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

A review of eye-tracking technology and its application in stroke diagnosis and assessment DOI
Jun Zhang, Wei Kong, Ming Ma

и другие.

Measurement, Год журнала: 2025, Номер unknown, С. 117325 - 117325

Опубликована: Март 1, 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