DeTrAs: deep learning-based healthcare framework for IoT-based assistance of Alzheimer patients
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S.I. : DATA FUSION IN THE ERA OF DATA SCIENCE
DeTrAs: deep learning-based healthcare framework for IoT-based assistance of Alzheimer patients Sumit Sharma1 • Rajan Kumar Dudeja1 • Gagangeet Singh Aujla2 Neeraj Kumar3
•
Rasmeet Singh Bali1
•
Received: 1 July 2020 / Accepted: 2 September 2020 The Author(s) 2020
Abstract Healthcare 4.0 paradigm aims at realization of data-driven and patient-centric health systems wherein advanced sensors can be deployed to provide personalized assistance. Hence, extreme mentally affected patients from diseases like Alzheimer can be assisted using sophisticated algorithms and enabling technologies. Motivated from this fact, in this paper, DeTrAs: Deep Learning-based Internet of Health Framework for the Assistance of Alzheimer Patients is proposed. DeTrAs works in three phases: (1) A recurrent neural network-based Alzheimer prediction scheme is proposed which uses sensory movement data, (2) an ensemble approach for abnormality tracking for Alzheimer patients is designed which comprises two parts: (a) convolutional neural network-based emotion detection scheme and (b) timestamp window-based natural language processing scheme, and (3) an IoT-based assistance mechanism for the Alzheimer patients is also presented. The evaluation of DeTrAs depicts almost 10–20% improvement in terms of accuracy in contrast to the different existing machine learning algorithms. Keywords Alzheimer disease Convolution neural network Internet of Health Naive Bayes Recurrent neural network
1 Introduction The transition of conventional healthcare systems to a datadriven and patient-centric healthcare 4.0 has initiated a pragmatic change in the health statistics [1]. The adaption of cutting-edge technologies (Internet of things, body area networks) which are driven by sophisticated data-driven algorithms (machine learning, deep learning) has supported this change in healthcare systems equipped with smart devices (wearable devices, sensors, medical gadgets) [2]. This transition has lowered the fatality rate of populations and has increased average life expectancy. The healthcare 4.0 ecosystem works largely on two foundations: (1) patients (physical world) and (2) cloud or edge/fog-enabled & Gagangeet Singh Aujla [email protected]; [email protected] 1
Chandigarh University, Mohali, India
2
Newcastle University, Newcastle Upon Tyne, UK
3
Thapar Institute of Engineering and Technology, Patiala, India
algorithms and autonomous systems (virtual world) [3]. This modern healthcare system relies heavily on the crossorganizational services which tend to promote personalization and individual healthcare assistance and support using big data analytic [4]. The enabling technologies, i.e., data analytics and recommender systems, have a vast research potential in context of healthcare systems. Due to this transformation, the personalized recommendations can be provided to the patients suffering from various diseases using the enabling technolo
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