Overview

This project involved the conceptual design of a wearable BioMEMS-inspired system aimed at predicting and preventing habit relapse through continuous physiological monitoring. Conducted over the course of an academic semester, the work emphasized research-driven system design and feasibility analysis rather than fabrication, integrating principles from BioMEMS, microfluidics and neural signal monitoring.

The proposed system explored how miniature biosensing technologies could be embedded into everyday wearables to enable early detection of relapse risk and support proactive, user-centered behavioral intervention.

Project Type: Academic BioMEMS Design Project
Duration: 4 months (1 academic semester)
Focus Areas: BioMEMS · Microfluidics · Biomedical Device Design · Signal Processing
Role: Concept development, system-level design contribution, literature research and feasibility analysis

Motivation

Habit relapse is often preceded by subtle physiological and neurological changes associated with stress and cognitive load. Conventional monitoring approaches are typically intrusive, episodic or impractical for long-term use.

The objective of this project was to conceptualize a discreet, wearable biomedical system capable of continuously monitoring stress-related biomarkers and neural activity to anticipate relapse risk and enable early, user-centered intervention strategies.

Conceptual System Architecture

The proposed system was explored as a smart eyewear platform, integrating multiple sensing modalities into a familiar and socially acceptable form factor. The emphasis was placed on system-level integration and technical feasibility rather than detailed mechanical or microfabricated design.

Key conceptual components included:

  • EEG sensing concepts integrated into eyeglass frames to monitor prefrontal neural activity
  • A microfluidic tear-analysis approach for detecting stress-related biomarkers (e.g., cortisol)
  • On-device signal conditioning and feature extraction concepts
  • Wireless communication with a companion application for data interpretation and feedback

The system architecture prioritized non-invasive sensing, continuous monitoring, and user comfort.

Design Rationale & Technical Considerations

Several technical challenges were central to the project’s feasibility analysis:

  • Miniaturization: Constraints associated with integrating EEG sensing and microfluidic pathways into a compact wearable form
  • Biomarker stability: Limitations of tear-based biochemical sensing under real-world conditions
  • Signal quality: Noise susceptibility and motion artifacts in wearable EEG acquisition
  • User comfort & compliance: Balancing sensing performance with long-term wearability

Design rationale was informed by BioMEMS literature, wearable sensor research and established biomedical device design principles.

Key Contributions

  • Contributed to the conceptualization of a multi-modal BioMEMS wearable for relapse risk prediction
  • Evaluated the integration of neural and biochemical sensing within a single device architecture
  • Analyzed technical, physiological and usability constraints impacting wearable BioMEMS feasibility
  • Translated BioMEMS theory and literature into a coherent system-level concept

Technical Documentation

📄 Final Project Presentation (PDF)
Includes system architecture, design rationale and feasibility analysis.

Tools & Technologies

BioMEMS Principles · Microfluidics · EEG Signal Monitoring
Biomedical Device Design · Conceptual System Architecture · Research Analysis

Limitations & Future Directions

As a conceptual design project, the system was not fabricated or experimentally validated. Limitations include the absence of physical prototyping, in-vivo testing and quantitative assessment of biomarker stability and EEG signal quality in wearable configurations.

Future work would involve microfabrication of sensing components, benchtop validation of tear-based biomarker detection and experimental evaluation of wearable EEG performance. Integration with machine learning–based prediction models could further enhance relapse risk forecasting capability.

Reflection

This project strengthened my ability to synthesize BioMEMS theory, biomedical sensing principles and human-centered design into a cohesive system concept. It reinforced research-driven problem framing, critical evaluation of feasibility constraints and clear technical communication—skills directly applicable to early-stage biomedical device development and translational research.

← Back to Portfolio