Cited by 58Hand Gesture Recognition SystemsInteractive and Immersive DisplaysBluetooth and Wireless Communication Technologies

BLEselect: Gestural IoT Device Selection via Bluetooth Angle of Arrival Estimation from Smart Glasses

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Zhang, Tengxiang, Lan, Zitong, Xu, Chenren, Li, Yanrong, Chen, Yiqiang·2022·Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Abstract

Spontaneous selection of IoT devices from the head-mounted device is key for user-centered pervasive interaction. BLEselect enables users to select an unmodified Bluetooth 5.1 compatible IoT device by nodding at, pointing at, or drawing a circle in the air around it. We designed a compact antenna array that fits on a pair of smart glasses to estimate the Angle of Arrival (AoA) of IoT and wrist-worn devices' advertising signals. We then developed a sensing pipeline that supports all three selection gestures with lightweight machine learning models, which are trained in real-time for both hand gestures. Extensive characterizations and evaluations show that our system is accurate, natural, low-power, and privacy-preserving. Despite the small effective size of the antenna array, our system achieves a higher than 90% selection accuracy within a 3 meters distance in front of the user. In a user study that mimics real-life usage cases, the overall selection accuracy is 96.7% for a diverse set of 22 participants in terms of age, technology savviness, and body structures.

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  1. [3][3] Abdul Rafey Aftab, Michael von der Beeck, and Michael Feld. 2020. You Have a Point There: Object Selection Inside an Automobile Using Gaze, Head Pose and Finger Pointing. In Proceedings of the 2020 International Conference on Multimodal Interaction (ICMI '20) . Association for Computing Machinery, New York, NY, USA, 595-603. https://doi.org/10/gk88qb -
  2. [6][6] Md Tanvir Islam Aumi, Sidhant Gupta, Mayank Goel, Eric Larson, and Shwetak Patel. 2013. DopLink: Using the Doppler Effect for Multi-device Interaction. In Proceedings of the 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp '13) . ACM, New York, NY, USA, 583-586. https://doi.org/10/gfsvhm -
  3. [10][10] Kaifei Chen, Jonathan Fürst, John Kolb, Hyung-Sin Kim, Xin Jin, David E. Culler, and Randy H. Katz. 2018. SnapLink: Fast and Accurate Vision-Based Appliance Control in Large Commercial Buildings. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 1, 4 (Jan. 2018), 129:1-129:27. https://doi.org/10/gfhwdh -
  4. [13][13] Adrian A. de Freitas, Michael Nebeling, Xiang 'Anthony' Chen, Junrui Yang, Akshaye Shreenithi Kirupa Karthikeyan Ranithangam, and Anind K. Dey. 2016. Snap-To-It: A User-Inspired Platform for Opportunistic Device Interactions. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (CHI '16) . ACM, New York, NY, USA, 5909-5920. https://doi.org/10/gg4nkk -
  5. [19][19] Kyu-Han Kim. 2019. When IoT met Augmented Reality: Visualizing the Source of the Wireless Signal in AR View. In Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services . ACM, Seoul Republic of Korea, 117-129. https://doi.org/10/gkfcrs -
  6. [31][31] Chunyi Peng, Guobin Shen, Yongguang Zhang, and Songwu Lu. 2009. Point&Connect: Intention-based Device Pairing for Mobile Phone Users. In Proceedings of the 7th International Conference on Mobile Systems, Applications, and Services (MobiSys '09) . ACM, New York, NY, USA, 137-150. https://doi.org/10/b6rcdg -
  7. [35][35] Elahe Soltanaghaei, Adwait Dongare, Akarsh Prabhakara, Swarun Kumar, Anthony Rowe, and Kamin Whitehouse. 2021. TagFi: Locating Ultra-Low Power WiFi Tags Using Unmodified WiFi Infrastructure. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 5, 1 (March 2021), 1-29. https://doi.org/10/gjnjgp -
  8. [36][36] Elahe Soltanaghaei, Akarsh Prabhakara, Artur Balanuta, Matthew Anderson, Jan M. Rabaey, Swarun Kumar, and Anthony Rowe. 2021. Millimetro: mmWave retro-reflective tags for accurate, long range localization. In Proceedings of the 27th Annual International Conference on Mobile Computing and Networking . ACM, New Orleans Louisiana, 69-82. https://doi.org/10/gjg3rb -
  9. [37][37] Zheng Sun, Aveek Purohit, Raja Bose, and Pei Zhang. 2013. Spartacus: Spatially-aware Interaction for Mobile Devices Through Energy-efficient Audio Sensing. In Proceeding of the 11th Annual International Conference on Mobile Systems, Applications, and Services (MobiSys '13) . ACM, New York, NY, USA, 263-276. https://doi.org/10/gk5cwm -
  10. [44][44] Jie Xiong and Kyle Jamieson. 2013. Arraytrack: A fine-grained indoor location system. In 10th ${$USENIX$}$ Symposium on Networked Systems Design and Implementation (${$NSDI$}$ 13) . 71-84. -
  11. [48][48] Ben Zhang, Yu-Hsiang Chen, Claire Tuna, Achal Dave, Yang Li, Edward Lee, and Björn Hartmann. 2014. HOBS: Head Orientation-based Selection in Physical Spaces. In Proceedings of the 2Nd ACM Symposium on Spatial User Interaction (SUI '14) . ACM, New York, NY, USA, 17-25. https://doi.org/10/gk5cwk -
  12. [50][50] Tengxiang Zhang, Xin Yi, Ruolin Wang, Yuntao Wang, Chun Yu, Yiqin Lu, and Yuanchun Shi. 2018. Tap-to-Pair: Associating Wireless Devices with Synchronous Tapping. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 2, 4 (Dec. 2018), 201:1-201:21. https: //doi.org/10/gg7k4k -

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Authors
Zhang, T., Lan, Z., Xu, C., Li, Y., & Chen, Y.
Affiliations
  • Chinese Academy of Sciences (CN)
  • Southeast University (CN)
  • Peking University (CN)
Venue
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
Published
2022-12-21
Volume
6
Issue
4
Pages
1-28
ISSN
2474-9567 (ISSN-L)
OpenAlex
W4319837251
Cited by
58
References
44
Citations by year
2026: 8 · 2025: 20 · 2024: 15 · 2023: 12 · 2022: 3
FWCI
6.18
Type
journal article
Language
en
Open access
Locations
Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies (publishedVersion)
Funders
  • National Natural Science Foundation of China (No. 62022005, 62272010 and 62061146001)
  • Young Scientists Fund of the National Natural Science Foundation of China (No.62102401)
  • National Key Research and Development Plan (2019YFB1404703)
Topics
  • Physical Sciences › Computer Science › Human-Computer Interaction › Hand Gesture Recognition Systems
  • Physical Sciences › Computer Science › Human-Computer Interaction › Interactive and Immersive Displays
  • Physical Sciences › Computer Science › Computer Networks and Communications › Bluetooth and Wireless Communication Technologies
Keywords
Bluetooth Low Energy, head-mounted devices, smart glasses, angle of arrival estimation, antenna arrays, hand gesture recognition, real-time machine learning, privacy-preserving interaction

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