Using Mobile Phone Sensors to Detect Rapid Respiratory Rate in the Diagnosis of Pneumonia

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... Charlton et al. (2018) present a recent literature review of the RR estimation from PPG signals. Several algorithms and techniques for RR extraction have been developed in the past few years (Karlen et al. 2015;Leonard et al. 2006;Li 2016;Mitali and Prabhu 2015;PS and Jatti 2015). Li (2016) proposed an Android mobile app that employs the smartphone sensors (i.e., accelerometer and gyroscope) for respiratory rate estimation. ...
... Several algorithms and techniques for RR extraction have been developed in the past few years (Karlen et al. 2015;Leonard et al. 2006;Li 2016;Mitali and Prabhu 2015;PS and Jatti 2015). Li (2016) proposed an Android mobile app that employs the smartphone sensors (i.e., accelerometer and gyroscope) for respiratory rate estimation. They used signal filtration and further processing to estimate the RR with an error of ±2 breaths/minute, which is high. ...
... The actual rate data was for paced breathing and showed little variance throughout the recordings. Across different subject, the average percentage error and the average percentage accuracy were 2.2%, 97.8% respectively, which surpasses the comparable literature (Karlen et al. 2015;Leonard et al. 2006;Li 2016;Mitali and Prabhu 2015;PS and Jatti 2015). The same applies to the maximum error, which was 0.67 breaths/min. ...
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Respiratory rate is a key vital sign that needs daily monitoring for hospital patients in general and those with respiratory conditions in particular. Moreover, it is a predictor of major heart conditions. Yet, studies have shown that it is widely neglected in hospital care due partially to the discomfort caused by the required equipment. In this paper, we propose a smartphone-based method for accurate measurement of the respiratory rate using the video of the skin surface as recorded by the smartphone built-in camera in the presence of the flash light. From this input, we use frame averaging to extract a photoplethysmographic signal of the red, green, and blue channels. Next, we apply discrete wavelet transform on the best representative photoplethysmographic signal for respiratory signal extraction and estimate the rate. Fifteen subjects participated in the testing and evaluation. The maximum absolute error was 0.67 breaths/min, whereas the root mean square error was 0.366 breaths/min. The average percentage error and average percentage accuracy using our approach were 2.2%, 97.8% respectively. A comparison with other works in the literature reveal a superior performance in terms of accuracy, ease of use, and cost.
... Research indicates that multi-axial accelerometers and gyroscopesas found ubiquitously in modern smartphones can accurately capture RR based on chest movements. [23][24][25][26][27][28][29][30] Additionally, due to their mechanism of action, these sensors are significantly less affected by environmental noise. Overall, smartphone-based measurement of RR provides a potential low cost, and a widely available method for RR measurement, both in a remote monitoring environment and in locations where specialised hardware and software are not available. ...
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Background Mobile health (mHealth) offers potential benefits to both patients and healthcare systems. Existing remote technologies to measure respiratory rates have limitations such as cost, accessibility and reliability. Using smartphone sensors to measure respiratory rates may offer a potential solution to these issues. Objective The aim of this study was to conduct a comprehensive assessment of a novel mHealth smartphone application designed to measure respiratory rates using movement sensors. Methods In Study 1, 15 participants simultaneously measured their respiratory rates with the app and a Food and Drug Administration-cleared reference device. A novel reference analysis method to allow the app to be evaluated ‘in the wild’ was also developed. In Study 2, 165 participants measured their respiratory rates using the app, and these measures were compared to the novel reference. The usability of the app was also assessed in both studies. Results The app, when compared to the Food and Drug Administration-cleared and novel references, respectively, showed a mean absolute error of 1.65 ( SD = 1.49) and 1.14 (1.44), relative mean absolute error of 12.2 (9.23) and 9.5 (18.70) and bias of 0.81 (limits of agreement = –3.27 to 4.89) and 0.08 (–3.68 to 3.51). Pearson correlation coefficients were 0.700 and 0.885. Ninety-three percent of participants successfully operated the app on their first use. Conclusions The accuracy and usability of the app demonstrated here in individuals with a normal respiratory rate range show promise for the use of mHealth solutions employing smartphone sensors to remotely monitor respiratory rates. Further research should validate the benefits that this technology may offer patients and healthcare systems.
