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SonicASL: an acoustic-based sign language gesture recognizer using earphones

SonicASL: an acoustic-based sign language gesture recognizer using earphones
SonicASL: an acoustic-based sign language gesture recognizer using earphones

We propose SonicASL, a real-time gesture recognition system that can recognize sign language gestures on the fly, leveraging front-facing microphones and speakers added to commodity earphones worn by someone facing the person making the gestures. In a user study (N=8), we evaluate the recognition performance of various sign language gestures at both the word and sentence levels. Given 42 frequently used individual words and 30 meaningful sentences, SonicASL can achieve an accuracy of 93.8% and 90.6% for word-level and sentence-level recognition, respectively. The proposed system is tested in two real-world scenarios: indoor (apartment, office, and corridor) and outdoor (sidewalk) environments with pedestrians walking nearby. The results show that our system can provide users with an effective gesture recognition tool with high reliability against environmental factors such as ambient noises and nearby pedestrians.

Acoustic sensing, earphones, sign language gesture recognition
Jin, Yincheng
4ba702a8-e154-426c-87e5-fbe23b7d839e
Gao, Yang
f97a309e-6a1b-49a8-bab0-3cdd431c3ece
Zhu, Yanjun
5b6e2892-08f3-41cc-9180-ff7b084c433e
Wang, Wei
784abe65-25c3-4eae-add7-2af85a5d2da0
Li, Jiyang
ed6ad869-2dbe-4832-8940-2318aa714a1d
Choi, Seokmin
56d5ba4c-a7b0-48f1-98cf-6ffe30379ddc
Li, Zhangyu
36908d3a-66b5-4db1-818a-1e214826e69d
Chauhan, Jagmohan
831a12dc-6df9-40ea-8bb3-2c5da8882804
Dey, Anind K.
27f12bee-24de-4545-90f7-16cc17a25c47
Jin, Zhanpeng
2dc91f6a-d27f-449d-81ac-501c14c1f91f
Jin, Yincheng
4ba702a8-e154-426c-87e5-fbe23b7d839e
Gao, Yang
f97a309e-6a1b-49a8-bab0-3cdd431c3ece
Zhu, Yanjun
5b6e2892-08f3-41cc-9180-ff7b084c433e
Wang, Wei
784abe65-25c3-4eae-add7-2af85a5d2da0
Li, Jiyang
ed6ad869-2dbe-4832-8940-2318aa714a1d
Choi, Seokmin
56d5ba4c-a7b0-48f1-98cf-6ffe30379ddc
Li, Zhangyu
36908d3a-66b5-4db1-818a-1e214826e69d
Chauhan, Jagmohan
831a12dc-6df9-40ea-8bb3-2c5da8882804
Dey, Anind K.
27f12bee-24de-4545-90f7-16cc17a25c47
Jin, Zhanpeng
2dc91f6a-d27f-449d-81ac-501c14c1f91f

Jin, Yincheng, Gao, Yang, Zhu, Yanjun, Wang, Wei, Li, Jiyang, Choi, Seokmin, Li, Zhangyu, Chauhan, Jagmohan, Dey, Anind K. and Jin, Zhanpeng (2021) SonicASL: an acoustic-based sign language gesture recognizer using earphones. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 5 (2), [67]. (doi:10.1145/3463519).

Record type: Article

Abstract

We propose SonicASL, a real-time gesture recognition system that can recognize sign language gestures on the fly, leveraging front-facing microphones and speakers added to commodity earphones worn by someone facing the person making the gestures. In a user study (N=8), we evaluate the recognition performance of various sign language gestures at both the word and sentence levels. Given 42 frequently used individual words and 30 meaningful sentences, SonicASL can achieve an accuracy of 93.8% and 90.6% for word-level and sentence-level recognition, respectively. The proposed system is tested in two real-world scenarios: indoor (apartment, office, and corridor) and outdoor (sidewalk) environments with pedestrians walking nearby. The results show that our system can provide users with an effective gesture recognition tool with high reliability against environmental factors such as ambient noises and nearby pedestrians.

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More information

Published date: 24 June 2021
Keywords: Acoustic sensing, earphones, sign language gesture recognition

Identifiers

Local EPrints ID: 491166
URI: http://eprints.soton.ac.uk/id/eprint/491166
PURE UUID: a1d235f9-67fa-441b-8e05-fe5f722c0156

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Date deposited: 13 Jun 2024 17:08
Last modified: 13 Jun 2024 17:08

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Contributors

Author: Yincheng Jin
Author: Yang Gao
Author: Yanjun Zhu
Author: Wei Wang
Author: Jiyang Li
Author: Seokmin Choi
Author: Zhangyu Li
Author: Jagmohan Chauhan
Author: Anind K. Dey
Author: Zhanpeng Jin

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