Smartphone Movement Sensors for Remote Monitoring of Respiratory Rate: Observational Study (Preprint)

2021 ◽  
Author(s):  
Sophie Valentine ◽  
Benjamin Klasmer ◽  
Mohammad Dabbah ◽  
Marko Balabanovic ◽  
DAVID PLANS
Keyword(s):  
Respiratory Rate ◽  
Reference Method ◽  
Absolute Error ◽  
Smartphone Application ◽  
Healthcare Systems ◽  
Economic Benefits ◽  
Preliminary Evaluation ◽  
The Novel ◽  
In The Wild ◽  
Movement Sensors

BACKGROUND Mobile health (mHealth) offers notable potential clinical and economic benefits to patients and healthcare systems alike. Although respiratory rate (RR) is of great clinical significance, existing remote technologies to measure RR suffer from limitations, such as cost, accessibility and reliability. Using smartphone movement sensors to measure RR may offer a potential solution to these shortcomings. OBJECTIVE The aim of this study was to conduct a comprehensive and ecologically valid assessment of a novel mHealth smartphone application designed to measure RR using movement sensors. METHODS Study 1 offered a preliminary evaluation, in which RR measurements from 15 participants generated via the mHealth app were compared to simultaneous measurements from a reference device cleared by the US Food and Drug Administration (FDA). Participants’ ability to successfully operate the app was also determined. Finally, a novel reference method, that would allow accuracy of the mHealth app to be investigated ‘in the wild’, was assessed for validity against the FDA-cleared reference. In Study 2, 165 participants of balanced demographics remotely downloaded the mHealth app and measured their RR. Measures from the mHealth app were compared to the novel reference that was assessed in Study 1. Usability was quantified based on the proportion of participants that were able to successfully use the app to measure their RR and standardised usability scales. RESULTS Outcomes from Study 1 supported further assessment of the mHealth app, including as assessed by the novel reference. The mHealth app, when compared to the FDA-cleared and novel references, respectively, showed a mean absolute error (MAE) of 1.65 (standard deviation (SD) = 1.49) and 1.14 (1.44), relative MAE of 12.2 (9.23) and 9.5 (18.70) and bias of 0.81 (limits of agreement (LoA) = -3.27-4.89) and 0.08 (-3.68-3.51). Pearson Product Moment Correlation (PPMC) coefficients were 0.700 and 0.885. 93% of participants could successfully operate the device on their first use and standardised usability scores were above industry averages. CONCLUSIONS The accuracy and usability of the mHealth app demonstrated in this research hold promise for the use of mHealth solutions employing smartphone movement sensors to remotely monitor RR. Considering methodological limitations, further research should be undertaken to more holistically validate the benefits that this technology may offer patients and healthcare systems.

scholarly journals Smartphone Movement Sensors for Remote Monitoring of Respiratory Rate: Observational Study

2021 ◽  
Author(s):  
Sophie Valentine ◽  
Benjamin Klasmer ◽  
Mohammad Dabbah ◽  
Marko Balabanovic ◽  
David Plans
Keyword(s):  
Respiratory Rate ◽  
Pearson Correlation ◽  
Correlation Coefficients ◽  
Reference Method ◽  
Absolute Error ◽  
Smartphone Application ◽  
Healthcare Systems ◽  
In The Wild ◽  
Smartphone Sensors ◽  
Movement Sensors

AbstractBackgroundMobile health offers potential benefits to patients and healthcare systems alike. Existing remote technologies to measure respiratory rate (RR) have limitations, such as cost, accessibility and reliability. Using smartphone sensors to measure RR may offer a potential solution.ObjectiveThe aim of this study was to conduct a comprehensive assessment of a novel mHealth smartphone application designed to measure RR using movement sensors.MethodsIn Study 1, 15 participants simultaneously measured their RR with the app, and an FDA cleared reference device. A novel reference method to allow the app to be evaluated ‘in the wild’ was also developed. In Study 2, 165 participants measured their RR using the app, and these measures were compared to the novel reference. Usability of the app was also assessed in both studies.ResultsThe app, when compared to the FDA-cleared and novel references, respectively, showed a mean absolute error (MAE) of 1.65 (SD=1.49) and 1.14 (1.44), relative MAE of 12.2 (9.23) and 9.5 (18.70) and bias of 0.81 (limits of agreement (LoA) =-3.27-4.89) and 0.08 (−3.68-3.51). Pearson correlation coefficients were 0.700 and 0.885. 93% of participants successfully operated the app on their first use.ConclusionsThe accuracy and usability of the app demonstrated here show promise for the use of mHealth solutions employing smartphone sensors to remotely monitor RR. Further research should validate the benefits that this technology may offer patients and healthcare systems.

