Abstract
In this paper, we propose a Doppler spectrum-based passenger detection scheme for a CW (Continuous Wave) radar sensor in vehicle applications. First, we design two new features, referred to as an ‘extended degree of scattering points’ and a ‘different degree of scattering points’ to represent the characteristics of the non-rigid motion of a moving human in a vehicle. We also design one newly defined feature referred to as the ‘presence of vital signs’, which is related to extracting the Doppler frequency of chest movements due to breathing. Additionally, we use a BDT (Binary Decision Tree) for machine learning during the training and test steps with these three extracted features. We used a 2.45 GHz CW radar front-end module with a single receive antenna and a real-time data acquisition module. Moreover, we built a test-bed with a structure similar to that of an actual vehicle interior. With the test-bed, we measured radar signals in various scenarios. We then repeatedly assessed the classification accuracy and classification error rate using the proposed algorithm with the BDT. We found an average classification accuracy rate of 98.6% for a human with or without motion.
Highlights
The fact that children are dying in hot vehicles has recently become a major social issue
Vehicle applications, the received is weak and there are paper, we distinguish between humans other byhuman, using feature vectorssignal extracted this fewer scattering points compared to theand case of aobjects walking the received has from multiple pattern, with the vitalthe signal through additional signal processing, and applying reflectiontogether points echoed from head,extracted torso, waist, arms, pelvis, and other parts of the passenger’s the features to machine learning
If the vital sign detection algorithm is advanced in the future, this problem will be resolved
Summary
The fact that children are dying in hot vehicles has recently become a major social issue. Earlier work proposed the concept of detecting the vital signals of a passenger by mounting a UWB radar sensor in a vehicle [8]. In another approach [9], a UWB radar sensor was used to detect human vital signs for each seat, applying the features extracted from the detected range into the machine leaning approach, such as a SVM (Support Vector Machine). These two related works only focused on non-moving humans, and did not consider moving humans or other objects.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
