Toward Next-Generation Human–Computer Interface Based on Earables

We introduce a new type of human–computer interface—mobile acoustic field (MAF). MAF explores a variety of onboard sensors, such as speaker transducers, inertial measurement unit (IMU), and feedforward and feedback microphones, on earphones/earbuds to capture a wide range of acoustic activities both inside-out and outside-in. We anticipate that the exponential advancement of generative AI, powered by the sheer size of cloud computing resources, will enable personal agents to sense, understand, and further interact with this MAF, offering many exciting mobile services to users. In this article, we summarize our preliminary results, discuss the downstream applications, technical challenges, and our vision on this exciting research field.

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Publication Details

Journal
IEEE Pervasive Computing
Published
2024-10-01
DOI
https://doi.org/10.1109/mprv.2024.3500112
Citations
3
Primary Topic
Hand Gesture Recognition Systems
Type
article
Field-Weighted Citation Impact
1.12

Funders

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article

Toward Next-Generation Human–Computer Interface Based on Earables

Yongjie Yang, Longfei Shangguan, Tao Chen
3 citations
IEEE Pervasive Computing
Hand Gesture Recognition Systems
1.12
article

Toward Next-Generation Human–Computer Interface Based on Earables

Yongjie Yang, Longfei Shangguan, Tao Chen
article en
3 citations

Abstract

We introduce a new type of human–computer interface—mobile acoustic field (MAF). MAF explores a variety of onboard sensors, such as speaker transducers, inertial measurement unit (IMU), and feedforward and feedback microphones, on earphones/earbuds to capture a wide range of acoustic activities both inside-out and outside-in. We anticipate that the exponential advancement of generative AI, powered by the sheer size of cloud computing resources, will enable personal agents to sense, understand, and further interact with this MAF, offering many exciting mobile services to users. In this article, we summarize our preliminary results, discuss the downstream applications, technical challenges, and our vision on this exciting research field.

IEEE Pervasive ComputingVol. 23(4)
University of Pittsburgh (US)
National Science Foundation
Openalex Percentile: Top 20%
Hand Gesture Recognition Systems
1.12
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