Northern Kentucky University

Poster Title

Gesture-based American Sign Language (ASL) Translation System

Grade Level at Time of Presentation

Senior

Major

Mechatronic Engineering Technology

2nd Grade Level at Time of Presentation

Senior

2nd Student Major

Mechatronic Engineering Technology

Institution 22-23

Northern Kentucky University

KY House District #

67

KY Senate District #

24

Department

Department of Physics, Geology and Engineering Technology

Abstract

According to the World Health Organization (WHO), over 5% of the world's population experiences severe hearing loss. Approximately 9 million people in the U.S. are either functionally deaf or have mild-to-severe hearing loss. In this research, we designed and implemented a translation interface which turns American Sign Language (ASL) gestures captured from a pair of soft robotic gloves into text and speech instantaneously.

We used a combination of flex sensors, tactile sensors, and accelerometers to recognize hand gestures and to record hand and fingers positions, movements, and orientations. The digitized captured gestures were then sent to our proposed translation interface wirelessly and were compared with the patterns stored in our dataset using a supervised Support Vector Machine (SVM) classification model. Once the captured gesture matched a predefined pattern, the associated letter, word, or phrase was shown on an embedded display, and the voice was generated by a text to speech conversion module.

This project aimed to develop an accessible and easy to use solution to help individuals who are deaf or have speech impairment problems to communicate directly to non‐signer people. These gloves can also be integrated with immersive learning technologies to enhance higher education and expand access to active learning opportunities for many underrepresented students.

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Gesture-based American Sign Language (ASL) Translation System

According to the World Health Organization (WHO), over 5% of the world's population experiences severe hearing loss. Approximately 9 million people in the U.S. are either functionally deaf or have mild-to-severe hearing loss. In this research, we designed and implemented a translation interface which turns American Sign Language (ASL) gestures captured from a pair of soft robotic gloves into text and speech instantaneously.

We used a combination of flex sensors, tactile sensors, and accelerometers to recognize hand gestures and to record hand and fingers positions, movements, and orientations. The digitized captured gestures were then sent to our proposed translation interface wirelessly and were compared with the patterns stored in our dataset using a supervised Support Vector Machine (SVM) classification model. Once the captured gesture matched a predefined pattern, the associated letter, word, or phrase was shown on an embedded display, and the voice was generated by a text to speech conversion module.

This project aimed to develop an accessible and easy to use solution to help individuals who are deaf or have speech impairment problems to communicate directly to non‐signer people. These gloves can also be integrated with immersive learning technologies to enhance higher education and expand access to active learning opportunities for many underrepresented students.