On Tracking the Physicality of Wi-Fi: A Subspace Approach

Alloulah, Mohammed; Isopoussu, Anton; Min, Chulhong; Kawsar, Fahim

doi:10.1109/access.2019.2897840

Cited by 6 publications

(4 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…WiFi-based vital signs sensing relies on techniques that often lead to high dependency on multiple, environment-dependent parameters, typically obtained via WiFi channel state information (CSI). These parameters are difficult to tune in realworld applications because it is mandatory to characterize the relationship between CSI and human-related movement [60]. CSI is known to be sensitive to specific environmental conditions, being heavily affected by indoor multipath and fading RF propagation phenomena, which compromises its reliable modeling [61].…”

Section: Wifi-based Sensingmentioning

confidence: 99%

“…CSI is known to be sensitive to specific environmental conditions, being heavily affected by indoor multipath and fading RF propagation phenomena, which compromises its reliable modeling [61]. Although CSI-based techniques are gaining a lot of attention from the scientific community, their maturity has not yet achieved a reliable performance for being incorporated into medical devices due to the fact that most of these approaches are still struggling to surpass experimental conditions [60]. In [62], Ali et al, present two distinct methods for respiration and body-motion tracking, which take advantage of continuous Wi-Fi CSI assessment.…”

Section: Wifi-based Sensingmentioning

confidence: 99%

See 1 more Smart Citation

CoViS: A Contactless Health Monitoring System for the Nursing Home

Lopes,

Silva,

Pinho

et al. 2024

IEEE Access

View full text Add to dashboard Cite

In a pandemic, the availability of health indicators among at-risk populations, such as the elderly, is vital due to the disease's rapid transmission and the need to act quickly to contain its evolution. Traditional clinical methods for monitoring vital signs typically require contact-based sensors that must be precisely attached by a skilled healthcare professional, are less practical for repeatable measurements, and are not suitable for long-term monitoring. Contactless vital signs monitoring, such as radar-based techniques or IR-thermal imaging, on the other hand, does not require the attachment of physical electrodes and can be useful for remote health monitoring and therefore reduce physical contact between subjects and healthcare professionals, ensuring social distancing, in particular, in nursing homes, and protecting the elderly population in this way from other external factors that increase the likelihood of infection. This article presents three main contributions: (i) vital signs characterization in the elderly population; (ii) a stateof-the-art review on the most prominent techniques and methods for Contactless Health Monitoring (CHM); (iii) the design, specification, and evaluation of a low-cost proof-of-concept CHM system for nursing homes, which includes an IoT Edge Device , which enables real-time vital signs monitoring (cardio-respiratory rates and elevated body temperature) using a multimodal approach based on Doppler radar and IR thermal imaging sensors, and thus generates health indicators without any type of contact or invasiveness. By direct comparison with reference instruments, results have shown an error below 10%, in 74%, 52%, and 96% of the cases for Heart Rate, Respiratory Rate, and Body Temperature measurements, respectively.

show abstract

Section: Wifi-based Sensingmentioning

confidence: 99%

Section: Wifi-based Sensingmentioning

confidence: 99%

CoViS: A Contactless Health Monitoring System for the Nursing Home

Lopes,

Silva,

Pinho

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…The MIMO system between the OFDM subcarriers and the Tx-Rx antennas, forms a multidimensional array which effectively represents a high-dimensional mathematical space. Contained in this space is the signal subspace along frequency and spatial dimensions [41]. The key intuition behind our model is that while a user is sleeping, the signal subspace along these dimensions is affected by both breathing and body/limb motion.…”

