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Author: search.filters.author.Murray, IStart date: 2022

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Now showing 1 - 3 of 3
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    PublicationOpen Access
    YOLO-MOTF: Motion-temporal fusion for dynamic object detection with a moving camera for assistive wheelchairs
    (Elsevier B.V., 2026-03-09) Tennekoon, S; Wedasingha, N; Welhenge, A; Abhayasinghe, N; Murray, I
    Dynamic object detection is fundamental to advancing vision-based navigation systems, particularly in environments where the camera itself is in motion. Despite progress in detection algorithms, existing approaches often struggle with challenges such as egomotion, short-term occlusions, temporal discontinuities, and computational cost. This paper presents YOLO-MOTF, a novel knowledge-based model that integrates spatial features with motion cues, especially for operation under moving camera conditions. The framework incorporates a hybrid motion compensation strategy to suppress camera-induced distortions and an occlusion handling buffer to preserve object trajectories through discontinuities. Additionally, a motion attention gating mechanism selectively reinforces moving object predictions by intersecting fused motion masks with semantic outputs. The proposed system achieves an F1 score of 88.6% and a 93% reduction in flow processing compared to dense flow methods, underscoring its robustness and efficiency in dynamic environments. Beyond theoretical contributions, the model demonstrates direct applicability in real-world knowledge-based decision systems, including healthcare applications such as assistive wheelchair navigation, as well as assistive robotics, autonomous navigation, and surveillance.
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    PublicationOpen 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.
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    PublicationEmbargo
    Human Gait Modeling, Prediction and Classification for Level Walking Using Harmonic Models Derived from a Single Thigh-Mounted IMU
    (MDPI, 2022-03) Abhayasinghe, N; Murray, I
    The majority of human gait modeling is based on hip, foot or thigh acceleration. The regeneration accuracy of these modeling approaches is not very high. This paper presents a harmonic approach to modeling human gait during level walking based on gyroscopic signals for a single thighmounted Inertial Measurement Unit (IMU) and the flexion–extension derived from a single thighmounted IMU. The thigh angle can be modeled with five significant harmonics, with a regeneration accuracy of over 0.999 correlation and less than 0.5◦ RMSE per stride cycle. Comparable regeneration accuracies can be achieved with nine significant harmonics for the gyro signal. The fundamental frequency of the harmonic model can be estimated using the stride time, with an error level of 0.0479% (±0.0029%). Six commonly observed stride patterns, and harmonic models of thigh angle and gyro signal for those stride patterns, are presented in this paper. These harmonic models can be used to predict or classify the strides of walking trials, and the results are presented herein. Harmonic models may also be used for activity recognition. It has shown that human gait in level walking can be modeled with a harmonic model of thigh angle or gyro signal, using a single thigh-mounted IMU, to higher accuracies than existing techniques.