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

Ali, Kamran; Alloulah, Mohammed; Kawsar, Fahim; Liu, Alex X.

doi:10.48550/arxiv.2003.09386

Cited by 2 publications

(3 citation statements)

References 26 publications

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“…2) Radar-based sensing (that utilizes the traditional radar principles of range and Doppler) [28], [29]. 3) Wi-Fi-based sensing (that uses the data collected off the reflections from the human subjects) [30], [29]. 4) Software-defined radio (SDR)-based sensing: (that utilizes the signals reflected off the human body) [31], [32].…”

Section: B Non-contact Methods For Health Sensingmentioning

confidence: 99%

Noncontact Monitoring of Dehydration Using RF Data Collected Off the Chest and the Hand

Buttar,

Pervez,

Rahman

et al. 2024

IEEE Sensors J.

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We report a novel non-contact method for dehydration monitoring. We utilize a transmit software defined radio (SDR) that impinges a wideband radio frequency (RF) signal (of frequency 5.23 GHz) onto either the chest or the hand of a subject who sits nearby. Further, another SDR in the closed vicinity collects the reflected RF signals. The two SDRs exchange orthogonal frequency division multiplexing (OFDM) signal, whose individual subcarriers get modulated once it reflects off (passes through) the chest (the hand) of the subject. This way, the signal collected by the receive SDR consists of channel frequency response (CFR) that captures the variation in the blood osmolality due to dehydration. The received raw CFR data is then passed through a handful of machine learning (ML) classifiers which classify each subject as either hydrated or dehydrated. To train our ML classifiers, we have constructed our custom dataset by collecting data from 5 Muslim subjects who were fasting during the month of Ramadan. Specifically, we have implemented and tested the following ML classifiers: k-nearest neighbour, support vector machine, decision tree, ensemble classifier, and a neural network classifier. Among all the classifiers, the neural network classifier achieved the best classification accuracy, i.e., an accuracy of 93.8% (96.15%) for the proposed chest-based (hand-based) method. Compared to prior contact-based method where the reported accuracy is 97.83%, our proposed non-contact method provides slightly less accuracy than that of reported in the literature for contact-based method; nevertheless, the advantages of our non-contact dehydration method speak for themselves.

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Section: B Non-contact Methods For Health Sensingmentioning

confidence: 99%

Noncontact Monitoring of Dehydration Using RF Data Collected Off the Chest and the Hand

Buttar,

Pervez,

Rahman

et al. 2024

IEEE Sensors J.

View full text Add to dashboard Cite

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“…The magnitude and phase response of the multiple OFDM frames is expressed in the Equations ( 21) and (22).…”

Section: R(k)|mentioning

confidence: 99%

“…The summary of drawbacks of the existing technologies used for connected health applications is provided in Table 1. SDR technology has the potential to monitor human health and early diagnosis of vital signs in a non-contact manner [22]. SDR technology is portable, flexible, has multifunctional capabilities, and it is cost-effective due to software-based modification without changing the hardware [23].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Non-Contact Sensing for Connected Health Using Software Defined Radio Technology

et al. 2021

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The unpredictable situation from the Coronavirus (COVID-19) globally and the severity of the third wave has resulted in the entire world being quarantined from one another again. Self-quarantine is the only existing solution to stop the spread of the virus when vaccination is under trials. Due to COVID-19, individuals may have difficulties in breathing and may experience cognitive impairment, which results in physical and psychological health issues. Healthcare professionals are doing their best to treat the patients at risk to their health. It is important to develop innovative solutions to provide non-contact and remote assistance to reduce the spread of the virus and to provide better care to patients. In addition, such assistance is important for elderly and those that are already sick in order to provide timely medical assistance and to reduce false alarm/visits to the hospitals. This research aims to provide an innovative solution by remotely monitoring vital signs such as breathing and other connected health during the quarantine. We develop an innovative solution for connected health using software-defined radio (SDR) technology and artificial intelligence (AI). The channel frequency response (CFR) is used to extract the fine-grained wireless channel state information (WCSI) by using the multi-carrier orthogonal frequency division multiplexing (OFDM) technique. The design was validated by simulated channels by analyzing CFR for ideal, additive white gaussian noise (AWGN), fading, and dispersive channels. Finally, various breathing experiments are conducted and the results are illustrated as having classification accuracy of 99.3% for four different breathing patterns using machine learning algorithms. This platform allows medical professionals and caretakers to remotely monitor individuals in a non-contact manner. The developed platform is suitable for both COVID-19 and non-COVID-19 scenarios.

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

Cited by 2 publications

References 26 publications

Noncontact Monitoring of Dehydration Using RF Data Collected Off the Chest and the Hand

Noncontact Monitoring of Dehydration Using RF Data Collected Off the Chest and the Hand

Intelligent Non-Contact Sensing for Connected Health Using Software Defined Radio Technology

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