scholarly journals Estimation of Motion and Respiratory Characteristics during the Meditation Practice Based on Video Analysis

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3771
Author(s):  
Alexey Kashevnik ◽  
Walaa Othman ◽  
Igor Ryabchikov ◽  
Nikolay Shilov
Keyword(s):  
Respiratory Rate ◽  
Evaluation System ◽  
Body Movement ◽  
Absolute Error ◽  
The Body ◽  
Meditation Practice ◽  
Body Parts ◽  
Mental Health Training ◽  
Negative Thoughts ◽  
The Mean

Meditation practice is mental health training. It helps people to reduce stress and suppress negative thoughts. In this paper, we propose a camera-based meditation evaluation system, that helps meditators to improve their performance. We rely on two main criteria to measure the focus: the breathing characteristics (respiratory rate, breathing rhythmicity and stability), and the body movement. We introduce a contactless sensor to measure the respiratory rate based on a smartphone camera by detecting the chest keypoint at each frame, using an optical flow based algorithm to calculate the displacement between frames, filtering and de-noising the chest movement signal, and calculating the number of real peaks in this signal. We also present an approach to detecting the movement of different body parts (head, thorax, shoulders, elbows, wrists, stomach and knees). We have collected a non-annotated dataset for meditation practice videos consists of ninety videos and the annotated dataset consists of eight videos. The non-annotated dataset was categorized into beginner and professional meditators and was used for the development of the algorithm and for tuning the parameters. The annotated dataset was used for evaluation and showed that human activity during meditation practice could be correctly estimated by the presented approach and that the mean absolute error for the respiratory rate is around 1.75 BPM, which can be considered tolerable for the meditation application.

scholarly journals SARS-CoV-2 sample-to-answer nucleic acid testing in a tertiary care emergency department: evaluation and utility

2020 ◽  
Author(s):  
Pia Jokela ◽  
Anu E Jääskeläinen ◽  
Hanna Jarva ◽  
Tanja Holma ◽  
Maarit J Ahava ◽  
...  
Keyword(s):  
Nucleic Acid ◽  
Emergency Departments ◽  
Large Scale ◽  
Tertiary Care ◽  
Reference Method ◽  
Preliminary Evaluation ◽  
Specificity And Sensitivity ◽  
Department Evaluation ◽  
Test Result ◽  
Respiratory Specimens

AbstractRapid sample-to-answer tests for detection of SARS-CoV-2 are emerging and data on their relative performance is urgently needed. We evaluated the analytical performance of two rapid nucleic acid tests, Cepheid Xpert® Xpress SARS-CoV-2 and Mobidiag Novodiag® Covid-19, in comparison to a combination reference of three large-scale PCR tests. Moreover, utility of the Novodiag® test in tertiary care emergency departments was assessed. In the preliminary evaluation, analysis of 90 respiratory samples resulted in 100% specificity and sensitivity for Xpert®, whereas analysis of 107 samples resulted in 93.4% sensitivity and 100% specificity for Novodiag®. Rapid SARS-CoV-2 testing with Novodiag® was made available for four tertiary care emergency departments in Helsinki, Finland between 18 and 31 May, coinciding with a rapidly declining epidemic phase. Altogether 361 respiratory specimens, together with relevant clinical data, were analyzed with Novodiag® and reference tests: 355/361 of the specimens were negative with both methods, and 1/361 was positive in Novodiag® and negative by the reference method. Of the 5 remaining specimens, two were negative with Novodiag®, but positive with the reference method with late Ct values. On average, a test result using Novodiag® was available nearly 8 hours earlier than that obtained with the large-scale PCR tests. While the performance of novel sample-to-answer PCR tests need to be carefully evaluated, they may provide timely and reliable results in detection of SARS-CoV-2 and thus facilitate patient management including effective cohorting.

