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Subject: search.filters.subject.Fall detection

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    PublicationOpen Access
    Towards Safer Elderly Care: A Convolutional Neural Network Solution for Fall Detection
    (Faculty of Engineering, 2025-09-09) Kalupahana R.W; Maduranga M.W.P
    As modern life becomes increasingly busy, computer vision-based monitoring systems have become essential, particularly in elderly care. This paper presents the development of a robust fall detection system using deep learning techniques, specifically a convolutional neural network (CNN) that processes RGB images to accurately distinguish between fall and non-fall events. The model is trained and validated on a dataset categorized into two classes: fall and non-fall. By utilizing convolutional and pooling layers, CNN effectively learns hierarchical representations of the input data, capturing both low-level and high-level features crucial for accurate fall detection. The key stages of this approach include data acquisition, pre-processing, and model training. The model's performance is evaluated using precision, recall, and F1-score metrics, demonstrating high accuracy, which is further enhanced through data augmentation, pre-processing, and crossvalidation techniques. A confusion matrix analysis confirms the model's effectiveness in correctly classifying instances across both classes. The system also extends its capabilities to video analysis by extracting frames at 30-second intervals, ensuring continuous and comprehensive monitoring. This research highlights the potential of deep learning to enhance safety and care for the elderly, offering a reliable solution for real-time fall detection. The findings underscore the importance of integrating advanced technologies into healthcare, paving the way for future innovations in monitoring and assistance systems.
  • Thumbnail Image
    PublicationOpen Access
    Smart Health Monitoring System
    (SLIIT, Faculty of Engineering, 2024-03) Gajanayake, G. M. T. S.; Ekanayake, W. E. M. K. D. D; Malinda, G. D. C.; Malasinghe, L; De Silva, S
    Due to the high inpatient population in hospitals, regular monitoring of inpatients' vital signs is currently a practical concern. As a solution, our proposed system manages the continuous analysis of the vital signs of every inpatient in the general wards, and informs medical professionals in any location at any time about their inpatients' current states in real-time to improve inpatients' health. The suggested system consists of the following arrangements; arrangement for acquiring health readings, identifying the on-duty reported doctors in charge of wards, arrangement for health data exhibiting unit, fall detection, and ECG acquisition. In addition to these arrangements, a website, and an android mobile application were designed to publish measured inpatient vital signs. This proposed product is both novel and different from the existent products because, it comprises of collective arrangements, and is developed in order to assess hospital wards’ inpatients, whereas other systems are designed for remote health monitoring of patients at home. This paper describes the system that was developed and tested successfully.