International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 12 | Dec 2025
p-ISSN: 2395-0072
www.irjet.net
Sensor-Based Sign Language Recognition using a Smart Glove and Hybrid Deep Learning Models (Conv1D + BiLSTM). S.P. Kullarkar 1 , Anshu Gudhewar 2, Sidhesh Dhande 3, Ravi Shripad4, Kautuk Butle 5 1Assistant Professor, Dept. of AI&DS, KDK. College Of Engineering, Maharashtra, India 2Student, Dept. of AI&DS, KDK. College Of Engineering, Maharashtra, India 3Student, Dept. of AI&DS, KDK. College Of Engineering, Maharashtra, India 4Student, Dept. of AI&DS, KDK. College Of Engineering, Maharashtra, India 5Student, Dept. of AI&DS, KDK. College Of Engineering, Maharashtra, India
---------------------------------------------------------------------***--------------------------------------------------------------------Abstract -Communication is key to human life, but for those understand in real time. Traditional sign language, are
highly used within the deaf and mute People and is not widely understand by the Majority population, thus creating a communication gap between them. This gap must be filled by a new type of solution that would read sign language, interprets it and translate it to a universal format such as text or speech.
who are deaf or Problem of hearing, communication across species borders can be complicated, not everyone knows sign language. This creates a communication outlet which restricts their ability to interact socially, attend school or work in public. To fix this, our project proposes AI Based Sign Language Interpreter Glove, that will convert any hand gesture for sign language into a speech or text.
This work is aiming to explore AI-powered smart wearable glove for near real-time sign language detection and translation. Multiple sensors (e.g., flex sensors, accelerometers, and gyroscopes) will be mounted on the glove to measure precise finger bending motions, hand posture and gestures. These sensor signals will be analyzed with the help of Advance deep learning techniques to reliably capture sign language patterns. Upon detection of the signs, the system will convert them into text or speech output so that sign language users and non-signers could communicate smoothly.
The glove is designed to include flex sensors and motion sensors to measure fingers and hands bending and moving. That information goes to a microcontroller, which then processes the data and sends it to an AI model/Deep Learning Model trained to interpret various sign language gestures. After the detection of the signs, the signs are translated into text appearing on the monitor or the display attach to Glove then Converted into voice through a Text-toSpeech (TTS) system. The presented solution is low-cost, portable, and easy-to-use, allowing daily use. It has the possibility of creating the conditions for individuals with disabilities to truly be themselves, participate in society, and experience the potential they have by allowing them to communicate freely and effectively with normal People.
The inclusion of Internet of Things (IoT) data connectivity also makes the system versatile in that via wireless or wired means, the identified output can be viewable on smart phones, computers, terms etc. This not only makes the technology portable and accessible but also ensures that it can be used in diverse real-world scenarios such as education, healthcare, workplace communication, and public services.
The most important advantage of this project was its practicability. Compared to vision-based systems based on cameras and controlled environments, a glove-based solution is low-weight, portable and not reliant upon the lighting or background. It is also economical and affordable, making it an appropriate device for daily aid. The design is also scalable, too, so more gestures and more than one sign language can be included in the future.
2. RELATED WORK A. In 2024, Zhang has developed the wearable gesture recognition glove that recognize gesture in real time with 95% accuracy. The overall system is combination of emg sensor and computer vision that demonstrating the feasibility and portability in gesture recognizing.
Key Words: Sign Language, Interpreter Glove, Artificial Intelligence, Deep Learning, Flex Sensors, Motion Sensor, Communication, Text-to-Speech, microcontroller, Embedded Systems
B. In 2024, Filipowska and the team build a device which uses neural network to recognize gestures. This design focuses on fixing sensors drift and difference between finger movement. This are the combination of on device and cloud processing to make it reliable in real time processing,
1. INTRODUCTION Communication is the foundation of human development, the individuals with speech and hearing Problem faces significant barriers in expressing themselves and not being
© 2025, IRJET
|
Impact Factor value: 8.315
|
ISO 9001:2008 Certified Journal
|
Page 416