scholarly journals Derivation and validation of an easy-to-compute trauma score that improves prognostication of mortality or the Trauma Rating Index in Age, Glasgow Coma Scale, Respiratory rate and Systolic blood pressure (TRIAGES) score

Critical Care ◽  
2019 ◽  
Vol 23 (1) ◽  
Author(s):  
Atsushi Shiraishi ◽  
Yasuhiro Otomo ◽  
Shunsuke Yoshikawa ◽  
Koji Morishita ◽  
Ian Roberts ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Glasgow Coma Scale ◽  
Systolic Blood Pressure ◽  
Respiratory Rate ◽  
Validation Cohort ◽  
Hospital Death ◽  
The Novel ◽  
Coma Scale ◽  
Physiological Variables ◽  
Trauma Score

Abstract Background Multiple trauma scores have been developed and validated, including the Revised Trauma Score (RTS) and the Mechanism, Glasgow Coma Scale, Age, and Arterial Pressure (MGAP) score. However, these scores are complex to calculate or have low prognostic abilities for trauma mortality. Therefore, we aimed to develop and validate a trauma score that is easier to calculate and more accurate than the RTS and the MGAP score. Methods The study was a retrospective prognostic study. Data from patients registered in the Japan Trauma Databank (JTDB) were dichotomized into derivation and validation cohorts. Patients’ data from the Clinical Randomisation of an Antifibrinolytic in Significant Haemorrhage-2 (CRASH-2) trial were assigned to another validation cohort. We obtained age and physiological variables at baseline, created ordinal variables from continuous variables, and defined integer weighting coefficients. Score performance to predict all-cause in-hospital death was assessed using the area under the curve in receiver operating characteristics (AUROC) analyses. Results Based on the JTDB derivation cohort (n = 99,867 with 12.5% mortality), the novel score ranged from 0 to 14 points, including 0–2 points for age, 0–6 points for the Glasgow Coma Scale, 0–4 points for systolic blood pressure, and 0–2 points for respiratory rate. The AUROC of the novel score was 0.932 for the JTDB validation cohort (n = 76,762 with 10.1% mortality) and 0.814 for the CRASH-2 cohort (n = 19,740 with 14.6% mortality), which was superior to RTS (0.907 and 0.808, respectively) and MGAP score (0.918 and 0.774, respectively) results. Conclusions We report an easy-to-use trauma score with better prognostication ability for in-hospital mortality compared to the RTS and MGAP score. Further studies to test clinical applicability of the novel score are warranted.

scholarly journals Non-contact physiological monitoring of preterm infants in the Neonatal Intensive Care Unit

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Mauricio Villarroel ◽  
Sitthichok Chaichulee ◽  
João Jorge ◽  
Sara Davis ◽  
Gabrielle Green ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Heart Rate ◽  
Respiratory Rate ◽  
Preterm Infants ◽  
Neonatal Intensive Care ◽  
Mean Absolute Error ◽  
Vital Signs ◽  
Absolute Error ◽  
Video Camera ◽  
The Mean

AbstractThe implementation of video-based non-contact technologies to monitor the vital signs of preterm infants in the hospital presents several challenges, such as the detection of the presence or the absence of a patient in the video frame, robustness to changes in lighting conditions, automated identification of suitable time periods and regions of interest from which vital signs can be estimated. We carried out a clinical study to evaluate the accuracy and the proportion of time that heart rate and respiratory rate can be estimated from preterm infants using only a video camera in a clinical environment, without interfering with regular patient care. A total of 426.6 h of video and reference vital signs were recorded for 90 sessions from 30 preterm infants in the Neonatal Intensive Care Unit (NICU) of the John Radcliffe Hospital in Oxford. Each preterm infant was recorded under regular ambient light during daytime for up to four consecutive days. We developed multi-task deep learning algorithms to automatically segment skin areas and to estimate vital signs only when the infant was present in the field of view of the video camera and no clinical interventions were undertaken. We propose signal quality assessment algorithms for both heart rate and respiratory rate to discriminate between clinically acceptable and noisy signals. The mean absolute error between the reference and camera-derived heart rates was 2.3 beats/min for over 76% of the time for which the reference and camera data were valid. The mean absolute error between the reference and camera-derived respiratory rate was 3.5 breaths/min for over 82% of the time. Accurate estimates of heart rate and respiratory rate could be derived for at least 90% of the time, if gaps of up to 30 seconds with no estimates were allowed.

