WiReSens Toolkit: An Open-source Platform towards Accessible Wireless Tactile Sensing
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3731459.3773323.pdf
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Author(s) • • • • • •
Murphy, Devin
Zhu, Junyi
Gadre, Akshay
Torralba, Antonio
Liang, Paul Pu
Matusik, Wojciech
Luo, Yiyue
Date Issued
March 7, 2026
Publisher
ACM|Twentieth International Conference on Tangible, Embedded, and Embodied Interaction
Citation
Devin Murphy, Junyi Zhu, Akshay Gadre, Antonio Torralba, Paul Pu Liang, Wojciech Matusik, and Yiyue Luo. 2026. WiReSens Toolkit: An Open-source Platform towards Accessible Wireless Tactile Sensing. In Proceedings of the Twentieth International Conference on Tangible, Embedded, and Embodied Interaction (TEI '26). Association for Computing Machinery, New York, NY, USA, Article 27, 1–15.
Version
Final published version
Abstract
Past research has widely explored the design and fabrication of resistive matrix-based tactile sensors for creating touch-sensitive devices. However, real-world deployment of resistive tactile sensing systems remains difficult for individuals with limited prior experience in embedded sensing due to challenges of portability, adaptivity, and efficiency. We introduce the WiReSens Toolkit, an accessible, open-source platform to bridge this gap. Central to our approach is adaptive hardware for interfacing with resistive sensors and a web-based GUI that streamlines access to advanced features for building scalable tactile sensing systems, including multi-device programming and wireless visualization across three communication protocols, autocalibration for adaptive sensitivity, and intermittent data transmission for low-power use. We validated the toolkit’s usability through a user study with 11 novice participants, who, on average, configured a tactile sensor with over 95% accuracy in under five minutes, calibrated sensors 10× faster than baseline methods, and showed improved sense-making of tactile data.
Description
TEI ’26, Chicago, IL, USA
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Media Laboratory
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Creative Commons Attribution-NonCommercial-NoDerivatives
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DOI of Published Version
https://doi.org/10.1145/3731459.3773323