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SmartPhone for measuring Vital Signs
Source publication
The human body exhibits many vital signs, such
as heart rate (HR) and respiratory rate (RR) used to assess
fitness and health. Vital signs are typically measured by a trained
health professional and may be difficult for individuals to
accurately measure at home. Clinic visits are therefore needed
with associated burdens of cost and time spent waiti...
Contexts in source publication
Context 1
... of going to the physician for checking heart rate, SPO2, blood pressure, body temperature and respiration rate it would be excellent if it could be measured at home. There are special devices made for this but everyone cannot reach to it or purchase them as they are not cost effective. Therefore, main area of focus through which these signs can be monitored and used by everyone is through the camera in mobile devices which everyone can use easily as shown by arrows in fig ...
Context 2
... vital signs such as heart rate, blood pressure, body temperature, respiration rate, and pain communicate important information about the physiological status of the human body. If these vital signs are to be monitored the person has to go its physician for the body check-ups. In 20th century technology is growing very fast and human beings are getting addicted to this technology e.g. Smart Phones. Fig. 1 shows different vital signs which have been worked by ...
Context 3
... temperature, respiration rate, and pain communicate important information about the physiological status of the human body. If these vital signs are to be monitored the person has to go its physician for the body check-ups. In 20th century technology is growing very fast and human beings are getting addicted to this technology e.g. Smart Phones. Fig. 1 shows different vital signs which have been worked by ...
Context 4
... are special devices made for this but everyone cannot reach to it or purchase them as they are not cost effective. Therefore, main area of focus through which these signs can be monitored and used by everyone is through the camera in mobile devices which everyone can use easily as shown by arrows in fig 1.. ...
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Objectives
To examine the severity and progression of acute illness or injury in children using vital signs obtained during ambulance transport and categorised according to a paediatric triage model.
Design
A population-based historical cohort study using data from prehospital patient medical records linked to a national civil registration databas...
Citations
... In order to better prevent motion artifacts, Ding et al. and companions suggested a preprocessing phase and convolutional neural network-based approach for measuring SaO2 using a smartphone [13]. Sinhal et al. and associates conducted a survey on estimating vital signs through noncontact video-based approaches [14]. In addition to reviewing the many image and video processing algorithms created for non-contact vital sign evaluation, their work identified the main unresolved issues in the field that could serve as future research subjects. ...
In order to determine human oxygen saturation (SpO 2 ) levels, this research provides an innovative approach that uses photoplethysmography (PPG) signals acquired from mobile video. Conventional methods of measuring SpO 2 frequently require specialist equipment, making them less accessible and useful. The suggested method entails taking PPG signals out of RGB channels and smartphone camera frames, then pre-processing and de-noising the signals to improve their quality. The preprocessed signals are then used to extract signal properties, including peak, mean, standard deviation, AC value, and RMS value. Following that, these features are fed into a number of machine learning models, such as logistic regression, decision tree regression, random forest regression, support vector regression (SVR), and K-neighbors regression. This strategy makes use of smartphones' sophisticated image processing capabilities and integrated cameras to take advantage of their ubiquitous use. A medically approved pulse oximeter is used to monitor blood oxygen saturation levels as a baseline, providing a comparison reference point. When the five regression models are applied to a collected dataset, the Logistic Regression Model exhibits the lowest Mean Absolute Error (MAE), of about 0.845. The suggested approach, which provides a cheap and portable substitute for SpO 2 monitoring, shows promise for broad adoption in healthcare settings, especially in settings with limited resources.
... The traditional measurement of these vital signs through physical examination can be challenging, and the recent pandemic has accelerated trends toward telehealth and remote monitoring. Instead of going to the physician to check HR, SpO 2 , BP, body temperature, and RR, it would be convenient to measure them at home [1]. Vital sign monitors, also known as physiological parameter monitors, are electronic devices that measure and display biological information about patients under constant monitoring [2]. ...
Background:
The traditional measurement of these vitals via physical examination has become challenging and the recent pandemic has accelerated trends towards telehealth and remote monitoring. Instead of going to the physician for checking Heart Rate, Oxygen Saturation (SPO2), Blood Pressure, Body Temperature and Respiration Rate it would be excellent if it could be measured at home. Vital signs monitors, also known as physiological parameter monitors, are electronic devices that measure and display biological information about patients under constant monitoring.
Objective:
The purpose of this study was to validate the accuracy of the Pulse Oxygen Saturation, Heart Rate, Blood Pressure and Respiratory Rate of Docsun Telehealth by comparing it with approved medical devices.
Methods:
This is a non-invasive self-check system-based study which was conducted to validate the detection of vital signs (Pulse Oxygen Saturation, Heart Rate, Blood Pressure and Respiratory Rate) raised by the Docsun Telehealth Portal. The input for the software processing is a facial screening without any accessories on the face, directly scanning through the software application portal. The subject's facial features are detected and screened for the extraction of necessary reading.
