Secure cognitive health monitoring using a directed acyclic graph-based and AI-enhanced IoMT framework^☆

Abstract

Millions of people throughout the world struggle with mental health disorders, but the widespread stigma associated with these issues often prevents them from seeking treatment. We propose a novel strategy that integrates Internet of Medical Things (IoMT), DAG-based hedera technology, and Artificial Intelligence (AI) to overcome these challenges. We also consider the costs of chronic diseases such as Parkinson's and Alzheimer's, which often require 24-hour care. Using smart monitoring tools coupled with AI algorithms that can detect early indicators of deterioration, our system aims to provide low-cost, continuous support. Since IoMT data is large in volume, we need a blockchain network with high transaction throughput without compromising the privacy of patient data. To address this concern, we propose to use Hedera technology to ensure the privacy, and security of personal mental health information, scalability and a faster transaction confirmation rate. Overall, this research paper outlines a holistic approach to mental health monitoring that respects privacy, promotes accessibility, and harnesses the potential of emerging technologies. By combining IoMT, Hedera, and AI, we offer a solution that helps break down the barriers preventing individuals from seeking mental well-being support. Furthermore, comparative analysis shows that our best-performing ML models achieve an accuracy of around 98%, which is more than 30% better than traditional models such as logistic regression.

Keywords

Blockchain / Machine learning / Mental health monitoring

CRediT authorship contribution statement

Shashank Srivastava: Writing - original draft, Methodology, Investigation. Kartikeya Kansal: Visualization, Resources, Methodology. Siva Sai: Writing - review & editing, Validation, Supervision, Resources, Methodology, Conceptualization. Vinay Chamola: Writing - review & editing, 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

This work was supported by CHANAKYA Fellowship Program of TIH Foundation for IoT & IoE (TIH-IoT) received by Dr. Vinay Chamola under Project Grant File CFP/2022/027.

References

[1] S. Sai, V. Chamola, K.-K.R. Choo, B. Sikdar, J.J. Rodrigues, Confluence of blockchain and artificial intelligence technologies for secure and scalable healthcare solutions: a review, IEEE Int. Things J. 10 (7) (2023) 5873-5897.

[2] S. Sai, V. Hassija, V. Chamola, M. Guizani, Federated learning and nft-based privacy-preserving medical data sharing scheme for intelligent diagnosis in smart healthcare, IEEE Int. Things J. 11 (4) (2024) 5568-5577.

[3] A. Mittal, L. Dumka, L. Mohan,A comprehensive review on the use of artificial intel-ligence in mental health care, in:2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT), 2023, pp. 1-5.

[4] A. Balasundaram, S. Routray, A.V. Prabu, P. Krishnan, P.P. Malla, M. Maiti, In-ternet of things (iot)-based smart healthcare system for efficient diagnostics of health parameters of patients in emergency care, IEEE Int. Things J. 10 (21) (2023) 18563-18570, https://doi.org/10.1109/JIOT.2023.3246065.

[5] M. Seliem, K. Elgazzar,Biomt: blockchain for the Internet of medical things, in: 2019 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom), IEEE, 2019, pp. 1-4.

[6] A. Cullen, P. Ferraro, W. Sanders, L. Vigneri, R. Shorten, Access control for dis-tributed ledgers in the Internet of Things: a networking approach, IEEE Int. Things J. 9 (3) (2021) 2277-2292.

[7] D. Dolenc, J. Turk, M. Pustišek,Distributed ledger technologies for iot and business dapps, in:2020 International Conference on Broadband Communications for Next Generation Networks and Multimedia Applications (CoBCom), 2020, pp. 1-8.

[8] C. Jayapal, A. Evr, G. Adithya, R. R, A.S. M,Certificate authenticity verification using hashgraph, in:2023 2nd International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), 2023, pp. 1-5.

[9] S.K. Singh, S. Rathore, J.H. Park, Blockiotintelligence: a blockchain-enabled intel-ligent iot architecture with artificial intelligence, Future Gener. Comput. Syst. 110 (2020) 721-743.

[10] Y. Chang, C. Fang, W. Sun, A blockchain-based federated learning method for smart healthcare, Comput. Intell. Neurosci. 24 (2021) 4376418.

[11] M. Bhandary, M. Parmar, D. Ambawade, A blockchain solution based on directed acyclic graph for iot data security using iota tangle, in: 2020 5th International Con-ference on Communication and Electronics Systems (ICCES), 2020, pp. 827-832.

[12] M.A. Uddin, A. Stranieri, I. Gondal, V. Balasubramanian,A decentralized patient agent controlled blockchain for remote patient monitoring, in: 2019 International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), IEEE, 2019, pp. 1-8.

[13] M.A.H. Wadud, T.A.-U.-H. Bhuiyan, M.A. Uddin, M.M. Rahman,A patient centric agent assisted private blockchain on hyperledger fabric for managing remote pa-tient monitoring, in: 2020 11th International Conference on Electrical and Computer Engineering (ICECE), IEEE, 2020, pp. 194-197.

