SmartASL

Author:

Jin Yincheng1ORCID,Zhang Shibo2ORCID,Gao Yang3ORCID,Xu Xuhai4ORCID,Choi Seokmin5ORCID,Li Zhengxiong6ORCID,Adler Henry J.7ORCID,Jin Zhanpeng8ORCID

Affiliation:

1. University at Buffalo, Department of Computer Science and Engineering, Buffalo, NY, USA

2. HP Inc., USA

3. East China Normal University, Department of Computer Science, China

4. University of Washington, Information School, Seattle, Washington, USA

5. University at Buffalo, Department of Computer Science and Engineering, USA

6. University of Colorado Denver, USA

7. University at Buffalo, Department of Communicative Disorders and Sciences, USA

8. South China University of Technology, School of Future Technology, China and University at Buffalo, Department of Computer Science and Engineering, USA

Abstract

Sign language builds up an important bridge between the d/Deaf and hard-of-hearing (DHH) and hearing people. Regrettably, most hearing people face challenges in comprehending sign language, necessitating sign language translation. However, state-of-the-art wearable-based techniques mainly concentrate on recognizing manual markers (e.g., hand gestures), while frequently overlooking non-manual markers, such as negative head shaking, question markers, and mouthing. This oversight results in the loss of substantial grammatical and semantic information in sign language. To address this limitation, we introduce SmartASL, a novel proof-of-concept system that can 1) recognize both manual and non-manual markers simultaneously using a combination of earbuds and a wrist-worn IMU, and 2) translate the recognized American Sign Language (ASL) glosses into spoken language. Our experiments demonstrate the SmartASL system's significant potential to accurately recognize the manual and non-manual markers in ASL, effectively bridging the communication gaps between ASL signers and hearing people using commercially available devices.

Funder

Shenzhen Holdfound Foundation

Guangdong Provincial Key Laboratory of Human Digital Twin

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Networks and Communications,Hardware and Architecture,Human-Computer Interaction

Reference62 articles.

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2. Ashwin Ahuja Andrea Ferlini and Cecilia Mascolo. 2021. PilotEar: Enabling In-ear Inertial Navigation. In Adjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2021 ACM International Symposium on Wearable Computers. 139--145. Ashwin Ahuja Andrea Ferlini and Cecilia Mascolo. 2021. PilotEar: Enabling In-ear Inertial Navigation. In Adjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2021 ACM International Symposium on Wearable Computers. 139--145.

3. Facial expression recognition using ear canal transfer function

4. EMG study of the anterior, superior and posterior auricular muscles in man

5. Hongliang Bi and Jiajia Liu . 2022. CSEar: Meta-learning for Head Gesture Recognition Using Earphones in Internet of Healthcare Things . IEEE Internet of Things Journal ( 2022 ). Hongliang Bi and Jiajia Liu. 2022. CSEar: Meta-learning for Head Gesture Recognition Using Earphones in Internet of Healthcare Things. IEEE Internet of Things Journal (2022).

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. SignRing;Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies;2023-09-27

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