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HRNN4F: HYBRID DEEP RANDOM NEURAL NETWORK FOR MULTI-CHANNEL FALL ACTIVITY DETECTION

Published online by Cambridge University Press:  23 August 2019

Ahsen Tahir Open the ORCID record for Ahsen Tahir [Opens in a new window] ,
Jawad Ahmad ,
Gordon Morison ,
Hadi Larijani Open the ORCID record for Hadi Larijani [Opens in a new window] ,
Ryan M. Gibson  and
Dawn A. Skelton
Ahsen Tahir
Affiliation:
School of Computing, Engineering and Built Environment, Glasgow Caledonian University, Glasgow, UK Department of Electrical Engineering, University of Engineering and Technology, Lahore, Pakistan E-mails: [email protected]; [email protected]
Jawad Ahmad
Affiliation:
School of Computing, Engineering and Built Environment, Glasgow Caledonian University, Glasgow, UK
Gordon Morison
Affiliation:
School of Computing, Engineering and Built Environment, Glasgow Caledonian University, Glasgow, UK
Hadi Larijani
Affiliation:
School of Computing, Engineering and Built Environment, Glasgow Caledonian University, Glasgow, UK
Ryan M. Gibson
Affiliation:
School of Computing, Engineering and Built Environment, Glasgow Caledonian University, Glasgow, UK
Dawn A. Skelton
Affiliation:
School of Health and Life Sciences, Glasgow Caledonian University, Glasgow, UK
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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.

Keywords

probabilistic networksqueueing theorysimulation
Type
Research Article
Information
Probability in the Engineering and Informational Sciences , Volume 35 , Special Issue 1: Learning, Optimization, and Theory of G-Networks , January 2021 , pp. 37 - 50
Copyright
Copyright © Cambridge University Press 2019

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