... The devices are demonstrated to 6 monitor the variation of humidity content on exhaled and inhaled air during breathing without requiring any external power. Therefore, these sensors can be used for continuous monitoring of breathing pattern to detect many critical diseases like, sleep apnea, asthma, pneumonia and chronic obstructive pulmonary diseases (COPD) [36][37][38][39] . Monitoring moisture level at the wound site can also help in deciding a suitable type of dressing and tracking the healing process continuously 40 . ...
Thin protein films of gelatin molecules grown on flexible substrates have been utilized to fabricate moisture-induced energy harvesting devices, which work as self-biased sensors. Adsorbed water molecules from the ambient moistures generate protons inside the film. A proton transfer path is formed through the hydrogen-bonded water molecules with protein around 55% relative humidity condition and the protons are transferred due to the gradient of absorbed water molecules within the protein films. The devices are capable to harvest electric power up to 5.5 μW/cm² with an induced voltage of 0.71V. Our findings not only provide a futuristic clean power generation concept from protein film as flexible power generator but also demonstrate the use of the energy harvesting devices as self-biased electronic sensors for various flexible and wearable applications. The devices showed exceptional performance as humidity sensors and have been used for flexible healthcare applications, such as, continuous monitoring of breathing pattern, lateral mapping of moisture levels at the finger tip for monitoring wound healing process. Nevertheless, the diode like response of the devices with humidity has been found to be suitable as self-biased humidity controlled electronic switch.
Conference Paper
Pneumonia has been labeled as the single largest cause of child mortality for the children under five in developing countries around the world. We propose a novel method to continuously monitor parameters like Respiration Rate, Heart Rate, Blood Oxygenation (SpO2) and Body Temperature in a noninvasive and nonobtrusive manner behind the ear. The data is streamed using WiFi to the parent's smartphone or a smart gateway device which uploads it to a server. This paper also explores the opportunities for presenting patient vital signs from such a device to the remote health care workers and doctors. A prototype named Raksh was developed and various sensors were evaluated. The final bill of material cost of the device would be 23 US dollars.
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Pervasiveness of mobile phones and the fact that the phones have sensors make them ideal as personal sensors. Smart phones are equipped with a wide range of motion, location and environment sensors, that allow us to analyze, model and predict mobility in urban areas. Raw sensory data is being collected as time-stamped sequences of records, and this data needs to be preprocessed and aggregated before any predictive modeling can be done. This paper presents a case study in preprocessing such data, collected by one person over six months period. Our goal with this exploratory pilot study is to discuss data aggregation challenges from machine learning point of view, and identify relevant directions for future research in preprocessing mobile sensing data for human mobility analysis.
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Background: There is lack of information in the medical literature on predictors of hypoxemia in severely malnourished children with pneumonia, although hypoxemia is common and is often associated with fatal outcome in this population. We explored the predictors of hypoxemia in under-five children who were hospitalized for the management of pneumonia and severe acute malnutrition (SAM). Methods: In this unmatched case-control design, SAM children of both sexes, aged 0-59 months, admitted to the Dhaka Hospital of the International Centre for Diarrhoeal Disease Research, Bangladesh (ICDDR,B) with radiological pneumonia and hypoxemia during April 2011 to April 2012 were studied. SAM children with pneumonia and hypoxemia (SpO(2)<90%) constituted the cases (n = 37), and randomly selected SAM children with pneumonia but without hypoxemia constituted controls (n = 111). Results: The case-fatality was significantly higher among the cases than the controls (30% vs. 4%; p<0.001). In logistic regression analysis, after adjusting for potential confounders such as nasal flaring, head nodding, inability to drink, and crackles in lungs, fast breathing (95% CI = 1.09-13.55), lower chest wall in-drawing (95% CI = 2.48-43.41), and convulsion at admission (95% CI = 3.14-234.01) were identified as independent predictors of hypoxemia in this population. The sensitivity of fast breathing, lower chest wall in-drawing and convulsion at admission and their 95% confidence intervals (CI) to predict hypoxemia were 84 (67-93)%, 89 (74-96)%, and 19 (9-36)% respectively, and their specificity were 53 (43-63)%, 60 (51-69)% and 98 (93-100)% respectively. Conclusion and significance: Fast breathing and lower chest wall in-drawing were the best predictors of hypoxemia in SAM children with pneumonia. There thus, in resources poor settings where pulse oximetry is not available, identification of these simple clinical predictors of hypoxemia in such children could be reliably used for early O(2) supplementation in addition to other appropriate management to reduce morbidity and deaths.