[14] P. Sajda, Machine learning for detection and diagnosis of disease, Annu. Rev. Biomed. Eng. 8 (2006) 537-565.

[15] C. Iwendi, S. Khan, J.H. Anajemba, A.K. Bashir, F. Noor, Realizing an efficient iomt-assisted patient diet recommendation system through machine learning model, IEEE Access 8 (2020) 28462-28474.

[16] C. Mistry, U. Thakker, R. Gupta, M.S. Obaidat, S. Tanwar, N. Kumar, J.J. Rodrigues,Medblock: an ai-enabled and blockchain-driven medical healthcare system for covid- 19, in: ICC 2021-IEEE International Conference on Communications, IEEE, 2021, pp. 1-6.

[17] G. Bansal, V. Chamola, P. Narang, S. Kumar, S. Raman, Deep3dscan: deep residual network and morphological descriptor based framework forlung cancer classification and 3d segmentation, IET Image Process. 14 (7) (2020) 1240-1247.

[18] N. Wu, B. Green, X. Ben, S. O’Banion, Deep transformer models for time series fore-casting: the influenza prevalence case, arXiv :2001.08317, 2020.

[19] J. McQuire, P. Watson, N. Wright, H. Hiden, M. Catt, A data efficient vision trans-former for robust human activity recognition from the spectrograms of wearable sensor data, in: 2023 IEEE Statistical Signal Processing Workshop (SSP), 2023, pp. 364-368.

[20] G. Sasubilli, A. Kumar,Machine learning and big data implementation on health care data, in:2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS), 2020, pp. 859-864.

[21] C. Anu, K.P. Muhammad Rasi, N. Salih, P. Nanditha, V. Rajeevan,Health monitoring system using iot with machine learning, in:2023 9th International Conference on Smart Computing and Communications (ICSCC), 2023, pp. 529-534.

[22] Navita P. Mittal,Machine learning (ml) based human activity recognition model using smart sensors in iot environment,in:2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence), 2022, pp. 330-334.

[23] Teach, learn, and make with the raspberry pi foundation, https://www.raspberrypi.org/. (Accessed 16 February 2023).

[24] S. Sai, V. Chamola, AI-assisted blockchain-enabled smart and secure e-prescription management framework, ACM Trans. Internet Technol. (2024), https://doi.org/10.1145/3641279.

[25] V.N. Vasu, R. Surendran, M.S. Saravanan, N. Madhusundar,Prediction of defective products using logistic regression algorithm against linear regression algorithm for better accuracy, in:2022 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT), 2022, pp. 161-166.

[26] G. Biau, E. Scornet, A random forest guided tour, Test 25 (2016) 197-227.

[27] S. Oehmcke, O. Zielinski, O.Kramer, knn ensembles with penalized dtw for multi-variate time series imputation, in: 2016 International Joint Conference on Neural Networks (IJCNN), IEEE, 2016, pp. 2774-2781.

[28] H. Deng, G. Runger, E. Tuv, M. Vladimir, A time series forest for classification and feature extraction, Inf. Sci. 239 (2013) 142-153.

[29] J.O. Berle, E.R. Hauge, K.J. Oedegaard, F. Holsten, O.B. Fasmer, Actigraphic registra-tion of motor activity reveals a more structured behavioural pattern in schizophrenia than in major depression, BMC Res. Notes 3 (1) (2010) 1-7.

[30] J.C. Ong, J.T. Arnedt, P.R. Gehrman, Chapter 83 - insomnia diagnosis, assessment, and evaluation, in: M. Kryger, T.Roth, W.C.Dement (Eds.), Principles and Practice of Sleep Medicine, sixth edition, Elsevier, 2017, pp. 785-793.e4.

[31] NCBI, Madrs, https://www.ncbi.nlm.nih.gov/books/NBK409740/6, 2016.

[32] H. Cai, Z. Yuan, Y. Gao, S. Sun, N. Li, F. Tian, H. Xiao, J. Li, Z. Yang, X. Li, et al., A multi-modal open dataset for mental-disorder analysis, Sci. Data 9 (1) (2022) 178.

[33] H.B. Edwards, E. Marques, W. Hollingworth, J. Horwood, M. Farr, E. Bernard, C. Salisbury, K. Northstone,Use of a primary care online consultation system, by whom, when and why: evaluation of a pilot observational study in 36 general practices in south west england, BMJ Open 7 (11) (2017) e016901.

[34] S. Agnisarman, S. Narasimha, K. Chalil Madathil, B. Welch, F. Brinda, A. Ashok, J. McElligott, Toward a more usable home-based video telemedicine system: a heuristic evaluation of the clinician user interfaces of home-based video telemedicine systems, JMIR Hum. Factors 4 (2) (2017) e11.