Ahsen Tahir
HRNN4F: Hybrid deep random neural network for multi-channel fall activity detection
Tahir, Ahsen; Ahmad, Jawad; Morison, Gordon; Larijani, Hadi; Gibson, Ryan M.; Skelton, Dawn A.
Authors
Dr Jawad Ahmad J.Ahmad@napier.ac.uk
Lecturer
Gordon Morison
Hadi Larijani
Ryan M. Gibson
Dawn A. Skelton
Abstract
Falls are a major health concern in older adults. Falls lead to mortality, immobility and high costs to social and health care services. Early detection and classification of falls is imperative for timely and appropriate medical aid response. Traditional machine learning models have been explored for fall classification. While newly developed deep learning techniques have the ability to potentially extract high-level features from raw sensor data providing high accuracy and robustness to variations in sensor position, orientation and diversity of work environments that may skew traditional classification models. However, frequently used deep learning models like Convolutional Neural Networks (CNN) are computationally intensive. To the best of our knowledge, we present the first instance of a Hybrid Multichannel Random Neural Network (HMCRNN) architecture for fall detection and classification. The proposed architecture provides the highest accuracy of 92.23% with dropout regularization, compared to other deep learning implementations. The performance of the proposed technique is approximately comparable to a CNN yet requires only half the computation cost of the CNN-based implementation. Furthermore, the proposed HMCRNN architecture provides 34.12% improvement in accuracy on average than a Multilayer Perceptron.
Journal Article Type | Article |
---|---|
Acceptance Date | Jul 11, 2019 |
Online Publication Date | Aug 23, 2019 |
Publication Date | Aug 23, 2019 |
Deposit Date | Oct 15, 2019 |
Publicly Available Date | Oct 16, 2019 |
Journal | Probability in the Engineering and Informational Sciences |
Print ISSN | 0269-9648 |
Electronic ISSN | 1469-8951 |
Publisher | Cambridge University Press |
Peer Reviewed | Peer Reviewed |
Pages | 1-14 |
DOI | https://doi.org/10.1017/s0269964819000317 |
Keywords | probabilistic networks, queueing theory, simulation |
Public URL | http://researchrepository.napier.ac.uk/Output/2227997 |
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HRNN4F: Hybrid Deep Random Neural Network For Multi-channel Fall Activity Detection (Accepted Manuscript)
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