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DeTrAs: deep learning-based healthcare framework for IoT-based assistance of Alzheimer patients

Lookup NU author(s): Dr Gagangeet Aujla, Dr Neeraj Kumar

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).

Abstract

© 2020, The Author(s). 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.

Publication metadata

Author(s): Sharma S, Dudeja RK, Aujla GS, Bali RS, Kumar N

Publication type: Article

Publication status: Published

Journal: Neural Computing and Applications

Year: 2020

Pages: Epub ahead of print

Online publication date: 17/09/2020

Acceptance date: 02/09/2020

Date deposited: 23/06/2022

ISSN (print): 0941-0643

ISSN (electronic): 1433-3058

Publisher: Springer Nature

URL: https://doi.org/10.1007/s00521-020-05327-2

DOI: 10.1007/s00521-020-05327-2

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