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Article

A Novel AI Approach for Assessing Stress Levels in Patients with Type 2 Diabetes Mellitus Based on the Acquisition of Physiological Parameters Acquired during Daily Life

by
Gonçalo Ribeiro
1,2,
João Monge
1,2,
Octavian Postolache
1,2,* and
José Miguel Dias Pereira
3,4
1
Department of Information Science and Technology, Iscte—Instituto Universitário de Lisboa, Av. das Forças Armadas, 1649-026 Lisbon, Portugal
2
Instituto de Telecomunicações (IT), Instituto Superior Técnico, North Tower, 10th Floor, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal
3
Instituto de Telecomunicações, 3810-193 Aveiro, Portugal
4
Instituto Politécnico de Setúbal, Escola Superior de Tecnologia de Setúbal, 2910-761 Setúbal, Portugal
*
Author to whom correspondence should be addressed.
Sensors 2024, 24(13), 4175; https://doi.org/10.3390/s24134175
Submission received: 25 April 2024 / Revised: 23 June 2024 / Accepted: 24 June 2024 / Published: 27 June 2024
(This article belongs to the Special Issue Non-contact Sensing Technologies for Motion Analysis and Health Monitoring)
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Abstract

Stress is the inherent sensation of being unable to handle demands and occurrences. If not properly managed, stress can develop into a chronic condition, leading to the onset of additional chronic health issues, such as cardiovascular illnesses and diabetes. Various stress meters have been suggested in the past, along with diverse approaches for its estimation. However, in the case of more serious health issues, such as hypertension and diabetes, the results can be significantly improved. This study presents the design and implementation of a distributed wearable-sensor computing platform with multiple channels. The platform aims to estimate the stress levels in diabetes patients by utilizing a fuzzy logic algorithm that is based on the assessment of several physiological indicators. Additionally, a mobile application was created to monitor the users’ stress levels and integrate data on their blood pressure and blood glucose levels. To obtain better performance metrics, validation experiments were carried out using a medical database containing data from 128 patients with chronic diabetes, and the initial results are presented in this study.
Keywords: blood glucose monitoring; fuzzy logic; mobile application; photoplethysmography; physiological parameters extraction; stress assessment; wearable devices blood glucose monitoring; fuzzy logic; mobile application; photoplethysmography; physiological parameters extraction; stress assessment; wearable devices

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MDPI and ACS Style

Ribeiro, G.; Monge, J.; Postolache, O.; Pereira, J.M.D. A Novel AI Approach for Assessing Stress Levels in Patients with Type 2 Diabetes Mellitus Based on the Acquisition of Physiological Parameters Acquired during Daily Life. Sensors 2024, 24, 4175. https://doi.org/10.3390/s24134175

AMA Style

Ribeiro G, Monge J, Postolache O, Pereira JMD. A Novel AI Approach for Assessing Stress Levels in Patients with Type 2 Diabetes Mellitus Based on the Acquisition of Physiological Parameters Acquired during Daily Life. Sensors. 2024; 24(13):4175. https://doi.org/10.3390/s24134175

Chicago/Turabian Style

Ribeiro, Gonçalo, João Monge, Octavian Postolache, and José Miguel Dias Pereira. 2024. "A Novel AI Approach for Assessing Stress Levels in Patients with Type 2 Diabetes Mellitus Based on the Acquisition of Physiological Parameters Acquired during Daily Life" Sensors 24, no. 13: 4175. https://doi.org/10.3390/s24134175

APA Style

Ribeiro, G., Monge, J., Postolache, O., & Pereira, J. M. D. (2024). A Novel AI Approach for Assessing Stress Levels in Patients with Type 2 Diabetes Mellitus Based on the Acquisition of Physiological Parameters Acquired during Daily Life. Sensors, 24(13), 4175. https://doi.org/10.3390/s24134175

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