Arsene Adjevi
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Arsene Adjevi has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Faruk Aktaş
Biomedical Engineering, Kocaeli University, Kocaeli, Türkiye
2 shared publications - Mustafa Hikmet Bilgehan Ucar
Information Systems Engineering, Kocaeli University, Kocaeli, Türkiye
2 shared publications - Serdar Solak
Kocaeli Üniversitesi
2 shared publications - Abdiwahab Mohamed Abdirashid
Information Systems Engineering, Kocaeli University, Kocaeli, Türkiye
1 shared publication
Latest Publications
- 29 January 2026
- Published by Elsevier in Computer Methods and Programs in Biomedicine
- Vol. 278, 109266
Explainable reinforcement learning for glucose monitoring based on shapley value analysis
- 29 January 2026
- Published by Elsevier in Computer Methods and Programs in Biomedicine
- Vol. 278, 109266
Explainable reinforcement learning for glucose monitoring based on shapley value analysis
- Open Access
Utilizing IoMT-Based Smart Gloves for Continuous Vital Sign Monitoring to Safeguard Athlete Health and Optimize Training Protocols
This paper presents the development of a vital sign monitoring system designed specifically for professional athletes, with a focus on runners. The system aims to enhance athletic performance and mitigate health risks associated with intense training regimens. It comprises a wearable glove that monitors key physiological parameters such as heart rate, blood oxygen saturation (SpO2), body temperature, and gyroscope data used to calculate linear speed, among other relevant metrics. Additionally, environmental variables, including ambient temperature, are tracked. To ensure accuracy, the system incorporates an onboard filtering algorithm to minimize false positives, allowing for timely intervention during instances of physiological abnormalities. The study demonstrates the system’s potential to optimize performance and protect athlete well-being by facilitating real-time adjustments to training intensity and duration. The experimental results show that the system adheres to the classical “220-age” formula for calculating maximum heart rate, responds promptly to predefined thresholds, and outperforms a moving average filter in noise reduction, with the Gaussian filter delivering superior performance.
Open AccessUtilizing IoMT-Based Smart Gloves for Continuous Vital Sign Monitoring to Safeguard Athlete Health and Optimize Training Protocols
This paper presents the development of a vital sign monitoring system designed specifically for professional athletes, with a focus on runners. The system aims to enhance athletic performance and mitigate health risks associated with intense training regimens. It comprises a wearable glove that monitors key physiological parameters such as heart rate, blood oxygen saturation (SpO2), body temperature, and gyroscope data used to calculate linear speed, among other relevant metrics. Additionally, environmental variables, including ambient temperature, are tracked. To ensure accuracy, the system incorporates an onboard filtering algorithm to minimize false positives, allowing for timely intervention during instances of physiological abnormalities. The study demonstrates the system’s potential to optimize performance and protect athlete well-being by facilitating real-time adjustments to training intensity and duration. The experimental results show that the system adheres to the classical “220-age” formula for calculating maximum heart rate, responds promptly to predefined thresholds, and outperforms a moving average filter in noise reduction, with the Gaussian filter delivering superior performance.