Research Publications Authored by SLIIT Staff
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This collection includes all SLIIT staff publications presented at external conferences and published in external journals. The materials are organized by faculty to facilitate easy retrieval.
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Publication Embargo Continuous American Sign Language Recognition Using Computer Vision And Deep Learning Technologies(IEEE, 2022-08-29) Senanayaka, S.A.M.A.S; Perera, R.A.D.B.S; Rankothge, W.; Usgalhewa, S.S.; Hettihewa, H.DSign language is a non-verbal communication method used to communicate between hard of hearing or deaf and ordinary people. Automatic Sign language detection is a complex computer vision problem due to the diversity of modern sign languages and variations in gesture positions, hand and finger form, and body part placements. This research paper aims to conduct a systematic experimental evaluation of computer vision-based approaches for sign language recognition. The present research focuses on mapping non-segmented video streams to glosses to gain insights into sign language recognition. The proposed machine learning model consists of Recurrent Neural Network (RNN) layers such as Long Short-Term Memory (LSTM). The model is implemented using current deep learning frameworks such as Google TensorFlow and Keras API.Publication Open Access Novel Sprinter Assistive Smart Agent for Continuous Performance Improvement(IEEE, 2021-04-06) Subhashana, H; Bandara, C; Bandara, I; Devindi, A; Kuruwitaarachchi, N; Dharmasena, TIn the field of Sports, sprinting is the term used for introducing running over a short distance in a limited time. To date, a method to identify whether sprinters are getting enough speed during the accelerated period is not available so far. This paper proposes a smart agent to recognize the technical precision and performance of a sprinter using wireless hardware devices and a software solution. Smart shoe, track sensor, arm motion detection bracelet are the devices used to collect data from a sprinter. After required data collecting is complete the based web application provides feedback to the sprinter to improve sprinting techniques. This modern technology based system reduces human errors and workload of a trainer and would be highly beneficial for the sports community including coaches and sprinters as it could be accessed through mobile phones. The results of the study show the visualization of sprinter data effectively and an analysis on the obtained data regarding the performance.