Browsing by Author "Velayuthan, M"

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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
Mobile-Based Analysis of Visual Attention in Young Children
(IEEE, 2022-12-09) Jayakody, K; Dhanawansa, V; Velayuthan, M; Samarasinghe, P
There is a crucial need to screen young children for attention impairments given that the ability of a child to deal with the demands of everyday life is dependent on the development of the child’s attention. Intervention at a young age facilitates the training and enhancement of attention, as young brains are the most responsive to treatment. Sri Lanka, a low-income country, lacks accessible, home-based screening tools which can be used to assess the attention of young children. Moreover, most Sri Lankan parents are not aware of attention impairments. To bridge this gap, this paper proposes an easily accessible, home-based attention assessment tool in the form of a mobile application. The application provides a series of engaging tasks for assessing and training, the aspects of visual attention (focused attention, selective attention, divided attention, sustained attention and shifting attention). The assessments were carefully designed to suit the age and the attention span of the child. The performance analysis performed on the data collected showed the varied responses of children of different ages on different assessments. Clustering was performed in identifying the varying performance levels of typical children and this project will be extended to evaluate atypical child performance.
Embargo
Qualitative Analysis of Automated Visual Tracking of Objects Through Head Pose Estimation
(IEEE, 2022-12-09) Abeysinghe, A; Arachchige, I. D; Samarasinghe, P; Dhanawansa, V; Velayuthan, M
An automated approach for object tracking and gaze estimation via head pose estimation is crucial, to facilitate a range of applications in the domain of -human-computer interfacing, this includes the analysis of head movement with respect to a stimulus in assessing one’s level of attention. While varied approaches for gaze estimation and object tracking exist, their suitability within such applications have not been justified. In order to address this gap, this paper conducts a quantitative comparison of existing models for gaze estimation including Mediapipe and standalone models of Openface and custom head pose estimation with MTCNN face detection; and object detection including models from CSRT object tracker, YOLO object detector, and a custom object detector. The accuracy of the aforementioned models were compared against the annotations of the EYEDIAP dataset, to evaluate their accuracy both relative and non-relative to each other. The analysis revealed that the custom object detector and the Openface models are relatively more accurate than the others when comparing the number of annotations, absolute mean error, and the relationship between x displacement-yaw, and y displacement-pitch, and thereby can be used in combination for gaze tracking tasks.