MicroFluID
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Published:2022-09-06
Issue:3
Volume:6
Page:1-23
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ISSN:2474-9567
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Container-title:Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
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language:en
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Short-container-title:Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.
Author:
Sun Wei1, Chen Yuwen1, Chen Yanjun1, Zhang Xiaopeng2, Zhan Simon3, Li Yixin1, Wu Jiecheng1, Han Teng1, Mi Haipeng2, Wang Jingxian4, Tian Feng1, Yang Xing-Dong5
Affiliation:
1. Institute of Software, Chinese Academy of Sciences, Beijing, China 2. Tsinghua University, Beijing, China 3. University of California, Berkeley, Berkeley, California, United States 4. Carnegie Mellon University, Pennsylvania, Pittsburgh, United States 5. Simon Fraser University, British Columbia, Burnaby, Canada
Abstract
RFID has been widely used for activity and gesture recognition in emerging interaction paradigms given its low cost, lightweight, and pervasiveness. However, current learning-based approaches on RFID sensing require significant efforts in data collection, feature extraction, and model training. To save data processing effort, we present MicroFluID, a novel RFID artifact based on a multiple-chip structure and microfluidic switches, which informs the input state by directly reading variable ID information instead of retrieving primitive signals. Fabricated on flexible substrates, four types of microfluidic switch circuits are designed to respond to external physical events, including pressure, bend, temperature, and gravity. By default, chips are disconnected into the circuit owing to the reserved gaps in transmission line. While external input or status change occurs, conductive liquid floating in the microfluidics channels will fill the gap(s), creating a connection to certain chip(s). In prototyping the device, we conducted a series of simulations and experiments to explore the feasibility of the multi-chip tag design, key fabrication parameters, interaction performance, and users' perceptions.
Publisher
Association for Computing Machinery (ACM)
Subject
Computer Networks and Communications,Hardware and Architecture,Human-Computer Interaction
Reference49 articles.
1. R. Bhattacharyya C. Di Leo , C. Floerkemeier , S. Sarma , and L. Anand . 2010. RFID tag antenna based temperature sensing using shape memory polymer actuation . In SENSORS, 2010 IEEE. 2363--2368. https://doi.org/10.1109/ICSENS. 2010 .5690951 10.1109/ICSENS.2010.5690951 R. Bhattacharyya C. Di Leo, C. Floerkemeier, S. Sarma, and L. Anand. 2010. RFID tag antenna based temperature sensing using shape memory polymer actuation. In SENSORS, 2010 IEEE. 2363--2368. https://doi.org/10.1109/ICSENS.2010.5690951 2. Multi-Chip RFID Antenna Integrating Shape-Memory Alloys for Detection of Thermal Thresholds 3. A Cost-Effective UHF RFID Tag for Transmission of Generic Sensor Data in Wireless Sensor Networks 4. Enhanced UHF RFID Sensor-Tag 5. Kaixuan Chen , Dalin Zhang , Lina Yao , Bin Guo , Zhiwen Yu , and Yunhao Liu . 2021. Deep Learning for Sensor-Based Human Activity Recognition: Overview, Challenges, and Opportunities. ACM Comput. Surv. 54, 4 , Article 77 (May 2021 ), 40 pages. https://doi.org/10.1145/3447744 10.1145/3447744 Kaixuan Chen, Dalin Zhang, Lina Yao, Bin Guo, Zhiwen Yu, and Yunhao Liu. 2021. Deep Learning for Sensor-Based Human Activity Recognition: Overview, Challenges, and Opportunities. ACM Comput. Surv. 54, 4, Article 77 (May 2021), 40 pages. https://doi.org/10.1145/3447744
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