Few-Shot User-Adaptable Radar-Based Breath Signal Sensing
Author:
Mauro GianfrancoORCID, De Carlos Diez Maria, Ott JuliusORCID, Servadei LorenzoORCID, Cuellar Manuel P., Morales-Santos Diego P.ORCID
Abstract
Vital signs estimation provides valuable information about an individual’s overall health status. Gathering such information usually requires wearable devices or privacy-invasive settings. In this work, we propose a radar-based user-adaptable solution for respiratory signal prediction while sitting at an office desk. Such an approach leads to a contact-free, privacy-friendly, and easily adaptable system with little reference training data. Data from 24 subjects are preprocessed to extract respiration information using a 60 GHz frequency-modulated continuous wave radar. With few training examples, episodic optimization-based learning allows for generalization to new individuals. Episodically, a convolutional variational autoencoder learns how to map the processed radar data to a reference signal, generating a constrained latent space to the central respiration frequency. Moreover, autocorrelation over recorded radar data time assesses the information corruption due to subject motions. The model learning procedure and breathing prediction are adjusted by exploiting the motion corruption level. Thanks to the episodic acquired knowledge, the model requires an adaptation time of less than one and two seconds for one to five training examples, respectively. The suggested approach represents a novel, quickly adaptable, non-contact alternative for office settings with little user motion.
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
Reference45 articles.
1. Sidikova, M., Martinek, R., Kawala-Sterniuk, A., Ladrova, M., Jaros, R., Danys, L., and Simonik, P. (2020). Vital sign monitoring in car seats based on electrocardiography, ballistocardiography and seismocardiography: A review. Sensors, 20. 2. Shimazaki, T., Anzai, D., Watanabe, K., Nakajima, A., Fukuda, M., and Ata, S. (2022). Heat stroke prevention in hot specific occupational environment enhanced by supervised machine learning with personalized vital signs. Sensors, 22. 3. Respiratory rate: The forgotten vital sign—Make it count!;Loughlin;Jt. Comm. J. Qual. Patient Saf.,2018 4. Brekke, I.J., Puntervoll, L.H., Pedersen, P.B., Kellett, J., and Brabrand, M. (2019). The value of vital sign trends in predicting and monitoring clinical deterioration: A systematic review. PLoS ONE, 14. 5. Organisation, W.H. (2022, October 25). Cardiovascular Diseases (CVDs). Available online: https://www.who.int/en/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds).
Cited by
4 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
|
|