HF-SPHR: Hybrid Features for Sustainable Physical Healthcare Pattern Recognition Using Deep Belief Networks
Madiha Javeed,
Munkhjargal Gochoo,
Ahmad Jalal and
Kibum Kim
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Madiha Javeed: Department of Computer Science, Air University, Islamabad 44000, Pakistan
Munkhjargal Gochoo: Department of Computer Science and Software Engineering, United Arab Emirates University, Al Ain 15551, United Arab Emirates
Ahmad Jalal: Department of Computer Science, Air University, Islamabad 44000, Pakistan
Kibum Kim: Department of Human-Computer Interaction, Hanyang University, Ansan 15588, Korea
Sustainability, 2021, vol. 13, issue 4, 1-28
Abstract:
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.
Keywords: deep belief networks; hybrid-features; restricted Boltzmann machines; sustainable physical healthcare pattern recognition; wearable sensors system (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:13:y:2021:i:4:p:1699-:d:493459
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