Browsing by Author "Wedasingha, N"

Now showing 1 - 7 of 7

Open Access
A Context-Aware Doorway Alignment and Depth Estimation Algorithm for Assistive Wheelchairs
(Multidisciplinary Digital Publishing Institute (MDPI), 2025-07-17) Tennekoon, S; Wedasingha, N; Welhenge, A; Abhayasinghe, N; Murray, I
Navigating through doorways remains a daily challenge for wheelchair users, often leading to frustration, collisions, or dependence on assistance. These challenges highlight a pressing need for intelligent doorway detection algorithm for assistive wheelchairs that go beyond traditional object detection. This study presents the algorithmic development of a lightweight, vision-based doorway detection and alignment module with contextual awareness. It integrates channel and spatial attention, semantic feature fusion, unsupervised depth estimation, and doorway alignment that offers real-time navigational guidance to the wheelchairs control system. The model achieved a mean average precision of 95.8% and a F1 score of 93%, while maintaining low computational demands suitable for future deployment on embedded systems. By eliminating the need for depth sensors and enabling contextual awareness, this study offers a robust solution to improve indoor mobility and deliver actionable feedback to support safe and independent doorway traversal for wheelchair users.
Embargo
Auto-encoder Based Data Clustering for Typical and Atypical Repetitive Child Hand Movement Pattern Identification
(SLIIT, Faculty of Engineering, 2024-10) Wedasingha, N; Samarasinhe, P; Seneviratne, L; Papandrea, M; Puiatti, A
This study is dedicated to the important task of identifying unique repetitive hand movement patterns in children, with the aim of facilitating early anomaly detection. The current body of literature lacks a comprehensive model capable of effectively discerning distinctive patterns in child repetitive hand movements. To address this gap, our innovative approach employs autoencoders to efficiently compress intricate data and extract latent features from a dataset with inherent limitations. By utilizing clustering techniques, we analyze these features to reveal distinct behaviors associated with child hand movements. Despite the challenges posed by binary annotated datasets, our model demonstrates outstanding performance in categorizing movements into four distinct types, thereby providing valuable insights into the intricate landscape of child hand movement patterns. Statistical assessments further underscore the superiority of our autoencoder, achieving a mean Bayesian value of 0.112, outperforming state-of-the-art algorithms in this domain. Subsequent in-depth analysis exposes notable inter-cluster patterns, elucidating transitions from typical to atypical behavior in child hand movements. This research constitutes a significant advancement in the field of child hand movement pattern analysis, offering a powerful and sophisticated tool for healthcare professionals and researchers alike. The automation capabilities embedded in our model empower these professionals to address childhood behavioral disorders more effectively and efficiently. In essence, our research not only contributes to the enhancement of early anomaly detection systems but also serves as a valuable resource for professionals engaged in child healthcare and behavioral research, facilitating a deeper understanding of these nuanced patterns.
Embargo
Autoencoder based data clustering for identifying anomalous repetitive hand movements, and behavioral transition patterns in children
(Springer Science and Business Media Deutschland GmbH, 2025-01-21) Wedasingha, N; Samarasinghe, P; Senevirathna, L; Papandrea, M; Puiatti, A
The analysis of repetitive hand movements and behavioral transition patterns holds particular significance in detecting atypical behaviors in early child development. Early recognition of these behaviors holds immense promise for timely interventions, which can profoundly impact a child’s well-being and future prospects. However, the scarcity of specialized medical professionals and limited facilities has made detecting these behaviors and unique patterns challenging using traditional manual methods. This highlights the necessity for automated tools to identify anomalous repetitive hand movements and behavioral transition patterns in children. Our study aimed to develop an automated model for the early identification of anomalous repetitive hand movements and the detection of unique behavioral patterns. Utilizing autoencoders, self-similarity matrices, and unsupervised clustering algorithms, we analyzed skeleton and image-based features, repetition count, and frequency of repetitive child hand movements. This approach aimed to distinguish between typical and atypical repetitive hand movements of varying speeds, addressing data limitations through dimension reduction. Additionally, we aimed to categorize behaviors into clusters beyond binary classification. Through experimentation on three datasets (Hand Movements in Wild, Updated Self-Stimulatory Behaviours, Autism Spectrum Disorder), our model effectively differentiated between typical and atypical hand movements, providing insights into behavioral transitional patterns. This aids the medical community in understanding the evolving behaviors in children. In conclusion, our research addresses the need for early detection of atypical behaviors through an automated model capable of discerning repetitive hand movement patterns. This innovation contributes to early intervention strategies for neurological conditions
Embargo
Child Head Gesture Classification through Transformers
(Institute of Electrical and Electronics Engineers Inc., 2022-11-04) Wedasingha, N; Samarasinghe, P; Singarathnam, D; Papandrea, M; Puiatti, A; Seneviratne, L
This paper proposes a transformer network for head pose classification (HPC) which outperforms the existing SoA for HPC. This robust model is then extended to overcome the limited child data challenge by applying transfer learning resulting in an accuracy of 95.34% for child HPC in the wild.
