| Group |
2023-10 |
Status |
inprogress |
| Title |
Radar System for Indoor Health Monitoring (A) |
| Supervisor |
W. Zhu |
| Description |
As part of future Internet of Health Things (IoHT), automatic detection of abnormal events in daily life has received increasing attentions. Monitoring and warning of these events including, but not limited to, fall, cough, abnormal heart rate and breathing etc., is of crucial importance for senior citizens especially those living alone. Radar-based human activity monitoring has been actively studied by many researchers due to its potential for high accuracy, robustness, and privacy preservation This project aims to develop a multi-radar-based indoor human vital sign monitoring system utilizing deep learning technology. This monitoring system will capture and analyze information about a person movements and physiological conditions in real-time through multiple radars and AI technology. The desired system is expected to implement raw radar data processing, multichannel dada fusion, vital sign estimation and abnormal action detection and warning. The project team shall design a hardware platform integrated with radar devices and microprocessors as well as a user-friendly graphical user interface (GUI). The team is also required to design own dataset for the classification of vital signs (breathing rate and heart beat) and indoor human events.
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| Requirement |
Background courses including Signals & Systems (ELEC342) and Probability and Statistics (ENGR371)
Software coding skills with Python and MATLAB
Hardware knowledge of RasberryPi or similar embedded system
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| Tools |
Hardware: Radar sensors (to be determined), hardware components for processing, communications and control
Software: Python, MATLAB, C++
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| Number of Students |
6 |
| Students |
K. Benaicha, C. Hakizimana, R. Gold, A. Khizhnyak, S. Nechita, H. Zhixing Li |
| Comments: |
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