Research Publications Authored by SLIIT Staff

Now showing 1 - 2 of 2

Embargo
Automated Child Social Attention Evaluation
(IEEE, 2022-12-09) Sandunika Wasala, K; Dhanawansa, V; Velayuthan, M; Samarasinghe, P
Providing proper care for children with attention difficulty disorder is crucial, one way to ensure this is early identification of these disorders. In Sri Lanka, a developing country, it is difficult to find resources such as clinics, clinical expertise, and other resources which are essential for diagnosis. The absence of these apparatuses risks the mental well-being of the child as well as access to help. Hence a need arises to develop an automated social attention evaluation system. This will serve as the first line of diagnosis and help the parents/guardians secure the help required from an early age for the child. To the best of the authors’ knowledge, no solution of this nature is readily available for the Sri Lankan community so far. Keeping the low-income bracket of the country in mind, we propose a solution that can be easily deployed even on a cheap mobile/tablet-like device. It is difficult to perform these evaluations for children in similar settings as adults, as they are easily distracted. Therefore, care must be taken to grab the child’s attention throughout the evaluation process. In this research, we developed applications for children at different levels and each level assesses child attention between social objects and non-social objects through a child-friendly game, as they have sufficient visual stimuli to hold the child’s attention. In this study we investigated the screen time spent by the child, the attention of the child on different categories of images (High Autism Interested or Low Autism Interested images), and the switching patterns of the attention between these images. Only typical children were evaluated for this research due to the pandemic situation as well as other internal problems in the country. This system will test and evaluate atypical children in our future work.
Embargo
Self-speech evaluation with speech recognition and gesture analysis
(IEEE, 2018-10-02) Shangavi, S; Jeyamaalmarukan, S; Jathevan, A; Umatharsini, M; Samarasinghe, P
Speaking helps people to improve their communication, public speaking and leadership skills. There are two main techniques that help a speaker to deliver a meaningful speech. The techniques are voice transition which expresses a verbal message and gestures that convey the message to an audience. A famous organization to help and improvise speech is Toastmasters. Their systems of evaluation are such as, Tracking Filler words, Usage of Redundant words and Phrases, Checking Grammar and Pronunciation, Usage of Body Movements and Gestures, Tracking Vocal Variations and Time Management. If an ordinary person wants to self-evaluate his or her speech, that person has to be a member of a Toastmasters Club or any other speech improvising organization. By using our application, it is possible for a person to evaluate his or her own speech without depending on an organization. All the above-mentioned criteria in manual evaluation processes are included in this application. Since nowadays mobile applications are frequent in use, our system is proposed in Android Platform. Several techniques and methods are used to interconnect with Android such as OpenCV, Microsoft Cognitive Services and MATLAB in order to achieve the objectives of the application. Acoustic Model, Support Vector Model (SVM), Hidden Markov Model (HMM) are some models used to build the application more efficient by giving approximately accurate results.

Research Publications Authored by SLIIT Staff

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