Hand-Breathe: Noncontact Monitoring of Breathing Abnormalities From Hand Palm

Kawish, Pervez,; Aman, Waqas; Rahman, Muhammad Mahboob Ur; Wasim, Nawaz, M.; Abbasi, Qammer H.

doi:10.1109/jsen.2023.3246631

Cited by 3 publications

(1 citation statement)

References 39 publications

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“…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.

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Non-Contact Acquisition of PPG Signal using Chest Movement-Modulated Radio Signals

Filho,

Rahman,

Laleg-Kirati

et al. 2024

IFAC-PapersOnLine

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A Piezoresistive-Sensor Nonlinearity Correction on-Chip Method with Highly Robust Class-AB Driving Capability

Jing,

Han,

Yuan

et al. 2024

Sensors

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This paper presents a thorough robust Class-AB power amplifier design and its application in pressure-mode sensor-on-chip nonlinearity correction. Considering its use in piezoresistive sensing applications, a gain-boosting-aided folded cascode structure is utilized to increase the amplifier’s gain by a large amount as well as enhancing the power rejection ability, and a push–pull structure with miller compensation, a floating gate technique, and an adaptive output driving limiting structures are adopted to achieve high-efficiency current driving capability, high stability, and electronic environmental compatibility. This amplifier is applied in a real sensor nonlinearity correction on-chip system. With the help of a self-designed 7-bit + sign DAC and a self-designed two-stage operational amplifier, this system is compatible with nonlinear correction at different signal conditioning output values. It can also drive resistive sensors as small as 300 ohms and as high as tens of thousands of ohms. The designed two-stage operational amplifier utilizes the TSMC 0.18 um process, resulting in a final circuit power consumption of 0.183 mW. The amplifier exhibits a gain greater than 140 dB, a phase margin of 68°, and a unit gain bandwidth exceeding 199.76 kHz. The output voltage range spans from 0 to 4.6 V. The final simulation results indicate that the nonlinear correction system designed in this paper can correct piezoresistive sensors with a nonlinearity of up to ±2.5% under various PVT (Process–Voltage–Temperature) conditions. After calibration by this system, the maximum error in the output voltage is 4 mV, effectively reducing the nonlinearity to 4% of its original value in the worst-case scenario.

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Hand-Breathe: Noncontact Monitoring of Breathing Abnormalities From Hand Palm

Cited by 3 publications

References 39 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

Non-Contact Acquisition of PPG Signal using Chest Movement-Modulated Radio Signals

A Piezoresistive-Sensor Nonlinearity Correction on-Chip Method with Highly Robust Class-AB Driving Capability

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