Multi-Modal Social Networks with IoT-Enabled Wearable Devices for Healthcare

Main Article Content

Om Prakash
Rajeev Kumar

Abstract

The combination of wearable devices and social networking sites has led to personalized health clinics. Since people are connected, their health is also interconnected. The COVID-19 pandemic has shown how important it is to use social media and wearable devices to track, listen to, interact with, and share important health information. By measuring the user's activity, stress, blood pressure, body temperature, etc., these techniques are always making healthcare better. This research proposes a framework to develop a standardized system using wearable devices and social media platforms for healthcare that will focus on detecting and monitoring chronic diseases like BP, diabetes, mental health, etc. The proposed framework works efficiently on two different interrelated datasets with appreciable accuracy.

Article Details

How to Cite
Prakash, O., & Rajeev Kumar. (2023). Multi-Modal Social Networks with IoT-Enabled Wearable Devices for Healthcare. INFOCOMP Journal of Computer Science, 22(1). Retrieved from https://infocomp.dcc.ufla.br/index.php/infocomp/article/view/2491
Section
Machine Learning and Computational Intelligence
Author Biography

Rajeev Kumar, Jawaharlal Nehru University

Rajeev Kumar Professor of Computer Science, JNU New Delhi http://www.jnu.ac.in/content/rkumarcs

References

Ali, F., El-Sappagh, S., Islam, S. R., Ali, A., Attique, M., Imran, M., and Kwak, K.-S. An intelligent healthcare monitoring framework using wearable sensors and social networking data. Future Generation Computer Systems, 114:23–43, 2021.

Alsagri, H. and Ykhlef, M. Machine learningbased approach for depression detection in twitter

using content and activity features. IEICE Trans. Information & Systems, pages 1825–1832, 03 2020.

Carrillo, D., Nguyen, L. D., Nardelli, P. H., Pournaras, E., Morita, P., Rodríguez, D. Z., Dzaferagic, M., Siljak, H., Jung, A., Hébert-

Dufresne, L., et al. Corrigendum: Containing future epidemics with trustworthy federated systems for ubiquitous warning and response. Frontiers in Communications and Networks, 2:721971, 2021.

Chikersal, P., Doryab, A., Tumminia, M., Villalba, D., Dutcher, J., Liu, X., Cohen, S., Creswell, K., Mankoff, J., Creswell, J., Goel, M., and Dey, A. Detecting depression and predicting its onset using longitudinal symptoms captured by passive

sensing: A machine learning approach with robust feature selection. ACM Trans. Computer-Human Interaction, 28:1–41, 2021.

Ferreira, J. P. B., Junior, F. L., Rosa, R. L., and Rodríguez, D. Z. Evaluation of sentiment and affectivity analysis in a blog recommendation system. In Proceedings of the XVI Brazilian Symposium on Human Factors in Computing Systems, pages 1–9, 2017.

Ji, N., Xiang, T., Bonato, P., Lovell, N. H., Ooi, S.-Y., Clifton, D. A., Akay, M., Ding, X.-R., Yan,

B. P., Mok, V., Fotiadis, D. I., and Zhang, Y.-T. Recommendation to use wearable-based mhealth in closed-loop management of acute cardiovascular disease patients during the covid-19 pandemic. IEEE Journal Biomedical & Health Informatics, 25(4):903–908, 2021.

Khan, W. Z., Arshad, Q.-u.-A., Hakak, S., Khan, M. K., and Saeed-Ur-Rehman. Trust management in social internet of things: Architectures, recent advancements, and future challenges. IEEE Internet of Things Journal, 8(10):7768–7788, 2021.

Kharel, P., Sharma, K., Dhimal, S., and Sharma, S. Early detection of depression and treatment response prediction using machine learning: A review. In Proc. 2nd Int. Conf. Adv. Compu. and Commun. Paradigms (ICACCP), pages 1–7, 2019.

