SpeciFingers

Huang, Zeyuan, Gao, Cangjun, Wang, Haiyan, Deng, Xiaoming, Lai, Yu-Kun ORCID: https://orcid.org/0000-0002-2094-5680, Ma, Cuixia, Qin, Sheng-feng, Liu, Yong-Jin and Wang, Hongan 2024. SpeciFingers. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 8 (1) , pp. 1-28. 10.1145/3643559

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Abstract

The inadequate use of finger properties has limited the input space of touch interaction. By leveraging the category of contacting fingers, finger-specific interaction is able to expand input vocabulary. However, accurate finger identification remains challenging, as it requires either additional sensors or limited sets of identifiable fingers to achieve ideal accuracy in previous works. We introduce SpeciFingers, a novel approach to identify fingers with the capacitive raw data on touchscreens. We apply a neural network of an encoder-decoder architecture, which captures the spatio-temporal features in capacitive image sequences. To assist users in recovering from misidentification, we propose a correction mechanism to replace the existing undo-redo process. Also, we present a design space of finger-specific interaction with example interaction techniques. In particular, we designed and implemented a use case of optimizing the performance in pointing on small targets. We evaluated our identification model and error correction mechanism in our use case.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	Association for Computing Machinery (ACM)
ISSN:	2474-9567
Date of First Compliant Deposit:	21 March 2024
Date of Acceptance:	11 January 2024
Last Modified:	25 Mar 2024 14:45
URI:	https://orca.cardiff.ac.uk/id/eprint/167411

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