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Extracting Breathing Patterns in 3D Radar Sensor Data
The project will utilize radar sensor data, specifically time-series data of 3D point clouds and range-Doppler
maps, to analyze body and micro-movements. Initial data preprocessing will involve normalizing and filtering.
Subsequently a model will be developed to extract breathing rate using algorithms based on Fourier transform,
wavelet transform, and artificial intelligence. The model will be validated against wearable monitoring methods
using data from a clinical study involving 40 participants, encompassing over 480 nights of sensor data.
Nature of the Thesis:
• Literature research: 10%
• Data and image processing and
programming: 80%
• Documentation: 10%
Requirements:
• Motivation to work in a
multidisciplinary team
• Programming skills in python for data
processing and AI
• Familiarity
Nature of the Thesis: • Literature research: 10% • Data and image processing and programming: 80% • Documentation: 10%
Requirements: • Motivation to work in a multidisciplinary team • Programming skills in python for data processing and AI • Familiarity
To develop method that accurately detects breathing patterns during sleep in radar data.
To develop method that accurately detects breathing patterns during sleep in radar data.