A Real-Time Intelligent System Based on Machine-Learning Methods for Improving Communication in Sign Language
In this article, we introduce a cost-effective and real-time intelligent system tailored to Pakistan sign language (PSL) recognition, aimed at facilitating communication for hearing-impaired individuals. The system utilizes a specialized glove equipped with flex sensors and an MPU-6050 device to cap...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10839384/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832087992796184576 |
|---|---|
| author | Victor Leiva Muhammad Zia Ur Rahman Muhammad Azeem Akbar Cecilia Castro Mauricio Huerta Muhammad Tanveer Riaz |
| author_facet | Victor Leiva Muhammad Zia Ur Rahman Muhammad Azeem Akbar Cecilia Castro Mauricio Huerta Muhammad Tanveer Riaz |
| author_sort | Victor Leiva |
| collection | DOAJ |
| description | In this article, we introduce a cost-effective and real-time intelligent system tailored to Pakistan sign language (PSL) recognition, aimed at facilitating communication for hearing-impaired individuals. The system utilizes a specialized glove equipped with flex sensors and an MPU-6050 device to capture finger movements and hand orientation in a three-dimensional space. A dataset comprising ten unique PSL signs, each performed by five participants for a total of 5000 samples, was used to train machine learning classifiers. These signs involve single-hand and single-movement gestures, optimizing the system for real-time PSL recognition. Machine learning classifiers, including decision trees, k-nearest neighbors, and support vector machines, achieved accuracy levels of 96%, 96.5%, and 97%, respectively. While direct quantitative comparisons with state-of-the-art systems are limited due to the uniqueness of PSL, we discuss our system in the context of recent advancements in sign language recognition. Real-time testing underscores the system’s practical applicability and portability, demonstrating its potential for deployment in resource-constrained settings as an accessible initial step toward more comprehensive PSL recognition solutions. |
| format | Article |
| id | doaj-art-58deb22009bb42919f44def06c46a648 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-58deb22009bb42919f44def06c46a6482025-02-06T00:00:42ZengIEEEIEEE Access2169-35362025-01-0113220552207310.1109/ACCESS.2025.352902510839384A Real-Time Intelligent System Based on Machine-Learning Methods for Improving Communication in Sign LanguageVictor Leiva0https://orcid.org/0000-0003-4755-3270Muhammad Zia Ur Rahman1Muhammad Azeem Akbar2https://orcid.org/0000-0002-4906-6495Cecilia Castro3https://orcid.org/0000-0001-9897-8186Mauricio Huerta4https://orcid.org/0000-0002-0985-1907Muhammad Tanveer Riaz5https://orcid.org/0000-0002-4391-9821School of Industrial Engineering, Pontificia Universidad Católica de Valparaíso, Valparaíso, ChileDepartment of Mechanical, Mechatronics, and Manufacturing Engineering, University of Engineering and Technology Lahore, Faisalabad Campus, Faisalabad, PakistanDepartment of Information Technology, Lappeenranta University of Technology, Lappeenranta, FinlandCentre of Mathematics, Universidade do Minho, Braga, PortugalDepartment of Economics and Management, Universidad Católica del Maule, Talca, ChileDepartment of Mechanical, Mechatronics, and Manufacturing Engineering, University of Engineering and Technology Lahore, Faisalabad Campus, Faisalabad, PakistanIn this article, we introduce a cost-effective and real-time intelligent system tailored to Pakistan sign language (PSL) recognition, aimed at facilitating communication for hearing-impaired individuals. The system utilizes a specialized glove equipped with flex sensors and an MPU-6050 device to capture finger movements and hand orientation in a three-dimensional space. A dataset comprising ten unique PSL signs, each performed by five participants for a total of 5000 samples, was used to train machine learning classifiers. These signs involve single-hand and single-movement gestures, optimizing the system for real-time PSL recognition. Machine learning classifiers, including decision trees, k-nearest neighbors, and support vector machines, achieved accuracy levels of 96%, 96.5%, and 97%, respectively. While direct quantitative comparisons with state-of-the-art systems are limited due to the uniqueness of PSL, we discuss our system in the context of recent advancements in sign language recognition. Real-time testing underscores the system’s practical applicability and portability, demonstrating its potential for deployment in resource-constrained settings as an accessible initial step toward more comprehensive PSL recognition solutions.https://ieeexplore.ieee.org/document/10839384/Assistive technologyflex sensor gloveKalman filterMPU-6050 deviceRaspberry Pi 3B microcontrollerreal-time processing |
| spellingShingle | Victor Leiva Muhammad Zia Ur Rahman Muhammad Azeem Akbar Cecilia Castro Mauricio Huerta Muhammad Tanveer Riaz A Real-Time Intelligent System Based on Machine-Learning Methods for Improving Communication in Sign Language IEEE Access Assistive technology flex sensor glove Kalman filter MPU-6050 device Raspberry Pi 3B microcontroller real-time processing |
| title | A Real-Time Intelligent System Based on Machine-Learning Methods for Improving Communication in Sign Language |
| title_full | A Real-Time Intelligent System Based on Machine-Learning Methods for Improving Communication in Sign Language |
| title_fullStr | A Real-Time Intelligent System Based on Machine-Learning Methods for Improving Communication in Sign Language |
| title_full_unstemmed | A Real-Time Intelligent System Based on Machine-Learning Methods for Improving Communication in Sign Language |
| title_short | A Real-Time Intelligent System Based on Machine-Learning Methods for Improving Communication in Sign Language |
| title_sort | real time intelligent system based on machine learning methods for improving communication in sign language |
| topic | Assistive technology flex sensor glove Kalman filter MPU-6050 device Raspberry Pi 3B microcontroller real-time processing |
| url | https://ieeexplore.ieee.org/document/10839384/ |
| work_keys_str_mv | AT victorleiva arealtimeintelligentsystembasedonmachinelearningmethodsforimprovingcommunicationinsignlanguage AT muhammadziaurrahman arealtimeintelligentsystembasedonmachinelearningmethodsforimprovingcommunicationinsignlanguage AT muhammadazeemakbar arealtimeintelligentsystembasedonmachinelearningmethodsforimprovingcommunicationinsignlanguage AT ceciliacastro arealtimeintelligentsystembasedonmachinelearningmethodsforimprovingcommunicationinsignlanguage AT mauriciohuerta arealtimeintelligentsystembasedonmachinelearningmethodsforimprovingcommunicationinsignlanguage AT muhammadtanveerriaz arealtimeintelligentsystembasedonmachinelearningmethodsforimprovingcommunicationinsignlanguage AT victorleiva realtimeintelligentsystembasedonmachinelearningmethodsforimprovingcommunicationinsignlanguage AT muhammadziaurrahman realtimeintelligentsystembasedonmachinelearningmethodsforimprovingcommunicationinsignlanguage AT muhammadazeemakbar realtimeintelligentsystembasedonmachinelearningmethodsforimprovingcommunicationinsignlanguage AT ceciliacastro realtimeintelligentsystembasedonmachinelearningmethodsforimprovingcommunicationinsignlanguage AT mauriciohuerta realtimeintelligentsystembasedonmachinelearningmethodsforimprovingcommunicationinsignlanguage AT muhammadtanveerriaz realtimeintelligentsystembasedonmachinelearningmethodsforimprovingcommunicationinsignlanguage |