Blood Pressure Estimation from PPG Signals
Budget: ₹1,250 – ₹2,500 INR
Looking for an ML engineer to develop and implement a Model for real-time blood pressure (BP) estimation using heart rate photoplethysmograph (PPG) signals which will be deployed on an MCU for non-invasive health monitoring.
Responsibilities:
• Design and implement a robust algorithm for extracting BP values from raw or pre-processed PPG data (depending on the provided format).
• Utilise machine learning or neural network techniques to achieve accurate BP estimation.
• Optimise the algorithm for real-time performance on Nordic chipsets in an embedded C environment (or provide an embedded C based implementation of the model)
• Integrate the algorithm seamlessly with existing hardware and software platforms (will be done by our engineers)
• Conduct extensive testing and validation of the algorithm's accuracy and performance under various conditions. (will be done by our engineers)
• Document the algorithm design, implementation, and testing process clearly and concisely.
Ideal candidate:
• Strong foundation in Python, C, C++
• Solid understanding of signal processing and machine learning/neural network techniques.
• Experience with real-time data processing and embedded system optimization.
• Familiarity with physiological signals, particularly PPG and blood pressure dynamics, is a plus.
• Excellent analytical and problem-solving skills with a meticulous approach to detail.
• Strong communication and documentation skills.
References:
https://drive.google.com/drive/folders/1Nf8LKs1MxffZqxoM3kuTvbqA8D7Q67-x
Responsibilities:
• Design and implement a robust algorithm for extracting BP values from raw or pre-processed PPG data (depending on the provided format).
• Utilise machine learning or neural network techniques to achieve accurate BP estimation.
• Optimise the algorithm for real-time performance on Nordic chipsets in an embedded C environment (or provide an embedded C based implementation of the model)
• Integrate the algorithm seamlessly with existing hardware and software platforms (will be done by our engineers)
• Conduct extensive testing and validation of the algorithm's accuracy and performance under various conditions. (will be done by our engineers)
• Document the algorithm design, implementation, and testing process clearly and concisely.
Ideal candidate:
• Strong foundation in Python, C, C++
• Solid understanding of signal processing and machine learning/neural network techniques.
• Experience with real-time data processing and embedded system optimization.
• Familiarity with physiological signals, particularly PPG and blood pressure dynamics, is a plus.
• Excellent analytical and problem-solving skills with a meticulous approach to detail.
• Strong communication and documentation skills.
References:
https://drive.google.com/drive/folders/1Nf8LKs1MxffZqxoM3kuTvbqA8D7Q67-x