scholarly journals Non-contact vital-sign monitoring of patients undergoing haemodialysis treatment

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Mauricio Villarroel ◽  
João Jorge ◽  
David Meredith ◽  
Sheera Sutherland ◽  
Chris Pugh ◽  
...  
Keyword(s):  
Heart Rate ◽  
Respiratory Rate ◽  
Reference Values ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Recording Time ◽  
Wide Range ◽  
Auto Regressive ◽  
The Mean ◽  
Haemodialysis Treatment

Abstract A clinical study was designed to record a wide range of physiological values from patients undergoing haemodialysis treatment in the Renal Unit of the Churchill Hospital in Oxford. Video was recorded for a total of 84 dialysis sessions from 40 patients during the course of 1 year, comprising an overall video recording time of approximately 304.1 h. Reference values were provided by two devices in regular clinical use. The mean absolute error between the heart rate estimates from the camera and the average from two reference pulse oximeters (positioned at the finger and earlobe) was 2.8 beats/min for over 65% of the time the patient was stable. The mean absolute error between the respiratory rate estimates from the camera and the reference values (computed from the Electrocardiogram and a thoracic expansion sensor—chest belt) was 2.1 breaths/min for over 69% of the time for which the reference signals were valid. To increase the robustness of the algorithms, novel methods were devised for cancelling out aliased frequency components caused by the artificial light sources in the hospital, using auto-regressive modelling and pole cancellation. Maps of the spatial distribution of heart rate and respiratory rate information were developed from the coefficients of the auto-regressive models. Most of the periods for which the camera could not produce a reliable heart rate estimate lasted under 3 min, thus opening the possibility to monitor heart rate continuously in a clinical environment.

scholarly journals Plasmid DNA Production in Proteome-Reduced Escherichia coli

2020 ◽  
Vol 8 (9) ◽  
pp. 1444
Author(s):  
Mitzi de la Cruz ◽  
Elisa A. Ramírez ◽  
Juan-Carlos Sigala ◽  
José Utrilla ◽  
Alvaro R. Lara
Keyword(s):  
Escherichia Coli ◽  
Plasmid Dna ◽  
Type Strain ◽  
Wild Type Strain ◽  
The Novel ◽  
Wild Type ◽  
Batch Mode ◽  
Cell Factories ◽  
Starting Point ◽  
In The Wild

The design of optimal cell factories requires engineering resource allocation for maximizing product synthesis. A recently developed method to maximize the saving in cell resources released 0.5% of the proteome of Escherichia coli by deleting only three transcription factors. We assessed the capacity for plasmid DNA (pDNA) production in the proteome-reduced strain in a mineral medium, lysogeny, and terrific broths. In all three cases, the pDNA yield from biomass was between 33 and 53% higher in the proteome-reduced than in its wild type strain. When cultured in fed-batch mode in shake-flask, the proteome-reduced strain produced 74.8 mg L−1 pDNA, which was four times greater than its wild-type strain. Nevertheless, the pDNA supercoiled fraction was less than 60% in all cases. Deletion of recA increased the pDNA yields in the wild type, but not in the proteome-reduced strain. Furthermore, recA mutants produced a higher fraction of supercoiled pDNA, compared to their parents. These results show that the novel proteome reduction approach is a promising starting point for the design of improved pDNA production hosts.

scholarly journals A Novel Mutual Information Based Feature Set for Drivers’ Mental Workload Evaluation Using Machine Learning

2020 ◽  
Vol 10 (8) ◽  
pp. 551
Author(s):  
Mir Riyanul Islam ◽  
Shaibal Barua ◽  
Mobyen Uddin Ahmed ◽  
Shahina Begum ◽  
Pietro Aricò ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mutual Information ◽  
Mental Workload ◽  
Contextual Information ◽  
Absolute Error ◽  
The Novel ◽  
Promising Technique ◽  
Extensive Evaluation ◽  
Target Values ◽  
Classification Tasks

