Thermochromic Textiles for Diabetic Monitoring
Overview
This research project focuses on developing thermochromic textiles for non-invasive diabetic inflammation monitoring. The work bridges materials science, optics, and biomedical applications to create patient-centric diagnostic solutions.
Key Achievements
Grant Funding
- Successfully drafted and secured $30,000 grant from Commonwealth Cyber Initiative
- Comprehensive literature review substantiated clinical viability of the technology
Optical Metrology System
Engineered an Integrated Optical Metrology System using Python/OpenCV that:
- Synchronizes mechanical strain with colorimetry measurements
- Eliminates temporal artifacts in data collection
- Optimizes 35+ metrics for thermodynamic reversibility
- Increased throughput by >300%
Fatigue Analysis
- Decoupled elastic signals from plastic damage using unsupervised Machine Learning (PCA)
- Identified failure modes to enhance material longevity
- Validated structural changes using extended Michel-Lévy interference chart
Rheology Correlation
- Utilized rheology models (Cross/Power-law) to correlate optical data with physical properties
- Ensured consistent formulation and processing behavior
Impact
This research has the potential to revolutionize diabetic care by providing:
- Non-invasive, continuous inflammation monitoring
- Cost-effective wearable diagnostic solutions
- Real-time patient feedback for better health management
Technologies Used
- Python (OpenCV, NumPy, Pandas, scikit-learn)
- Optical Microscopy
- Rheology Characterization
- Machine Learning (PCA)
