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Article

HF-SPHR: Hybrid Features for Sustainable Physical Healthcare Pattern Recognition Using Deep Belief Networks

1
Department of Computer Science, Air University, Islamabad 44000, Pakistan
2
Department of Computer Science and Software Engineering, United Arab Emirates University, Al Ain 15551, United Arab Emirates
3
Department of Human-Computer Interaction, Hanyang University, Ansan 15588, Korea
*
Author to whom correspondence should be addressed.
Academic Editor: Justo García Sanz-Calcedo
Sustainability 2021, 13(4), 1699; https://doi.org/10.3390/su13041699
Received: 18 December 2020 / Revised: 29 January 2021 / Accepted: 31 January 2021 / Published: 4 February 2021
(This article belongs to the Special Issue Sustainable Human-Computer Interaction and Engineering)
The daily life-log routines of elderly individuals are susceptible to numerous complications in their physical healthcare patterns. Some of these complications can cause injuries, followed by extensive and expensive recovery stages. It is important to identify physical healthcare patterns that can describe and convey the exact state of an individual’s physical health while they perform their daily life activities. In this paper, we propose a novel Sustainable Physical Healthcare Pattern Recognition (SPHR) approach using a hybrid features model that is capable of distinguishing multiple physical activities based on a multiple wearable sensors system. Initially, we acquired raw data from well-known datasets, i.e., mobile health and human gait databases comprised of multiple human activities. The proposed strategy includes data pre-processing, hybrid feature detection, and feature-to-feature fusion and reduction, followed by codebook generation and classification, which can recognize sustainable physical healthcare patterns. Feature-to-feature fusion unites the cues from all of the sensors, and Gaussian mixture models are used for the codebook generation. For the classification, we recommend deep belief networks with restricted Boltzmann machines for five hidden layers. Finally, the results are compared with state-of-the-art techniques in order to demonstrate significant improvements in accuracy for physical healthcare pattern recognition. The experiments show that the proposed architecture attained improved accuracy rates for both datasets, and that it represents a significant sustainable physical healthcare pattern recognition (SPHR) approach. The anticipated system has potential for use in human–machine interaction domains such as continuous movement recognition, pattern-based surveillance, mobility assistance, and robot control systems. View Full-Text
Keywords: deep belief networks; hybrid-features; restricted Boltzmann machines; sustainable physical healthcare pattern recognition; wearable sensors system deep belief networks; hybrid-features; restricted Boltzmann machines; sustainable physical healthcare pattern recognition; wearable sensors system
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MDPI and ACS Style

Javeed, M.; Gochoo, M.; Jalal, A.; Kim, K. HF-SPHR: Hybrid Features for Sustainable Physical Healthcare Pattern Recognition Using Deep Belief Networks. Sustainability 2021, 13, 1699. https://doi.org/10.3390/su13041699

AMA Style

Javeed M, Gochoo M, Jalal A, Kim K. HF-SPHR: Hybrid Features for Sustainable Physical Healthcare Pattern Recognition Using Deep Belief Networks. Sustainability. 2021; 13(4):1699. https://doi.org/10.3390/su13041699

Chicago/Turabian Style

Javeed, Madiha, Munkhjargal Gochoo, Ahmad Jalal, and Kibum Kim. 2021. "HF-SPHR: Hybrid Features for Sustainable Physical Healthcare Pattern Recognition Using Deep Belief Networks" Sustainability 13, no. 4: 1699. https://doi.org/10.3390/su13041699

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