Role of deep learning in cognitive healthcare: Wearable signal analysis, algorithms, benefits, and challenges^☆

Abstract

Deep Learning (DL) offers promising solutions for analyzing wearable signals and gaining valuable insights into cognitive disorders. While previous review studies have explored various aspects of DL in cognitive healthcare, there remains a lack of comprehensive analysis that integrates wearable signals, data processing techniques, and the broader applications, benefits, and challenges of DL methods. Addressing this limitation, our study provides an extensive review of DL's role in cognitive healthcare, with a particular emphasis on wearables, data processing, and the inherent challenges in this field. This review also highlights the considerable promise of DL approaches in addressing a broad spectrum of cognitive issues. By enhancing the understanding and analysis of wearable signal modalities, DL models can achieve remarkable accuracy in cognitive healthcare. Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Long Short-term Memory (LSTM) networks have demonstrated improved performance and effectiveness in the early diagnosis and progression monitoring of neurological disorders. Beyond cognitive impairment detection, DL has been applied to emotion recognition, sleep analysis, stress monitoring, and neurofeedback. These applications lead to advanced diagnosis, personalized treatment, early intervention, assistive technologies, remote monitoring, and reduced healthcare costs. Nevertheless, the integration of DL and wearable technologies presents several challenges, such as data quality, privacy, interpretability, model generalizability, ethical concerns, and clinical adoption. These challenges emphasize the importance of conducting future research in areas such as multimodal signal analysis and explainable AI. The findings of this review aim to benefit clinicians, healthcare professionals, and society by facilitating better patient outcomes in cognitive healthcare.

Keywords

Cognitive healthcare / Deep learning / Wearable sensor / Convolutional neural network / Recurrent neural network

CRediT authorship contribution statement

Md. Sakib Bin Alam: Writing - original draft, Visualization, Formal analysis, Conceptualization. Aiman Lameesa: Writing - original draft, Visualization, Investigation. Senzuti Sharmin: Writing - original draft, Formal analysis. Shaila Afrin: Writing - original draft, Investigation. Shams Forruque Ahmed: Writing - review & editing, Supervision, Methodology, Conceptualization. Mohammad Reza Nikoo: Writing - review & editing, Software, Resources. Amir H. Gandomi: Writing - review & editing, Visualization, Supervision.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

The authors highly express their gratitude to the Asian Institute of Technology, Khlong Nueng, Thailand for their support in carrying out this study.

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