D-SCAPE-v2 represents the culmination of four years of study at Imperial College London, serving as the final year master’s project in Electronic and Information Engineering. This sophisticated medical wearable addresses the critical challenge of monitoring dengue fever patients, providing continuous vital sign tracking and predictive analytics to enable early intervention before complications arise.
Dengue fever affects millions globally, with severe cases potentially progressing to dengue hemorrhagic fever or dengue shock syndrome. Early detection of deterioration proves crucial for patient outcomes, but traditional monitoring approaches rely on periodic manual measurements that may miss critical transitions. D-SCAPE-v2 solves this problem through continuous, automated patient monitoring with intelligent alert generation.
The device architecture integrates multiple sensor modalities to capture comprehensive physiological data. Photoplethysmography sensors measure heart rate and blood oxygen saturation, providing insight into cardiovascular function. Temperature sensors track body temperature fluctuations characteristic of dengue progression. Accelerometers enable activity monitoring and sleep pattern analysis, important for understanding patient recovery trajectories.
Signal processing algorithms extract reliable vital sign measurements from noisy sensor data. Advanced filtering techniques remove motion artifacts while preserving physiological signals. Heart rate variability analysis provides additional indicators of patient status beyond simple rate measurements. Temperature compensation ensures accurate readings across varying environmental conditions.
The embedded firmware implements efficient power management strategies essential for extended wearable operation. Adaptive sampling rates balance measurement frequency against power consumption, increasing sampling during critical periods while conserving energy during stable conditions. Low-power wireless communication protocols transmit data to monitoring stations without excessive battery drain.
Machine learning integration enables predictive analytics, analyzing patterns in collected data to identify early warning signs of patient deterioration. The system learns normal patterns for individual patients, triggering alerts when deviations suggest developing complications. This proactive approach enables intervention before clinical deterioration becomes obvious through conventional assessment.
Data visualization interfaces present clinician-friendly representations of patient status and trends. Real-time dashboards display current vital signs alongside historical trends, enabling quick assessment of patient trajectory. Alert management systems prioritize notifications based on urgency, ensuring critical warnings receive immediate attention while avoiding alarm fatigue.
The project achieved significant recognition, winning the regional category (Europe, Middle East, Africa) at the International Symposium on Circuits and Systems (ISCAS) 2023. This prestigious competition evaluates innovative circuit and system designs from institutions worldwide, with D-SCAPE-v2 selected for its novel approach to continuous patient monitoring and potential clinical impact.
Development involved extensive prototyping and testing cycles, validating sensor accuracy against clinical-grade equipment. Usability studies with medical professionals refined interface design and alert logic. Regulatory compliance research informed design choices around patient safety and data security.
The complete system demonstrates successful integration of multiple engineering disciplines including analog circuit design for sensor interfaces, digital signal processing for data analysis, embedded systems programming for device firmware, wireless communication protocols for data transmission, and machine learning for predictive analytics.
Documentation includes comprehensive technical reports detailing system architecture, algorithm implementations, validation studies, and future development roadmap. Source code and hardware designs enable reproduction and extension of the work by other researchers.
This project showcases ability to undertake complex, multi-disciplinary engineering challenges with real-world impact. The combination of technical excellence and practical clinical applicability demonstrates readiness for professional engineering work addressing important societal challenges through innovative technology solutions.