AVR based embedded system for speech impaired people

All over the world deaf and dump people face many problems while communication. There are various challenges experienced by speech and hearing impaired people at public places in expressing themselves to normal people. The objective of this paper is to provide the solution to this problem. To reduce the communication gap between the common people and speech impaired people the proposed system is designed and implemented. The embedded system consist of wearable sensing gloves along with flex sensors which are used to sense the motion of the fingers. Indian sign language is used for determining the words. Flex sensors and accelerometer are used as sensor, these sensors are mounted on the gloves, the movement include the angle tilt, rotation and direction changes, these signals are processed by the microcontroller and playback voice is generated indicating signs through speaker.

[1]  Kotaro Tadano,et al.  Development of grip amplified glove using bi-articular mechanism with pneumatic artificial rubber muscle , 2010, 2010 IEEE International Conference on Robotics and Automation.

[2]  Kandarpa Kumar Sarma,et al.  Hand gesture recognition system with real-time palm tracking , 2014, 2014 Annual IEEE India Conference (INDICON).

[3]  Jeongsoo Lee,et al.  Development of a finger motion measurement system using linear potentiometers , 2014, 2014 IEEE/ASME International Conference on Advanced Intelligent Mechatronics.

[4]  Suchin Adhan,et al.  American Sign Language recognition by using 3D geometric invariant feature and ANN classification , 2014, The 7th 2014 Biomedical Engineering International Conference.

[5]  Michiko Nishiyama,et al.  Wearable Sensing Glove With Embedded Hetero-Core Fiber-Optic Nerves for Unconstrained Hand Motion Capture , 2009, IEEE Transactions on Instrumentation and Measurement.

[6]  Jeongsoo Lee,et al.  Development of a Wearable Sensing Glove for Measuring the Motion of Fingers Using Linear Potentiometers and Flexible Wires , 2015, IEEE Transactions on Industrial Informatics.

[7]  M. Geetha,et al.  Gesture Recognition for American Sign Language with Polygon Approximation , 2011, 2011 IEEE International Conference on Technology for Education.

[8]  Haruhisa Kawasaki,et al.  Development of a Hand-Assist Robot With Multi-Degrees-of-Freedom for Rehabilitation Therapy , 2012, IEEE/ASME Transactions on Mechatronics.

[9]  Mohamed Hisham Jaward,et al.  Robust ASL Fingerspelling Recognition Using Local Binary Patterns and Geometric Features , 2013, 2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA).

[10]  Waqar Ahmed,et al.  Cost effective portable system for sign language gesture recognition , 2008, 2008 IEEE International Conference on System of Systems Engineering.

[11]  S. Poonguzhali,et al.  Design and development of hand gesture recognition system for speech impaired people , 2015, 2015 International Conference on Industrial Instrumentation and Control (ICIC).

[12]  Chin-Shyurng Fahn,et al.  Development of a data glove with reducing sensors based on magnetic induction , 2005, IEEE Transactions on Industrial Electronics.

[13]  Kongqiao Wang,et al.  A Sign-Component-Based Framework for Chinese Sign Language Recognition Using Accelerometer and sEMG Data , 2012, IEEE Transactions on Biomedical Engineering.

[14]  Abdulmotaleb El-Saddik,et al.  E-Glove: An electronic glove with vibro-tactile feedback for wrist rehabilitation of post-stroke patients , 2011, 2011 IEEE International Conference on Multimedia and Expo.