Analysis of physiological signals, electroencephalography more specifically, is considered a very promising technique to obtain objective measures for mental workload evaluation, however, it requires a complex apparatus to record, and thus, with poor usability in monitoring in-vehicle drivers’ mental workload. This study proposes a methodology of constructing a novel mutual information-based feature set from the fusion of electroencephalography and vehicular signals acquired through a real driving experiment and deployed in evaluating drivers’ mental workload. Mutual information of electroencephalography and vehicular signals were used as the prime factor for the fusion of features. In order to assess the reliability of the developed feature set mental workload score prediction, classification and event classification tasks were performed using different machine learning models. Moreover, features extracted from electroencephalography were used to compare the performance. In the prediction of mental workload score, expert-defined scores were used as the target values. For classification tasks, true labels were set from contextual information of the experiment. An extensive evaluation of every prediction tasks was carried out using different validation methods. In predicting the mental workload score from the proposed feature set lowest mean absolute error was 0.09 and for classifying mental workload highest accuracy was 94%. According to the outcome of the study, it can be stated that the novel mutual information based features developed through the proposed approach can be employed to classify and monitor in-vehicle drivers’ mental workload.

scholarly journals ID3: PRELIMINARY EVALUATION OF THE CLINICAL AND ECONOMIC BENEFITS OF UNIVERSAL VARICELLA VACCINATION OF CHILDREN IN GERMANY

2000 ◽  
Vol 3 (5) ◽  
pp. 297 ◽  
Author(s):  
K Banz ◽  
U Staginnus ◽  
S Wagenpfeil ◽  
A Neiss ◽  
A Goertz ◽  
...  
Keyword(s):  
Economic Benefits ◽  
Varicella Vaccination ◽  
Preliminary Evaluation

scholarly journals Automated Quantification of Macronutrients using Computer Vision on a Depth-Sensing Smartphone (Preprint)

2019 ◽  
Author(s):  
David Herzig ◽  
Christos T Nakas ◽  
Janine Stalder ◽  
Christophe Kosinski ◽  
Céline Laesser ◽  
...  
Keyword(s):  
Computer Vision ◽  
Processing Time ◽  
Energy Content ◽  
Absolute Error ◽  
Smartphone Application ◽  
Estimation Accuracy ◽  
Depth Sensor ◽  
Depth Sensing ◽  
Food Items ◽  
Macronutrient Content

BACKGROUND Quantification of dietary intake is key to the prevention and management of numerous metabolic disorders. Conventional approaches are challenging, laborious, and, suffer from lack of accuracy. The recent advent of depth-sensing smartphones in conjunction with computer vision has the potential to facilitate reliable quantification of food intake. OBJECTIVE To evaluate the accuracy of a novel smartphone application combining depth-sensing hardware with computer vision to quantify meal macronutrient content. METHODS The application ran on a smartphone with built-in depth sensor applying structured light (iPhone X) and estimated weight, macronutrient (carbohydrate, protein, fat) and energy content of 48 randomly chosen meals (type of meals: breakfast, cooked meals, snacks) encompassing 128 food items. Reference weight was generated by weighing individual food items using a precision scale. The study endpoints were fourfold: i) error of estimated meal weight; ii) error of estimated meal macronutrient content and energy content; iii) segmentation performance; and iv) processing time. RESULTS Mean±SD absolute error of the application’s estimate was 35.1±42.8g (14.0±12.2%) for weight, 5.5±5.1g (14.8±10.9%) for carbohydrate content, 2.4±5.6g (13.0±13.8%), 1.3±1.7g (12.3±12.8%) for fat content and 41.2±42.5kcal (12.7±10.8%) for energy content. While estimation accuracy was not affected by the viewing angle, the type of meal mattered with slightly worse performance for cooked meals compared to breakfast and snack. Segmentation required adjustment for 7 out of 128 items. Mean±SD processing time across all meals was 22.9±8.6s. CONCLUSIONS The present study evaluated the accuracy of a novel smartphone application with integrated depth-sensing camera and found a high accuracy in food estimation across all macronutrients. This was paralleled by a high segmentation performance and low processing time corroborating the high usability of this system.

Sign in / Sign up

Export Citation Format

Share Document