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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Author: search.filters.author.Samarasinghe, PSubject: search.filters.subject.Analysis

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    Non-Verbal Bio-Markers for Automatic Depression Analysis
    (IEEE, 2021-12-02) Yashodhika, G. B. O; De Silva, L. S. R.; Chathuranaga, W. W. P K; Yasasmi, D. L. R; Samarasinghe, P; Pandithakoralage, S; Piyawardana, V
    Detection of early depression risk is essential to help the affected individual to get timely medical treatment. However, automatic Depression Risk Analysis has not received significant focus in prior studies. This paper aims to propose an Automatic Depression Risk Analyzer based on non-verbal biomarkers; facial and emotional features, head posture, linguistic, mobile utilization, and biometrics. The analysis has shown that facial and emotional features can learn to identify depression risk better when compared with the head pose and emotional features. Moreover, the study shows that Depression Risk Analysis based on linguistic performed well with 95% accuracy for Sinhala content and 96% accuracy for contextual in English. Identifying the depression risk based on the biometrics, the sleep pattern analysis obtained 95% accuracy with the K Nearest Neighbour (KNN). Further, the mobile utilization analysis with the KNN model achieved 81% accuracy towards the Depression Risk Analysis. The accuracy of Depression Risk Analysis can be improved by extending analytic models to work as a single model. Furthermore, The models have been integrated with a mobile application that allows users to get a comprehensive Depression Risk Analysis based on each biomarker. These additional methods will function together to provide a more accurate on assessing depression risk.
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    Skeleton Based Periodicity Analysis of Repetitive Actions
    (IEEE, 2022-04-07) Wedasingha, N; Samarasinghe, P; Seneviratne, L; Puiatti, A; Papandrea, M; Dhanayaka, D
    This paper investigates the problem of detecting and recognizing repetitive actions performed by a human. Repetitive action analysis play a major role in detecting many behavioral disorders. In this work, we present a robust framework for detecting and recognizing repetitive actions performed by a human subject based on periodic and aperiodic action analysis. Our framework uses focal joints in the human skeleton for the analysis of repetitive actions which are substantiated by the principles of human anatomy and physiology. Using Non-deterministic Finite Automata (NFA) techniques, in this paper, we introduce a novel model to transform repetitive action count to differentiate the periodicity in human action. Experimental results on a dataset consisting of 371 video clips show that our algorithm outperforms the state-of-art (RepNet) [1] in simultaneous multiple repetitive action counts. Further, while the proposed model and RepNet give comparable results in counting periodic repetitive actions, our model performance surpass RepNet significantly on analysing non-periodic repetitive behavior.
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    Analysis and performance of CMA blind deconvolution for image restoration
    (Wiley Online Library, 2015-09) Samarasinghe, P; Kennedy, R. A
    In this paper we study the applicability of classical blind deconvolution methods such as constant modulus algorithm (CMA) for blind adaptive image restoration. The requirements such as the source to be white, uniformly distributed and zero mean, which yield satisfactory convergence in the data communication application context, are revisited in the image restoration context, where a linear deblur kernel needs to be blindly adapted to compensate for an unknown image blur kernel with the objective to recover a source ground truth image. Through analysis and performance studies, we show that the performance of CMA is adversely affected by the intrinsic spatial correlation of natural images and by any deviation of their distribution from being platykurtic. We also show that decorrelation techniques designed to overcome spatial correlation cannot be effectively applied to rectify CMA performance for blind adaptive image restoration