Section: Impact Of Breathing and Body/limb Movements On Wifi Signal S...mentioning

confidence: 99%

On Goodness of WiFi based Monitoring of Vital Signs in the Wild

Ali¹,

Alloulah²,

Kawsar³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

WiFi channel state information (CSI) has emerged as a plausible modality for sensing different human vital signs, i.e. respiration and body motion, as a function of modulated wireless signals that travel between WiFi devices. Although a remarkable proposition, most of the existing research in this space struggles to withstand robust performance beyond experimental conditions. To this end, we take a careful look at the dynamics of WiFi signals under human respiration and body motions in the wild. We first characterize the WiFi signal components -multipath and signal subspace -that are modulated by human respiration and body motions. We extrapolate on a set of transformations, including first-order differentiation, max-min normalization and component projections, that faithfully explains and quantifies the dynamics of respiration and body motions on WiFi signals. Grounded in this characterization, we propose two methods: 1) a respiration tracking technique that models the peak dynamics observed in the time-varying signal subspaces and 2) a body-motion tracking technique built with a multi-dimensional clustering of evolving signal subspaces. Finally, we reflect on the manifestation of these techniques in a practical sleep monitoring application. Our systematic evaluation with over 550 hours of data from 5 users covering both line-of-sight (LOS) and non-line-of-sight (NLOS) settings shows that the proposed techniques can achieve comparable performance to purpose-built pulse-Doppler radar.

show abstract

“…Most RSI algorithms estimate the unknown state matrix A A A and the output matrix C C C from the ''data equation'' consisting of the input and output samples of the systems. This requires the estimation of the subspace of the extended observability matrix, which requires SVD [10] or the use of more efficient subspace tracking algorithms [11], [12]. The computation of the state matrix A A A also requires the evaluation of a pseudoinverse and few works discussed the online estimation of the model order since it involves the computation of its eigenvalues.…”

Section: Introductionmentioning

confidence: 99%

A Variable Regularized Recursive Subspace Model Identification Algorithm With Extended Instrumental Variable and Variable Forgetting Factor

2020

View full text Add to dashboard Cite

This paper proposes a new smoothly clipped absolute deviation (SCAD) regularized recursive subspace model identification algorithm with square root(SR) extended instrumental variable (EIV) and locally optimal variable forgetting factor (LOFF). The proposed algorithm is based on a new local polynomial modeling (LPM)-based variable FF EIV projection approximation subspace tracking (PAST) and multivariate recursive least squares algorithms for estimating respectively the subspace of the extended observability matrix, and the input matrix and feedthrough matrix. The SREIV-PAST algorithm offers improved resilience to additive noise over the PAST algorithm and it is implemented using the more stable square-root form. The asymptotic mean square error of the corresponding LPM-based model is derived and minimized at each time instant to obtain the proposed LOFF for improving the convergence speed and steady state error. A recursive bi-iteration singular value decomposition (SVD) algorithm is also proposed for recursive computation of the pseudo-inverse of the state transition matrix and its eigenvalues. This facilitates online estimation of its model order using classical model selection criteria. Moreover, a new criterion based on the percentage of explained variance is also proposed with improved performance. The SCAD regularization is proposed for automatic model selection of the input and feedthrough matrices, as it is asymptotically unbiased. Efficient techniques for incorporating SCAD and estimating other required quantities are also developed. The proposed algorithms are evaluated using computer simulations under stationary and nonstationary environments and a real dataset on wing flutter data. Results show that the proposed algorithms offer improved convergence speed and steady state performance over the conventional algorithms and it also provides an online estimate of the system model order. INDEX TERMS Recursive subspace model identification (RSI), variable forgetting factor (VFF), SCAD regularization, extended instrumental variable (EIV), local polynomial modeling (LPM), model order estimation.

show abstract

On Tracking the Physicality of Wi-Fi: A Subspace Approach

Cited by 6 publications

References 24 publications

CoViS: A Contactless Health Monitoring System for the Nursing Home

CoViS: A Contactless Health Monitoring System for the Nursing Home

On Goodness of WiFi based Monitoring of Vital Signs in the Wild

A Variable Regularized Recursive Subspace Model Identification Algorithm With Extended Instrumental Variable and Variable Forgetting Factor

Contact Info

Product

Resources

About