A Predictive Framework for Real-Time Health Monitoring via Wearable Biosensors DOI

Duru İbişağaoğlu

Next frontier., Год журнала: 2024, Номер 8(1), С. 123 - 123

Опубликована: Ноя. 14, 2024

Wearable biosensors, coupled with predictive analytics, are transforming real-time health monitoring by providing continuous, personalized insights into physiological metrics. This framework leverages wearable technology and advanced machine learning algorithms to process biometric data, enabling early detection of anomalies proactive intervention. Through the integration biosensors parameters such as heart rate, blood pressure, glucose levels, respiratory can identify patterns indicative potential risks. Machine analyze data streams, facilitating precise predictions feedback users healthcare providers. approach not only enhances patient engagement preventive care but also supports management chronic conditions allowing continuous tracking outside clinical settings. Despite its potential, challenges privacy, battery life limitations, accuracy sensor must be addressed. research explores design, functionality, implications a framework, examining role in solutions.

Язык: Английский

Integration of Functional Materials in Photonic and Optoelectronic Technologies for Advanced Medical Diagnostics DOI Creative Commons
Naveen Thanjavur,

Laxmi Bugude,

Young‐Joon Kim

и другие.

Biosensors, Год журнала: 2025, Номер 15(1), С. 38 - 38

Опубликована: Янв. 10, 2025

Integrating functional materials with photonic and optoelectronic technologies has revolutionized medical diagnostics, enhancing imaging sensing capabilities. This review provides a comprehensive overview of recent innovations in materials, such as quantum dots, perovskites, plasmonic nanomaterials, organic semiconductors, which have been instrumental the development diagnostic devices characterized by high sensitivity, specificity, resolution. Their unique optical properties enable real-time monitoring biological processes, advancing early disease detection personalized treatment. However, challenges material stability, reproducibility, scalability, environmental sustainability remain critical barriers to their clinical translation. Breakthroughs green synthesis, continuous flow production, advanced surface engineering are addressing these limitations, paving way for next-generation tools. article highlights transformative potential interdisciplinary research overcoming emphasizes importance sustainable scalable strategies harnessing diagnostics. The ultimate goal is inspire further innovation field, enabling creation practical, cost-effective, environmentally friendly solutions.

Язык: Английский

Процитировано

2

Sustainable and scalable detection: Paper-based analytical devices and miniaturized detection systems for modern diagnostics DOI

Ahmed Isa,

Mahdi Gharibi,

Ahmet Çetinkaya

и другие.

Microchemical Journal, Год журнала: 2025, Номер unknown, С. 113210 - 113210

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

1

Wearable MOF Biosensors: A New Frontier in Real-Time Health Monitoring DOI
Navid Rabiee

TrAC Trends in Analytical Chemistry, Год журнала: 2025, Номер unknown, С. 118156 - 118156

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0

Metal-organic framework-based materials: From synthesis and characterization routes to electrochemical sensing applications DOI
Gullit Deffo, Arnaud Kamdem Tamo, Cyrille Ghislain Fotsop

и другие.

Coordination Chemistry Reviews, Год журнала: 2025, Номер 536, С. 216680 - 216680

Опубликована: Апрель 10, 2025

Язык: Английский

Процитировано

0

A Predictive Framework for Real-Time Health Monitoring via Wearable Biosensors DOI

Duru İbişağaoğlu

Next frontier., Год журнала: 2024, Номер 8(1), С. 123 - 123

Опубликована: Ноя. 14, 2024

Wearable biosensors, coupled with predictive analytics, are transforming real-time health monitoring by providing continuous, personalized insights into physiological metrics. This framework leverages wearable technology and advanced machine learning algorithms to process biometric data, enabling early detection of anomalies proactive intervention. Through the integration biosensors parameters such as heart rate, blood pressure, glucose levels, respiratory can identify patterns indicative potential risks. Machine analyze data streams, facilitating precise predictions feedback users healthcare providers. approach not only enhances patient engagement preventive care but also supports management chronic conditions allowing continuous tracking outside clinical settings. Despite its potential, challenges privacy, battery life limitations, accuracy sensor must be addressed. research explores design, functionality, implications a framework, examining role in solutions.

Язык: Английский

Процитировано

0