Development of a wearable biosensor for real-time autonomic nervous system monitoring

Thesis Abstract

The autonomic nervous system (ANS) plays a critical role in maintaining physiological homeostasis and responding to environmental stimuli, yet real-time, continuous monitoring of its dynamic functions remains a significant challenge due to limitations in existing technology. This study aims to develop a wearable biosensor capable of real-time autonomic nervous system monitoring, thereby facilitating early detection of dysregulation associated with stress, cardiovascular conditions, and other stress-related disorders. The specific objectives include designing and fabricating a multimodal biosensor integrating electrochemical, photoplethysmographic, and galvanic skin response sensors; validating its performance through laboratory and clinical trials; and developing an analytical framework utilizing machine learning algorithms to interpret biosensor data in relation to autonomic activity. The research adopts a mixed-methods approach, combining quantitative experimental design with qualitative assessments to optimize sensor functionality and user acceptability. The study population comprises 150 adult participants aged 18 to 65 years, recruited via stratified random sampling from a metropolitan health clinic. Data collection involves deploying the biosensor prototypes on participants over a continuous 48-hour period, capturing physiological signals including electrodermal activity, heart rate variability, and skin temperature. These signals are analyzed using advanced statistical techniques such as multivariate regression analysis and support vector machine (SVM) classifiers to identify patterns indicative of sympathetic and parasympathetic dominance. Calibration procedures adhere to standardized guidelines, and sensor validity is ensured through concurrent comparison with gold-standard biomedical equipment. Key expected findings suggest that the integrated biosensor can reliably detect fluctuations in autonomic activity with a sensitivity of over 85% and specificity of 80% when compared to traditional monitoring methods. The device's ability to provide near real-time feedback is anticipated to enable precise tracking of stress episodes and autonomic imbalance, contributing valuable insights into individual physiological responses under various emotional and physical conditions. The data analysis is expected to reveal significant correlations between biosensor outputs and established measures of autonomic function, supporting the development of predictive models for health risk assessment. This study makes a notable contribution to knowledge by advancing portable, user-friendly biosensing technology specifically tailored for continuous autonomic monitoring in naturalistic settings. It integrates interdisciplinary concepts from physiology, biomedical engineering, and artificial intelligence to offer a novel solution that enhances the understanding of autonomic regulation outside laboratory environments. The findings will inform the design of personalized health interventions and contribute to the scientific discourse on non-invasive autonomic assessment, validating theoretical models such as the Neurovisceral Integration Model and the Polyvagal Theory within a technological context. The main conclusion underscores the potential of wearable biosensors to revolutionize autonomic health monitoring by providing accurate, real-time data that can inform preventive strategies and clinical decision-making. Recommendations emphasize further development of sensor miniaturization, integration with mobile health platforms, and large-scale longitudinal studies to establish broader clinical applicability. Future research should explore adapting the biosensor for pediatric populations and remote monitoring in underserved areas. Overall, the study advances the field of physiological monitoring by bridging the gap between complex biomarker analysis and accessible wearable technology, offering a scalable solution for enhancing health outcomes through continuous autonomic assessment.