Research Papers - Department of Electrical and Electronic Engineering

Permanent URI for this collectionhttps://rda.sliit.lk/handle/123456789/679

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Author: search.filters.author.Abhayasinghe, N.Start date: 2020

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    Hand Rehabilitation Using Robot-Assisted Physiotherapy
    (2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Madhushan, I.H.D.; Charnara, E.B.K.; De Zoysa, A.T.J.; Upeka, G.S.; Abhayasinghe, N.; Abeygunawardhana, P.
    Robotics technology in the modern world is currently being implemented in medical fields to improve the quality of care and patient outcomes. In the proposed system, the robotics technology is used for physiotherapy. In the existing physiotherapy robot devices, there is no feature that provides exercise for every joint of the fingers and the wrist. Therefore, in this system, we used forward kinematics technologies to address each joint of the fingers and wrist thatcan access by the physiotherapist. We have designed the robot hand using the solid work and implemented 3D model then assembled system was tested again using different scenarios. Most existing robotic systems provide finger and wrist exercises separately, but our system can provide all exercises simultaneously. In here, we can predict the next exercises that are given for the patient and the progress of the rehabilitation of the patient. For the prediction, we developed the models using the FB prophet algorithm. When using this device, the patient's hand exercises are monitored in real-time and the physiotherapist can see the angles of the hand movement while controlling the robot device. To control this robot device, we used a mobile application.
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    IoT Based Sign Language Recognition System
    (2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Punsara, K.K.T.; Premachandra, H.H.R.C.; Chanaka, A.W.A.D.; Wijayawickrama, R.V.; Abhayasinghe, N.; De Silva, R.
    Sign language is the key communication medium, which deaf and mute people use in their day - to-day life. Talking to disabled people will cause a difficult situation since a non-mute person cannot understand their hand gestures and in many instances, mute people are hearing impaired. Same as Sinhala, Tamil, English, or any other language, sign language also tend to have differences according to the region. This paper is an attempt to assist deaf and mute people to develop an effective communication mechanism with non-mute people. The end product of this project is a combination of a mobile application that can translate the sign language into digital voice and loT-enabled, light-weighted wearable glove, which capable of recognizing twenty-six English alphabet, digits, and words. Better user experience provides with voice-to-text feature in mobile application to reduce the communication gap within mute and non-mute communities. Research findings and results from the current system visualize the output of the product can be optimized up to 25 % -35 % with an enhanced pattern recognition mechanism.