The "Bible on Anesthesia Equipment" returns in a new Fifth Edition, and once again takes readers step-by-step through all the basic anesthesia equipment. This absolute leader in the field includes comprehensive references and detailed discussions on the scientific fundamentals of anesthesia equipment, its design, and its optimal use. This thoroughly updated edition includes new information on suction devices, the magnetic resonance imaging environment, temperature monitoring and control, double-lumen tubes, emergency room airway equipment, and many other topics. Readers will have access to an online quizbank at a companion Website.
Acute lower respiratory tract infections are a common cause of morbidity and mortality in children in the less developed countries. Considering the urgent need for rational protocols for the management of these infections in children and how little is known about the clinical signs that might predict the need for antibiotic therapy in a primary health care setting, a prospective study of the clinical signs in 200 paediatric outpatients presenting with a cough, 100 age-matched controls without cough, and 50 children admitted to hospital with pneumonia was carried out.In children with cough, a respiratory rate greater than 40 or 50 per minute (or a qualitative impression of tachypnoea) is probably the best indicator of the need for starting antibiotic treatment by primary health workers. The presence of fever appeared to be a poor guide to the need for antibiotic therapy. The presence of chest indrawing is, however, a reliable indication that a child with cough should be admitted to a health centre or a hospital. Further prospective studies are needed to determine the ability of these clinical signs to predict the course of these infections.
This is the second of five papers in the child survival series. The first focused on continuing high rates of child mortality (over 10 million each year) from preventable causes: diarrhoea, pneumonia, measles, malaria, HIV/AIDS, the underlying cause of undernutrition, and a small group of causes leading to neonatal deaths. We review child survival interventions feasible for delivery at high coverage in low-income settings, and classify these as level 1 (sufficient evidence of effect), level 2 (limited evidence), or level 3 (inadequate evidence). Our results show that at least one level-1 intervention is available for preventing or treating each main cause of death among children younger than 5 years, apart from birth asphyxia, for which a level-2 intervention is available. There is also limited evidence for several other interventions. However, global coverage for most interventions is below 50%. If level 1 or 2 interventions were universally available, 63% of child deaths could be prevented. These findings show that the interventions needed to achieve the millennium development goal of reducing child mortality by two-thirds by 2015 are available, but that they are not being delivered to the mothers and children who need them.
  • T Wardlaw
  • E Johansson
T. Wardlaw, E. Johansson et al., Pneumonia: the Forgotten Killer of Children, World Health Organization press, 2006.
Low cast diagnostics based on robust sensors and mobile phones
  • G Woodrow
G. Woodrow. (October 2011). Low cast diagnostics based on robust sensors and mobile phones. Australasian Biotechnology. 21 (3). pp. 15. Available: [Online].
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G. Morgan and M. Mikhail, Clinical Anesthesiology, 4th ed, Mcgraw Hill Press, 2006, pp. 44-90.
Her research interest is in bio-medical engineering
International Journal of Engineering and Technology, Vol. 8, No. 4, April 2016 Her research interest is in bio-medical engineering.
She received her master's degree in electrical engineering from University of Queensland
  • Xingjuan Li Was Born In
Xingjuan Li was born in 1990. She received her master's degree in electrical engineering from University of Queensland. Brisbane, Queensland, Australia, in 2014.
Pneumonia: the Forgotten Killer of Children
  • T Wardlaw
  • E Johansson
T. Wardlaw, E. Johansson et al., Pneumonia: the Forgotten Killer of Children, World Health Organization press, 2006.
How many child deaths can we prevent this year
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  • R Stekette
  • R Black
  • Z Bhutta
  • S Morris
G. Jones, R. Stekette, R. Black, Z. Bhutta, and S. Morris, "How many child deaths can we prevent this year," The Lancet, vol. 362, pp. 65-71. July 2003.