Development of a wearable biosensor for real-time cardiovascular health monitoring

Thesis Abstract

The prevalence of cardiovascular diseases (CVDs) remains a leading cause of morbidity and mortality worldwide, necessitating innovative approaches for continuous health monitoring to facilitate early detection and timely intervention. Despite advancements in medical diagnostics, existing monitoring systems are often limited by their invasiveness, high cost, and inability to provide real-time data outside clinical settings. This study aims to develop a wearable biosensor capable of real-time cardiovascular health monitoring, with a focus on physiological signals such as heart rate, blood oxygen levels, blood pressure, and electrocardiographic parameters. The specific objectives include designing and fabricating a cost-effective, flexible biosensor integrated with wireless communication modules; evaluating its accuracy and reliability against standard clinical devices; and assessing its usability and acceptance among target users. A mixed-methods research design was adopted, combining quantitative experimental validation with qualitative usability assessments. The population comprised 300 adult participants aged 30-65 years, representing diverse demographic and health backgrounds, recruited from urban healthcare centers. A stratified random sampling technique was employed to select a representative sample, with 150 participants designated for device validation and 150 for usability testing. Data collection instruments included the biosensor prototype, clinical reference devices (standard ECG, sphygmomanometer, pulse oximeter), structured questionnaires, and interview guides. The biosensor's performance was assessed through statistical analyses using regression analysis, Bland-Altman plots, and paired t-tests to determine accuracy, sensitivity, and specificity relative to clinical standards. Usability and acceptance data were analyzed thematically, employing NVivo software for qualitative data. Expected findings indicate that the developed biosensor will demonstrate high concordance (correlation coefficients exceeding 0.85) with clinical reference devices, with minimal mean differences observed in Bland-Altman analysis, thus confirming its accuracy and reliability. The device is anticipated to achieve a sensitivity of over 90% in detecting cardiac irregularities and provide continuous data streams with a latency of less than five seconds, enabling prompt response. Usability assessments are expected to reveal high acceptability scores, adaptability, and perceived ease of use among diverse user groups. The contribution of this research to knowledge includes providing an innovative, integrated wearable biosensor platform tailored for continuous cardiovascular monitoring in non-clinical settings, bridging gaps identified in prior studies concerning device affordability, accuracy, and user-friendliness. The theoretical framework of the Health Belief Model and the Technology Acceptance Model underpin the analysis of user engagement and acceptance, offering insights into behavioral factors influencing device adoption. The main conclusion emphasizes that the biosensor prototype holds significant potential for empowering individuals with real-time cardiovascular health insights, thereby enhancing preventive care and reducing hospital admissions. Recommendations include broader field testing across different populations, integration with telemedicine platforms, and policy development for regulatory standards. Future research should focus on long-term studies to evaluate device durability, data security, and integration into existing health information systems to facilitate large-scale deployment and personalized health management.