3025453.3025663Google ScholarDigital Library
- Robin Schweigert, Jan Leusmann, Simon Hagenmayer, Maximilian Weiß, Huy Viet Le, Sven Mayer, and Andreas Bulling. 2019. KnuckleTouch: Enabling Knuckle Gestures on Capacitive Touchscreens Using Deep Learning. In Proceedings of Mensch Und Computer 2019 (Hamburg, Germany) (MuC’19). Association for Computing Machinery, New York, NY, USA, 387–397. https://doi.org/10.1145/3340764.3340767Google ScholarDigital Library
- Peng Song, Xiaoqi Yan, Wooi Boon Goh, Alex Qiang Chen, and Chi-Wing Fu. 2016. Hand-Posture-Augmented Multitouch Interactions for Exploratory Visualization. In SIGGRAPH ASIA 2016 Technical Briefs (Macau) (SA ’16). Association for Computing Machinery, New York, NY, USA, Article 27, 4 pages. https://doi.org/10.1145/3005358.3005363Google ScholarDigital Library
- Gerard Wilkinson, Ahmed Kharrufa, Jonathan Hook, Bradley Pursglove, Gavin Wood, Hendrik Haeuser, Nils Y. Hammerla, Steve Hodges, and Patrick Olivier. 2016. Expressy: Using a Wrist-Worn Inertial Measurement Unit to Add Expressiveness to Touch-Based Interactions. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (San Jose, California, USA) (CHI ’16). Association for Computing Machinery, New York, NY, USA, 2832–2844. https://doi.org/10.1145/2858036.2858223Google ScholarDigital Library
Index Terms
- Estimation of Brush Type Passive Stylus Angles Using Capacitive Image
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