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.

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Blood glucose monitoring,Fuzzy logic,Mobile application,Photoplethysmography,Physiological parameters extraction,Stress assessment,Wearable devices

• Computer and Information Sciences - Natural Sciences
• Physical Sciences - Natural Sciences
• Chemical Sciences - Natural Sciences
• Biological Sciences - Natural Sciences
• Other Engineering and Technology Sciences - Engineering and Technology
• Electrical Engineering, Electronic Engineering, Information Engineering - Engineering and Technology
• Clinical Medicine - Medical and Health Sciences
• Other Medical Sciences - Medical and Health Sciences