Wearable Biosensors and AI Analytics for Continuous Health Monitoring and Early Disease Detection
DOI:
https://doi.org/10.59675/E225Keywords:
Wearable biosensors; Flexible electronics; Nanomaterials; Graphene-based sensors; Continuous health monitoring.Abstract
The advent of wearable biosensor technologies is a paradigm shift in healthcare provision, as this technology allows continuous physiological monitoring beyond the scope of routine clinical practice, and allows the development of disease conditions through real time analysis of the data. This paper looks at how flexible electronics, advanced nanomaterials, and artificial intelligence analytics can work together to create holistic health monitoring systems that can identify the slightest physiological variations that signify the development of pathological conditions. It has been a multi-stage study that included development of flexible sensor arrays using graphene-based nanomaterials and conducting polymers, application of machine learning algorithms to real-time (physiological) signal processing, and biosensor systems integrated with telemedicine platforms to manage patients remotely. The study proves that fabricated flexible biosensor arrays of screen-printed polyimide substrate can be used with mechanical flexibility greater than 10,000 bending cycles without losing electrochemical functionality within 5 percent of their initial performance. A set of nanomaterial-based electrodes with reduced graphene oxide showed the sensitivity enhancement of 340 percent relative to the usual metallic electrodes in detecting biomarkers in physiological fluids. The accuracy of disease state classification of machine learning models trained on continuous data through biosensors was 91.7% of cardiovascular anomalies and 87.3% of metabolic dysfunction, with an average of 72 hours ago before clinical symptoms appear. It was integrated with telemedicine systems which allowed real-time data transfer, automatic triggering of alerts in case of abnormal physiological behavior, and remote physician consultation leading to after-effects of 43% less emergency department visits in monitored patients. The economic analysis shows that a sustained monitoring by the use of wearable biosensors would save healthcare expenditures by about 2840 per patient every year based on early detection, low hospitalization, and better medication therapies. Implementation is however characterized by very serious challenges such as regulatory compliance requirements with reference to certification of medical devices, data security issues pertaining to the constant delivery of sensitive health information, patient acceptance issues, and technical constraint to multi-analyte ability of sensing. The results show that wearable biosensor systems in combination with artificial intelligence analytics could be a valid direction towards personalized, proactive healthcare provision, but their successful implementation should pay close attention to regulatory frameworks, cybersecurity efforts, and clinical validation guidelines to ensure patient safety and data quality.
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