Wireless Monitoring of Respiration and Heart Rates Using Software-Defined-Radio

Al-Wahedi, Ali; Al-Shams, Mojtaba; Albettar, Mohammed Aieash; Alawsh, Saleh A.; Muqaibel, Ali H.

doi:10.1109/ssd.2019.8893254

Cited by 11 publications

(12 citation statements)

References 8 publications

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“…Most authors chose to detect chest wall movements (60%). For the environmental category, modulation of cardiac activity was also very common [ 5 , 7 , 50 , 52 , 54 , 163 , 164 , 168 , 174 , 175 , 177 , 178 , 189 , 190 , 196 , 197 , 198 , 199 , 205 , 206 , 207 , 213 , 214 , 219 , 221 , 223 , 224 , 225 , 226 , 229 , 232 , 233 ]. Meanwhile, air temperature and air humidity were the second [ 2 , 64 , 91 , 104 , 107 , 116 , 120 , 128 , 133 , 145 , 153 , 154 , 155 , 156 , 162 ] and third [ 66 , 74 , 75 , 80 ,…”

Section: Resultsmentioning

confidence: 99%

“…Regarding the duration of the experiments, most of the studies carried out short experiments of a few minutes. In fact, 58% of the studies performed tests of less than 5 minutes [ 69 , 70 , 81 , 86 , 87 , 88 , 96 , 100 , 102 , 104 , 111 , 125 , 136 , 161 , 163 , 165 , 171 , 172 , 173 , 174 , 193 , 195 , 196 , 200 , 212 , 215 , 221 , 228 , 229 , 232 ]. Most of the works that conducted longer tests included sleep studies [ 7 , 17 , 53 , 60 , 115 , 146 , 148 , 164 , 165 , 169 , 173 , 192 , 198 , 205 , 211 , 220 , 223 ].…”

Section: Resultsmentioning

confidence: 99%

“…Regarding the tools for measurement processing, Figure 36 shows the distribution of use of the different tools for the wearable and environmental respiration monitoring systems. MATLAB was the preferred software for both types of systems, since it was adopted by half of the studies, while NI Labview was the second most used tool as it appeared in 20–30% of the works [ 2 , 3 , 19 , 63 , 71 , 85 , 86 , 87 , 88 , 127 , 129 , 131 , 138 , 142 , 146 , 153 , 163 , 201 , 218 , 219 , 220 , 223 , 225 ]. The rest of the tools relied heavily on the specific sensor used to capture the data.…”

Section: Resultsmentioning

confidence: 99%

“…This would facilitate comparison of system performance among studies. However, this is not the most common approach and only a limited number of studies have incorporated it [ 10 , 44 , 57 , 60 , 61 , 65 , 78 , 92 , 100 , 109 , 112 , 114 , 122 , 123 , 124 , 132 , 163 , 165 , 166 , 167 , 168 , 169 , 176 , 178 , 191 , 196 , 205 , 206 , 211 , 224 , 228 ].…”

Section: Discussionmentioning

confidence: 99%

See 3 more Smart Citations

Sensing Systems for Respiration Monitoring: A Technical Systematic Review

Vanegas

Igual

Plaza

2020

Sensors

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Respiratory monitoring is essential in sleep studies, sport training, patient monitoring, or health at work, among other applications. This paper presents a comprehensive systematic review of respiration sensing systems. After several systematic searches in scientific repositories, the 198 most relevant papers in this field were analyzed in detail. Different items were examined: sensing technique and sensor, respiration parameter, sensor location and size, general system setup, communication protocol, processing station, energy autonomy and power consumption, sensor validation, processing algorithm, performance evaluation, and analysis software. As a result, several trends and the remaining research challenges of respiration sensors were identified. Long-term evaluations and usability tests should be performed. Researchers designed custom experiments to validate the sensing systems, making it difficult to compare results. Therefore, another challenge is to have a common validation framework to fairly compare sensor performance. The implementation of energy-saving strategies, the incorporation of energy harvesting techniques, the calculation of volume parameters of breathing, or the effective integration of respiration sensors into clothing are other remaining research efforts. Addressing these and other challenges outlined in the paper is a required step to obtain a feasible, robust, affordable, and unobtrusive respiration sensing system.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Sensing Systems for Respiration Monitoring: A Technical Systematic Review

Vanegas

Igual

Plaza

2020

Sensors

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“…SDR-based breath monitoring has been investigated by various authors [20][21][22][23][24]. SDRbased breath monitoring is considered the most efficient among all the RF-based techniques, as it offers a flexible, portable, and scalable solution.…”

Section: Literature Reviewmentioning

confidence: 99%

Improving Machine Learning Classification Accuracy for Breathing Abnormalities by Enhancing Dataset

Rehman

Shah

Khan

et al. 2021

Sensors

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The recent severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), also known as coronavirus disease (COVID)-19, has appeared as a global pandemic with a high mortality rate. The main complication of COVID-19 is rapid respirational deterioration, which may cause life-threatening pneumonia conditions. Global healthcare systems are currently facing a scarcity of resources to assist critical patients simultaneously. Indeed, non-critical patients are mostly advised to self-isolate or quarantine themselves at home. However, there are limited healthcare services available during self-isolation at home. According to research, nearly 20–30% of COVID patients require hospitalization, while almost 5–12% of patients may require intensive care due to severe health conditions. This pandemic requires global healthcare systems that are intelligent, secure, and reliable. Tremendous efforts have been made already to develop non-contact sensing technologies for the diagnosis of COVID-19. The most significant early indication of COVID-19 is rapid and abnormal breathing. In this research work, RF-based technology is used to collect real-time breathing abnormalities data. Subsequently, based on this data, a large dataset of simulated breathing abnormalities is generated using the curve fitting technique for developing a machine learning (ML) classification model. The advantages of generating simulated breathing abnormalities data are two-fold; it will help counter the daunting and time-consuming task of real-time data collection and improve the ML model accuracy. Several ML algorithms are exploited to classify eight breathing abnormalities: eupnea, bradypnea, tachypnea, Biot, sighing, Kussmaul, Cheyne–Stokes, and central sleep apnea (CSA). The performance of ML algorithms is evaluated based on accuracy, prediction speed, and training time for real-time breathing data and simulated breathing data. The results show that the proposed platform for real-time data classifies breathing patterns with a maximum accuracy of 97.5%, whereas by introducing simulated breathing data, the accuracy increases up to 99.3%. This work has a notable medical impact, as the introduced method mitigates the challenge of data collection to build a realistic model of a large dataset during the pandemic.

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Spectral Analysis and Digital Signal Processing in Engineering Using Software Defined Radios and GNU Radio Software

Şorecău,

Sârbu

et al. 2023

Lecture Notes in Networks and Systems

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Wireless Monitoring of Respiration and Heart Rates Using Software-Defined-Radio

Cited by 11 publications

References 8 publications

Sensing Systems for Respiration Monitoring: A Technical Systematic Review

Sensing Systems for Respiration Monitoring: A Technical Systematic Review

Improving Machine Learning Classification Accuracy for Breathing Abnormalities by Enhancing Dataset

Spectral Analysis and Digital Signal Processing in Engineering Using Software Defined Radios and GNU Radio Software

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