Abstract
Hearing-impaired communities around the world communicate via a sign language. The focus of this work is to develop an interpreting human-robot interaction system that could act as a sign language interpreter in public places. This paper presents an ongoing work, which aims to recognize fingerspelling gestures in real time. To this end, we utilize a deep learning method for classification of 33 gestures used for fingerspelling by the local deaf-mute community. In order to train and test the performance of the recognition system, we utilize previously collected dataset of motion RGB-D data of 33 manual gestures. After applying it to a deep learning method, we achieved an offline result of an average accuracy of 75% for a complete alphabet recognition. In real time, the result was only 24.72%. In addition, we integrated a form of auto-correction in order to perform spell-checking on the recognized letters. Among 35 tested words, four words were recognized correctly (11.4%). Finally, we conducted an exploratory study inviting ten deaf individuals to interact with our sign language interpreting robotic system.
| Original language | English |
|---|---|
| Title of host publication | HRI 2018 - Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction |
| Publisher | IEEE Computer Society |
| Pages | 141-142 |
| Number of pages | 2 |
| ISBN (Electronic) | 9781450356152 |
| DOIs | |
| Publication status | Published - Mar 1 2018 |
| Event | 13th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2018 - Chicago, United States Duration: Mar 5 2018 → Mar 8 2018 |
Conference
| Conference | 13th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2018 |
|---|---|
| Country | United States |
| City | Chicago |
| Period | 3/5/18 → 3/8/18 |
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Keywords
- human-robot interaction
- sign language recognition
- social robotics
ASJC Scopus subject areas
- Artificial Intelligence
- Human-Computer Interaction
- Electrical and Electronic Engineering
Cite this
Towards Interpreting Robotic System for Fingerspelling Recognition in Real Time. / Kalidolda, Nazerke; Sandygulova, Anara.
HRI 2018 - Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction. IEEE Computer Society, 2018. p. 141-142.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Towards Interpreting Robotic System for Fingerspelling Recognition in Real Time
AU - Kalidolda, Nazerke
AU - Sandygulova, Anara
PY - 2018/3/1
Y1 - 2018/3/1
N2 - Hearing-impaired communities around the world communicate via a sign language. The focus of this work is to develop an interpreting human-robot interaction system that could act as a sign language interpreter in public places. This paper presents an ongoing work, which aims to recognize fingerspelling gestures in real time. To this end, we utilize a deep learning method for classification of 33 gestures used for fingerspelling by the local deaf-mute community. In order to train and test the performance of the recognition system, we utilize previously collected dataset of motion RGB-D data of 33 manual gestures. After applying it to a deep learning method, we achieved an offline result of an average accuracy of 75% for a complete alphabet recognition. In real time, the result was only 24.72%. In addition, we integrated a form of auto-correction in order to perform spell-checking on the recognized letters. Among 35 tested words, four words were recognized correctly (11.4%). Finally, we conducted an exploratory study inviting ten deaf individuals to interact with our sign language interpreting robotic system.
AB - Hearing-impaired communities around the world communicate via a sign language. The focus of this work is to develop an interpreting human-robot interaction system that could act as a sign language interpreter in public places. This paper presents an ongoing work, which aims to recognize fingerspelling gestures in real time. To this end, we utilize a deep learning method for classification of 33 gestures used for fingerspelling by the local deaf-mute community. In order to train and test the performance of the recognition system, we utilize previously collected dataset of motion RGB-D data of 33 manual gestures. After applying it to a deep learning method, we achieved an offline result of an average accuracy of 75% for a complete alphabet recognition. In real time, the result was only 24.72%. In addition, we integrated a form of auto-correction in order to perform spell-checking on the recognized letters. Among 35 tested words, four words were recognized correctly (11.4%). Finally, we conducted an exploratory study inviting ten deaf individuals to interact with our sign language interpreting robotic system.
KW - human-robot interaction
KW - sign language recognition
KW - social robotics
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U2 - 10.1145/3173386.3177085
DO - 10.1145/3173386.3177085
M3 - Conference contribution
AN - SCOPUS:85045263087
SP - 141
EP - 142
BT - HRI 2018 - Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction
PB - IEEE Computer Society
ER -