Open Access
Object Recognition and Assistance System for Visually Impaired Shoppers
(Sri Lanka Institute of Information Technology, 2023-03-25) Tennekoon, S; Abhayasinghe, N; Wedasingha, N
Shopping is indeed effortless for many individuals. However, it could certainly be a struggle and chaotic experience for the visually impaired. Visual impairment causes many societal stigma and inconvenience to visually impaired individuals. Although shopping may sound extremely easy, this is a crucial social activity for many visually impaired (VI) individuals. Visually impaired (VI) shoppers always require assistance when shopping for product identification purposes. This may lead to greater inconvenience as delays, lack of information and product familiarity of shop assistants may occur. Therefore, allowing visually impaired shoppers to independently perform shopping regardless of size and position of the shopping mall is essential. This encourages them to participate in enhanced social activities and perform their daily chores in independence. Although many products have been developed to assist visually impaired shoppers at shopping malls, due to their drawbacks, some of these have seem to undergo failures in producing accurate information to the visually impaired shopper for object identification and caused inconvenience. This project proposes a feasible solution for visually impaired shoppers to perform their shopping at ease and independently. Object recognition has been made possible in order to identify garment items while shopping with no assistance of another individual. The Convolutional Neural Network (CNN) has been used to obtain a sufficiently good accuracy and precision with a validation accuracy of 90%. Some of the novel techniques such as Ensemble Modelling has also been performed in order to reduce any generalization errors of the prediction and achieve a greater accuracy while overcoming all of the drawbacks of the currently existing products in the market. The overall product is proposed to attain maximum consumer population of visually impaired shoppers with satisfaction, reliability, and low cost.
Open Access
Pneumonia Detection and Lung Disease Assessment from Chest X-rays: Developing A Diagnostic Support System
(SLIIT, Faculty of Engineering, 2025-01) Jayawardena, C.A; Wedasingha, N; Kolambage, N; Perera, S
This research, dedicated to developing an accurate and efficient pneumonia detection system from Chest X-Ray images, highlights the significance of automated tools in enhancing healthcare diagnostics. Its significance lies in the fact that pneumonia is a prevalent respiratory condition that requires timely and accurate diagnosis for effective medical intervention. The project's objective was to make use of convolutional neural networks and image analyses to create an automated diagnostic tool that could assist healthcare professionals in identifying pneumonia with precision and efficiency. To achieve this, the system initially made use of two custom deep learning architectures but ultimately used a pretrained CheXNet-based model, developed by using transfer learning. This choice was made by considering CheXNet’s proven performance in identifying pneumonia and other pulmonary conditions. The project's results proved promising, with the CheXNet-based model achieving high diagnostic accuracy and providing valuable insights into the presence of pneumonia. The system's architecture, using deep learning and the use of DICOM images, demonstrated its effectiveness in improving the accuracy and efficiency of pneumonia diagnosis. Based on the results, this paper further demonstrates a web-based application for interaction with the system. Additionally, it provides information on the work that could be done in the future. Thus, this research contributes to the growing field of medical image analysis and highlights the significance of automated tools in enhancing healthcare diagnostics. The project's outcomes are meant to pave the way for more efficient and accessible methods for pneumonia detection, ultimately benefiting both healthcare providers and patients.
Embargo
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.