59 research outputs found
MEDUSA: Scalable Biometric Sensing in the Wild through Distributed MIMO Radars
Radar-based techniques for detecting vital signs have shown promise for
continuous contactless vital sign sensing and healthcare applications. However,
real-world indoor environments face significant challenges for existing vital
sign monitoring systems. These include signal blockage in non-line-of-sight
(NLOS) situations, movement of human subjects, and alterations in location and
orientation. Additionally, these existing systems failed to address the
challenge of tracking multiple targets simultaneously. To overcome these
challenges, we present MEDUSA, a novel coherent ultra-wideband (UWB) based
distributed multiple-input multiple-output (MIMO) radar system, especially it
allows users to customize and disperse the into sub-arrays.
MEDUSA takes advantage of the diversity benefits of distributed yet wirelessly
synchronized MIMO arrays to enable robust vital sign monitoring in real-world
and daily living environments where human targets are moving and surrounded by
obstacles. We've developed a scalable, self-supervised contrastive learning
model which integrates seamlessly with our hardware platform. Each attention
weight within the model corresponds to a specific antenna pair of Tx and Rx.
The model proficiently recovers accurate vital sign waveforms by decomposing
and correlating the mixed received signals, including comprising human motion,
mobility, noise, and vital signs. Through extensive evaluations involving 21
participants and over 200 hours of collected data (3.75 TB in total, with 1.89
TB for static subjects and 1.86 TB for moving subjects), MEDUSA's performance
has been validated, showing an average gain of 20% compared to existing systems
employing COTS radar sensors. This demonstrates MEDUSA's spatial diversity gain
for real-world vital sign monitoring, encompassing target and environmental
dynamics in familiar and unfamiliar indoor environments.Comment: Preprint. Under Revie
Respiratory Rate Estimation Using WiFi Channel State Information - A Machine Learning Approach
Respiratory rate (RR) is an important vital sign for diagnosing and treating a number of medical conditions. Current respiration monitoring systems require that a special device is continuously attached to the human body. However, contactless respiration monitoring systems have recently been developed to overcome this inconvenience. Research has shown that channel state information (CSI) measured by WiFi devices can be used for estimating RR. Although pattern-based respiration detection has been used to extract RR from periodic changes in CSI, systems based on this method do not perform well when channel conditions are not favorable. This thesis highlights newly introduced learning-based approaches used for RR estimation. Off-the-shelf WiFi devices were used to collect fine-grained wireless CSI data, which was then used to train and evaluate machine learning models.
Results show that classification algorithms, including KNN, SVM, Random Forest, Logistic Regression and MLP, achieve over 96% accuracy when predicting RR. Regression models were compared to an existing pattern-based system, demonstrating that the majority of regression models have better performance when estimating RR. For instance, Logistic Regressionâs Root Mean Square Error (RMSE) is 0.35, while pattern-based systemâs RMSE is 2.7. It is important to note that classification and regression models cannot be generalized, nor can they accurately predict respiratory rate using the data collected from a new and previously unseen subject. To improve and make the models more generalizable, data used to train the models must be collected from a larger number of subjects
Non-invasive RF sensing for detecting breathing abnormalities using software deïŹned radios
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 (17.131%), 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
Hand-breathe: Non-Contact Monitoring of Breathing Abnormalities from Hand Palm
In post-covid19 world, radio frequency (RF)-based non-contact methods, e.g.,
software-defined radios (SDR)-based methods have emerged as promising
candidates for intelligent remote sensing of human vitals, and could help in
containment of contagious viruses like covid19. To this end, this work utilizes
the universal software radio peripherals (USRP)-based SDRs along with classical
machine learning (ML) methods to design a non-contact method to monitor
different breathing abnormalities. Under our proposed method, a subject rests
his/her hand on a table in between the transmit and receive antennas, while an
orthogonal frequency division multiplexing (OFDM) signal passes through the
hand. Subsequently, the receiver extracts the channel frequency response
(basically, fine-grained wireless channel state information), and feeds it to
various ML algorithms which eventually classify between different breathing
abnormalities. Among all classifiers, linear SVM classifier resulted in a
maximum accuracy of 88.1\%. To train the ML classifiers in a supervised manner,
data was collected by doing real-time experiments on 4 subjects in a lab
environment. For label generation purpose, the breathing of the subjects was
classified into three classes: normal, fast, and slow breathing. Furthermore,
in addition to our proposed method (where only a hand is exposed to RF
signals), we also implemented and tested the state-of-the-art method (where
full chest is exposed to RF radiation). The performance comparison of the two
methods reveals a trade-off, i.e., the accuracy of our proposed method is
slightly inferior but our method results in minimal body exposure to RF
radiation, compared to the benchmark method
FarSense: pushing the range limit of WiFi-based respiration sensing with CSI ratio of two antennas
International audienceThe past few years have witnessed the great potential of exploiting channel state information retrieved from commodity WiFi devices for respiration monitoring. However, existing approaches only work when the target is close to the WiFi transceivers and the performance degrades significantly when the target is far away. On the other hand, most home environments only have one WiFi access point and it may not be located in the same room as the target. This sensing range constraint greatly limits the application of the proposed approaches in real life. This paper presents FarSense-the first real-time system that can reliably monitor human respiration when the target is far away from the WiFi transceiver pair. FarSense works well even when one of the transceivers is located in another room, moving a big step towards real-life deployment. We propose two novel schemes to achieve this goal: (1) Instead of applying the raw CSI readings of individual antenna for sensing, we employ the ratio of CSI readings from two antennas, whose noise is mostly canceled out by the division operation to significantly increase the sensing range; (2) The division operation further enables us to utilize the phase information which is not usable with one single antenna for sensing. The orthogonal amplitude and phase are elaborately combined to address the "blind spots" issue and further increase the sensing range. Extensive experiments show that FarSense is able to accurately monitor human respiration even when the target is 8 meters away from the transceiver pair, increasing the sensing range by more than 100%. 1 We believe this is the first system to enable through-wall respiration sensing with commodity WiFi devices and the proposed method could also benefit other sensing applications
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