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

Sharma, Sumit; Dudeja, Rajan Kumar; Aujla, Gagangeet Singh; Bali, Rasmeet Singh; Kumar, Neeraj

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.

Citation

Sharma, S., Dudeja, R. K., Aujla, G. S., Bali, R. S., & Kumar, N. (2020). DeTrAs: deep learning-based healthcare framework for IoT-based assistance of Alzheimer patients. Neural Computing and Applications, https://doi.org/10.1007/s00521-020-05327-2

• Journal Article Type: Article
• Online Publication Date: Sep 17, 2020
• Publication Date: Jan 1, 2020
• Deposit Date: Apr 27, 2021
• Journal: Neural Computing and Applications
• Print ISSN: 0941-0643
• Electronic ISSN: 1433-3058
• Publisher: Springer
• Peer Reviewed: Peer Reviewed
• DOI: https://doi.org/10.1007/s00521-020-05327-2
• Public URL: https://durham-repository.worktribe.com/output/1243710