Kotsilieris, T., Pavlaki, A., Christopoulou, S. C., and Anagnostopoulos, I. The impact of social networks on health care. Social Network Analysis & Mining, 7:1–6, 2017.

Kumar, R., Anand, A., Kumar, P., and Kumar, R. K. Internet of things and social media: A review of literature and validation from Twitter analytics. In Proc. Int. Conf. Emerging Smart Comp. and Informatics (ESCI), pages 158–163, 2020.

Okey, O. D., Melgarejo, D. C., Saadi, M., Rosa, R. L., Kleinschmidt, J. H., and Rodríguez, D. Z. Transfer learning approach to ids on cloud iot devices using optimized cnn. IEEE Access, 11:1023–1038, 2023.

PINTO, G. E., Rosa, R. L., and Rodriguez, D. Z. Applications for 5g networks. INFOCOMP Journal of Computer Science, 20(1), 2021.

Prakash, O. and Kumar, R. Fake account detection in social networks with supervised machine learning.

In Proc. Int. Conf. IoT, Intelligent Computing & Security (IICS), pages 287–295. Springer, 2023.

Prakash, O. and Kumar, R. Fake news detection in social networks using attention mechanism. In Proc. Int. Conf. on Cognitive & Intelligence Computing (ICCIC), Vol. 2, pages 453–462. Springer, 2023.

Priya, A., Garg, S., and Tigga, N. P. Predicting anxiety, depression and stress in modern life using

machine learning algorithms. Procedia Computer Science, 167:1258–1267, 2020.

Rahman, M. S. and Reza, H. A systematic review towards big data analytics in social media. Big Data Mining and Analytics, 5(3):228–244, 2022.

Rodriguez, D. Z., de Oliveira, F. M., Nunes, P. H., and de Morais, R. M. A. Wearable devices: Concepts

and applications. INFOCOMP Journal of Computer Science, 18(2), 2019.

Rook, K. S. Social networks in later life: Weighing positive and negative effects on health and well-being. Current Directions in Psychological Science, 24:45–51, 2015.

Rosa, R. L., De Silva, M. J., Silva, D. H., Ayub, M. S., Carrillo, D., Nardelli, P. H., and Rodriguez, D. Z. Event detection system based on user behavior changes in online social networks: Case of the covid-19 pandemic. Ieee Access, 8:158806– 158825, 2020.

Rosa, R. L., Rodriguez, D. Z., and Bressan, G. Sentimeter-br: Facebook and twitter analysis tool

to discover consumers sentiment. AICT 2013, page 72, 2013.

Silva, D. H., Rosa, R. L., and Rodriguez, D. Z. Sentimental analysis of soccer games messages from social networks using userâs profiles. INFOCOMP Journal of Computer Science, 19(1), 2020.

Teodoro, A. A., Gomes, O. S., Saadi, M., Silva, B. A., Rosa, R. L., and Rodríguez, D. Z. An fpgabased performance evaluation of artificial neural network architecture algorithm for iot. Wireless Personal Communications, pages 1–32, 2021.

Teodoro, A. A., Silva, D. H., Rosa, R. L., Saadi, M., Wuttisittikulkij, L., Mumtaz, R. A., and Rodriguez,

D. Z. A skin cancer classification approach using gan and roi-based attention mechanism. Journal of Signal Processing Systems, 95(2-3):211–224, 2023.

Uddin, M. Z., Dysthe, K. K., Følstad, A., and Brandtzaeg, P. B. Deep learning for prediction of depressive symptoms in a large textual dataset. Neural Computing & Applications, 34(1):721– 744, 2022.

Yadav, S., Kaim, T., Gupta, S., Bharti, U., and Priyadarshi, P. Predicting depression from routine survey data using machine learning. In Proc. 2nd Int. Conf. Advances in Comp., Comm. Control & Netw. (ICACCCN), pages 163–168, 2020.