CDR Sample for Bio-Medical Engineers (233913)

This is my final year project focused on creating an accessible cardiac monitoring solution. My core responsibilities were:

I was responsible for the end-to-end design of a portable ECG device. I began by defining the system requirements based on clinical needs for remote patient monitoring. I designed the analog front-end circuit. It included a bio-instrumentation amplifier and advanced filters. It was instrumental in removing noise from the raw ECG signal. A critical task was ensuring that the design was compliant with the medical electrical equipment safety IEC 60601 standard. I created the PCB layout in Altium Designer and developed embedded C++ firmware for the microcontroller to digitise the signal and transmit it wirelessly to a mobile application. Finally, I validated the device’s accuracy by testing it against the certified medical-grade ECG machine.

• Skills & Technologies Demonstrated:
  • Software: MATLAB/Simulink (for signal processing), Altium Designer, Embedded C++, Python.
  • Standards & Frameworks: IEC 60601 (Medical Safety), ISO 13485 (QMS), IoT Protocols (MQTT, Bluetooth LE).
  • Competencies: Biopotential Amplification, Analog & Digital Signal Processing, PCB Design for Medical Devices, Embedded Systems, Validation & Verification.

This research project aimed to create an intuitive control system for an advanced prosthetic limb. My key duties included:

I developed a control system that translates electromyography (EMG) signals from a user’s forearm into complex hand movements. My first task was to design the data acquisition hardware to capture and condition the weak EMG biosignals. The core of my work involved developing a pattern recognition algorithm in MATLAB. I used machine learning techniques to train the system to recognise different muscle activation patterns and map them to specific prosthetic hand gestures (e.g., open, close, pinch). A significant challenge was implementing this algorithm in a real-time embedded system to ensure instantaneous response. I integrated the system with a mechanical prosthetic hand and conducted extensive testing with users to fine-tune the performance and accuracy.

• Skills & Technologies Demonstrated:
  • Software: MATLAB, Simulink, Python (Scikit-learn, TensorFlow), SolidWorks.
  • Standards & Frameworks: Real-Time Operating Systems (RTOS), Human-Machine Interface Design Principles.
  • Competencies: EMG Signal Processing, Machine Learning, Pattern Recognition, Real-Time Control Systems, Mechatronics Integration, System Validation.

This project focused on developing a software tool to automate a time-consuming laboratory process. My role involved:

I was tasked with creating an algorithm to automatically count and analyse cells from microscope images. I developed the entire image processing pipeline using MATLAB and Python with OpenCV. This involved implementing pre-processing steps like noise reduction and contrast enhancement, followed by a sophisticated image segmentation algorithm to accurately identify and separate individual cells, even in dense clusters. Finally, I extracted key morphological features, which are size, shape, and intensity, from each detected cell. The system’s accuracy was rigorously validated by comparing its automated counts against manual counts performed by a team of biologists, achieving over 98% correlation.

• Skills & Technologies Demonstrated:
  • Software: MATLAB (Image Processing Toolbox), Python (OpenCV, Scikit-image), ImageJ.
  • Standards & Frameworks: GxP (Good Practice) quality guidelines, Statistical Analysis Methods (e.g., Bland-Altman plot).
  • Competencies: Digital Image Processing, Algorithm Development, Machine Learning for Classification, Software Validation, Statistical Analysis, Feature Extraction.