Key result
Contactless breathing rate detection using a USRP platform and a K-Nearest Neighbor model achieved 91% accuracy in classifying breathing patterns compared to a wearable sensor.
Why the study?
Non-contact continuous monitoring of vital signs such as breathing and heart rate can evaluate physical health, offering an alternative to dedicated wearable sensors through synthesized radio frequency signals.
Does a USRP-based contact-less RF sensing system accurately detect breathing abnormalities compared to a wearable sensor in a human subject?
Does a USRP-based contact-less RF sensing system accurately detect breathing abnormalities compared to a wearable sensor in a human subject?
A contact-less RF sensing system using USRP and KNN machine learning can accurately monitor breathing rates, showing potential for non-invasive patient monitoring.
May support non-invasive respiratory monitoring; leaves open clinical validation in larger cohorts.
The non-contact continuous monitoring of biomarkers comprising breathing detection and heart rate are essential vital signs to evaluate the general physical health of a patient. As compared to existing methods that need dedicated equipment (such as wearable sensors), the radio frequency (RF) signals can be synthesised to continuously monitor breathing rate in a contact-less setting. In this paper, we proposed the contact less breathing rate detection using universal software radio peripheral (USRP) platform without any wearable sensor. Our system leverage on the channel state information (CSI) to record the minute movement caused by breathing over orthogonal frequency division multiplexing (OFDM) in multiple sub-carriers. We presented a comparison of our breathing rate detection with wearable sensor (ground truth) results for single human subject. In this paper, we used wireless data to train, validate and test different machine learning (ML) algorithms to classify USRP data into normal, shallow and elevated breathing depending on the breathing rate. Although different ML models were developed using the K-Nearest Neighbor (KNN), Discriminant Analysis (DA), Naive Bayes (NB) and Decision Tree (DT) algorithms, however results showed KNN based model provided the highest accuracy for our data (91%) each time the trial was made. DT (71.131%), DA (59.72%) and NB (48.99%). Results presented in this paper showed that USRP based breathing rate is comparable to the wearable sensor demonstrating the potential application of our method to accurately monitor breathing rate of patients in primary or acute setting.
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Ashleibta et al. (2020) studied Breathing abnormalities (n=1). Contactless breathing rate detection using universal software radio peripheral (USRP) platform vs. Wearable sensor was evaluated on Accuracy of breathing rate classification (normal, shallow, elevated). Contactless breathing rate detection using a USRP platform and a K-Nearest Neighbor model achieved 91% accuracy in classifying breathing patterns compared to a wearable sensor.
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