Wearable Optic-Fiber Sensor for Cardiovascular Health Monitoring
Cardiovascular health monitoring has become a key focus in medical research due to the rising prevalence of heart-related diseases. Traditional methods often lack continuous monitoring capabilities, creating a need for advanced wearable devices. A newly proposed wearable optic-fiber sensor, designed with a W-shaped microfiber encapsulated in PDMS, offers high sensitivity and weak pressure sensing for real-time pulse monitoring. This innovative approach not only enhances early detection of cardiovascular risks but also promotes preventive healthcare strategies in healthy individuals.
Sensor Design and Working Principle
The proposed sensor integrates a W-shaped microfiber into a polydimethylsiloxane (PDMS) film, optimizing the structure for high sensitivity to small pressure variations. By effectively transmitting optical signals in response to pressure changes, the sensor captures detailed pulse characteristics. Its compact design makes it suitable for wearable applications without compromising user comfort. The structural optimization ensures reliable detection of pulse waveform changes under varying physiological conditions.
Sensitivity and Performance Evaluation
One of the major achievements of this research is the significant pressure sensitivity demonstrated by the sensor. In the low-pressure range of 0–0.1 N, the sensor exhibits a sensitivity of 38.77 nm/N, while in the range of 0–0.15 N, the voltage sensitivity reaches 14.18 V/N. These results reflect the sensor’s ability to detect even the slightest variations in pulse pressure, which is critical for accurate cardiovascular monitoring. Such performance establishes a strong foundation for clinical and wearable device applications.
Pulse Waveform Monitoring and Application
The sensor has been successfully tested at various positions on the human body, where it displays clear pulse waveforms. Beyond waveform capture, it provides accurate measurements of pulse rate across individuals with different Body Mass Index (BMI) categories. This adaptability makes the sensor valuable in personalized healthcare, enabling tailored monitoring for individuals based on their physical attributes and health status.
Machine Learning Integration for Pulse Classification
To enhance the diagnostic capabilities, machine learning algorithms were applied to classify wrist pulse samples. The constructed models achieved high classification accuracy, demonstrating the potential of combining optic-fiber sensing with advanced computational methods. This integration paves the way for intelligent healthcare systems, capable of not only monitoring physiological signals but also interpreting them for early diagnosis and prediction of cardiovascular diseases.
Future Research and Application Prospects
The development of this wearable optic-fiber sensor represents an important step toward personalized medicine and intelligent healthcare. Future research can focus on integrating wireless communication, miniaturization, and multi-parameter monitoring to create comprehensive health tracking systems. With its proven high sensitivity and machine learning-based classification, this sensor shows promising potential for clinical applications, preventive healthcare, and next-generation wearable devices.
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