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HeadFi II: Toward More Resilient Earable Computing Platform

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Longfei ShangguanAuthors Info & Claims

SenSys '22: Proceedings of the 20th ACM Conference on Embedded Networked Sensor Systems

Pages 827 - 828

https://doi.org/10.1145/3560905.3568092

Published: 24 January 2023 Publication History

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Abstract

Earables are embedded devices that can be placed in, on, or around the ear to sense human motions and physiological activities over an extended period of time. However, today's earable design principle heavily relies on dedicated sensors (e.g., accelerometer, gyroscope, proximity sensor), which inevitably adds cost, weight, and power consumption to earable devices, constituting a critical bottleneck in their wide adoption. Moreover, the tight coupling of sensors with onboard microcontrollers makes existing earables difficult to program, raising the barrier of entry to earable computing.

In this poster, we describe HeadFi II, a stand-alone earable computing platform that integrates sensing and computing into a tiny hardware device with low power and computation footprint. We describe the design guideline and technical challenges as well as potential solutions. We believe this project would open up a new dimension of cutting-edge research and exciting educational opportunities. The developed solutions will lead to considerable advancements in both low-power hardware designs and efficient earable sensing algorithms, lowering the barrier of entry to earable computing by providing the research community with a versatile earable platform.

References

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K.-J. Butkow, T. Dang, A. Ferlini, D. Ma, and C. Mascolo. Motion-resilient heart rate monitoring with in-ear microphones. arXiv preprint arXiv:2108.09393, 2021.

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F. Corinto, M. Forti, and L. Chua. Nonlinear circuits and systems with memristors. Nonlinear Dynamics and Analogue Computing via the Flux-Charge Analysis, 2020.

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X. Fan, L. Shangguan, S. Rupavatharam, Y. Zhang, J. Xiong, Y. Ma, and R. Howard. Headfi: bringing intelligence to all headphones. In MOBICOM, 2021.

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O. Heath and J. Russell. The wheatstone bridge porometer. Journal of Experimental Botany, 1951.

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D. Ma, A. Ferlini, and C. Mascolo. Oesense: employing occlusion effect for in-ear human sensing. In Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services, pages 175--187, 2021.

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Fan XThormundsson T(2023)Design Earable Sensing Systems: Perspectives and Lessons Learned from IndustryAdjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable Computing10.1145/3594739.3610673(342-345)Online publication date: 8-Oct-2023
https://dl.acm.org/doi/10.1145/3594739.3610673

Index Terms

HeadFi II: Toward More Resilient Earable Computing Platform
1. Computer systems organization
  1. Embedded and cyber-physical systems
  2. Real-time systems
2. Human-centered computing

Index terms have been assigned to the content through auto-classification.

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Published In

SenSys '22: Proceedings of the 20th ACM Conference on Embedded Networked Sensor Systems

November 2022

1280 pages

ISBN:9781450398862

DOI:10.1145/3560905

General Chairs:
Jeremy Gummeson
University of Massachusetts Amherst
,
Sunghoon Ivan Lee
University of Massachusetts Amherst
,
Program Chairs:
Jie Gao
Rutgers University
,
Guoliang Xing
The Chinese University of Hong Kong

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

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Published: 24 January 2023

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SenSys '22: The 20th ACM Conference on Embedded Networked Sensor Systems

November 6 - 9, 2022

Massachusetts, Boston

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SenSys '22 Paper Acceptance Rate 52 of 187 submissions, 28%;

Overall Acceptance Rate 174 of 867 submissions, 20%

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Fan XThormundsson T(2023)Design Earable Sensing Systems: Perspectives and Lessons Learned from IndustryAdjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable Computing10.1145/3594739.3610673(342-345)Online publication date: 8-Oct-2023
https://dl.acm.org/doi/10.1145/3594739.3610673

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HeadFi: bringing intelligence to all headphones

Loudspeaker virtualization–Part II: The inverse transducer model and the Direct-Inverse-Direct Chain

Obtaining Binaural Room Impulse Responses From B-Format Impulse Responses Using Frequency-Dependent Coherence Matching

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HeadFi: bringing intelligence to all headphones

Loudspeaker virtualization–Part II: The inverse transducer model and the Direct-Inverse-Direct Chain

Obtaining Binaural Room Impulse Responses From B-Format Impulse Responses Using Frequency-Dependent Coherence Matching

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