Browsing by Author "Peiris, H"

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Open Access
Diagnosing autism in low‐income countries: Clinical record‐based analysis in Sri Lanka
(Wily, 2022-06-16) Samarasinghe, P; Wickramarachchi, C; Peiris, H; Vance, P; Dahanayake, D. M. A.; Kulasekara, V; Nadeeshani, M
Use of autism diagnosing standards in low-income countries (LICs) are restricted due to the high price and unavailability of trained health professionals. Furthermore, these standards are heavily skewed towards developed countries and LICs are underrepresented. Due to such constraints, many LICs use their own ways of assessing autism. This is the first retrospective study to analyze such local practices in Sri Lanka. The study was conducted at Ward 19B of Lady Ridgeway Hospital (LRH) using the clinical forms filled for diagnosing ASD. In this study, 356 records were analyzed, from which 79.5% were boys and the median age was 33 months. For each child, the clinical form together with the Childhood Autism Rating Scale (CARS) value were recorded. In this study, a Clinically Derived Autism Score (CDAS) is obtained from the clinical forms. Scatter plot and Pearson product moment correlation coefficient were used to benchmark CDAS with CARS, and it was found CDAS to be positively and moderately correlated with CARS. In identifying the significant variables, a logistic regression model was built based on clinically observed data and it evidenced that “Eye Contact,” “Interaction with Others,” “Pointing,” “Flapping of Hands,” “Request for Needs,” “Rotate Wheels,” and “Line up Things” variables as the most significant variables in diagnosing autism. Based on these significant predictors, the classification tree was built. The pruned tree depicts a set of rules, which could be used in similar clinical environments to screen for autism.
Embargo
EyeVista: An assistive wearable device for visually impaired sprint athletes
(IEEE, 2016-12-16) Peiris, H; Kulasekara, C; Wijesinghe, H; Kothalawala, B; Walgampaya, N; Kasthurirathna, D
On-going progressions of Information Technology increase the scope for computer vision-based interventions to facilitate efficient and promising technology for people with disabilities. This project aims to develop a wearable navigational assistive device, titled EyeVista, to facilitate visually impaired sprint athletes. It is a lightweight, easy-to-use, customizable and low-cost wearable jacket built-in with off-the-shelf based on computer vision techniques. Synthesis of research initially reflects the impact of the main barriers of a human guide and how to break down such barriers. In doing so, we hope to introduce an alternative to the current practice of having a human guide for blind athletes, overcoming the shortcomings of it. The designed system uses Raspberry Pi single board computer to process the real-time image captured by Raspberry Pi camera module to navigate the athletes within the assigned track and to avoid collisions. As a result, we believe the project EyeVista will empower the visually impaired sprint athletes to enhance their performance by easing their mobility by allowing the user to move within their relevant track lanes and avoid collisions without the support of a human guide and enhance the independence, safety along with the quality of life.