Publicação
A real-time intelligent system based on machine-learning methods for improving communication in sign language
| Resumo: | 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. |
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| Autores principais: | Leiva, Víctor |
| Outros Autores: | Rahman, Muhammad Zia Ur; Akbar, Muhammad Azeem; Castro, Cecilia; Huerta, Mauricio; Riaz, Muhammad Tanveer |
| Assunto: | Assistive technology Flex sensor glove Kalman filter MPU-6050 device Raspberry Pi 3B Microcontroller Real-time processing Sensor-based gesture recognition Sign language recognition Raspberry Pi 3B microcontroller |
| Ano: | 2025 |
| País: | Portugal |
| Tipo de documento: | artigo |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade do Minho |
| Idioma: | inglês |
| Origem: | RepositóriUM - Universidade do Minho |
| Resumo: | 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. |
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