Results:
For the HR, SPO2, BP and RR measurement validation, the main outcome was the mean of the absolute difference between the respective investigational devices and the reference values and the percent absolute difference between the respective investigational devices and the reference values. If the HR was within ±10% of the reference standard or 5 beats/min, it was considered to be acceptable for clinical purposes. The average absolute difference between the Docsun Telehealth Portal and the reference values was 1.41 bpm. The mean absolute percentage difference was 1.69%. The Docsun Telehealth Portal therefore met the predefined accuracy cutoff for HR measurements. If the RR was within ±10% of the reference standard or 3 breaths/min, it was considered to be acceptable for clinical purposes. The average absolute difference between the Docsun Telehealth Portal was 0.86 breaths/minute. The absolute percentage difference was 4.72%. The Docsun Telehealth Portal therefore met the predefined accuracy cutoff for RR measurements. SPO2 levels were considered acceptable if the average absolute difference between the Docsun Telehealth Portal and the reference values was ±3%. The mean absolute percentage difference was 0.59%. The Docsun Telehealth Portal therefore met the predefined accuracy cutoff for SPO2 measurements. The Docsun Telehealth Portal predicted systolic BP with an accuracy of 94.81% and diastolic BP with an accuracy of 95.71%.
Conclusions:
The results of the study shows that the accuracy of the Heart Rate, Blood Pressure, SPO2 and Respiratory Rate values raised by the Docsun Telehealth Portal compared against the clinically approved medical device proved to be accurate by meeting the predefined accuracy guidelines.
... Currently, some noncontact monitoring technologies utilize camera sensors [2,3]. These technologies can extract vital parameters, such as heart and respiration rates, via video signal processing, offering unique value in clinical and telehealth settings [4]. However, there are concerns regarding the accuracy of camera sensors in certain applications, and extended usage might pose privacy issues. ...
Noncontact heart rate monitoring techniques based on millimeter-wave radar have advantages in unique medical scenarios. However, the accuracy of the existing noncontact heart rate estimation methods is still limited by interference, such as DC offsets, respiratory harmonics, and environmental noise. Additionally, these methods still require longer observation times. Most deep learning methods related to heart rate estimation still need to collect more heart rate marker data for training. To address the above problems, this paper introduces a radar signal-based heart rate estimation network named the “masked phase autoencoders with a vision transformer network” (MVN). This network is grounded on masked autoencoders (MAEs) for self-supervised pretraining and a vision transformer (ViT) for transfer learning. During the phase preprocessing stage, phase differencing and interpolation smoothing are performed on the input phase signal. In the self-supervised pretraining step, masked self-supervised training is performed on the phase signal using the MAE network. In the transfer learning stage, the encoder segment of the MAE network is integrated with the ViT network to enable transfer learning using labeled heart rate data. The innovative MVN offers a dual advantage—it not only reduces the cost associated with heart rate data acquisition but also adeptly addresses the issue of respiratory harmonic interference, which is an improvement over conventional signal processing methods. The experimental results show that the process in this paper improves the accuracy of heart rate estimation while reducing the requisite observation time.
... Some non-contact solutions employ camera sensors. Through video signal processing, it is indeed possible to extract parameters such as heart rate (HR) and respiration rate (RR), which can be particularly useful in clinical or telehealth consultations [9,10]. The use of camera sensors, however, can be inadequate in many applications, leading, especially in long-term monitoring, to serious privacy concerns. ...
Citation: Mauro, G.; De Carlos Diez, M.; Ott, J.; Servadei, L.; Cuellar, M.P.; Morales-Santos, D.P. Few-Shot User-Adaptable Radar-Based Breath Signal Sensing. Sensors 2023, 23, 804. Abstract: Vital signs estimation provides valuable information about an individual's overall health status. Gathering such information usually requires wearable devices or privacy-invasive settings. In this work, we propose a radar-based user-adaptable solution for respiratory signal prediction while sitting at an office desk. Such an approach leads to a contact-free, privacy-friendly, and easily adaptable system with little reference training data. Data from 24 subjects are preprocessed to extract respiration information using a 60 GHz frequency-modulated continuous wave radar. With few training examples, episodic optimization-based learning allows for generalization to new individuals. Episodically, a convolutional variational autoencoder learns how to map the processed radar data to a reference signal, generating a constrained latent space to the central respiration frequency. Moreover, autocorrelation over recorded radar data time assesses the information corruption due to subject motions. The model learning procedure and breathing prediction are adjusted by exploiting the motion corruption level. Thanks to the episodic acquired knowledge, the model requires an adaptation time of less than one and two seconds for one to five training examples, respectively. The suggested approach represents a novel, quickly adaptable, non-contact alternative for office settings with little user motion.
... At present, most algorithms detect vital signs of the human body through computer recorded videos. However, the widespread use of smart phones with video recording function provides an opportunity for the integration of rPPG methods to create non-invasive vital signs assessment (e.g., via an app download) [6]. Estimating the HR through rPPG methods by the front camera of smartphone device exist now [7,8]. ...
Remote photoplethysmography (rPPG), which aims at measuring heart activities without any contact, has great potential in many applications. The emergence of novel coronavirus pneumonia COVID-19 has attracted worldwide attentions. Contact photoplethysmography (cPPG) methods need to contact the detection equipment with the patient, which may accelerate the spread of the epidemic. In the future, the non-contact heart rate detection will be an urgent need. However, existing heart rate measuring methods from facial videos are vulnerable to the less-constrained scenarios (e.g., with head movement and wearing a mask). In this paper, we proposed a method of heart rate detection based on eye location of region of interest (ROI) to solve the problem of missing information when wearing masks. Besides, a model to filter outliers based on residual network was conceived first by us and the better heart rate measurement accuracy was generated. To validate our method, we also created a mask dataset. The results demonstrated that after using our method for correcting the heart rate (HR) value measured with the traditional method, the accuracy reaches 4.65 bpm, which is 0.42 bpm higher than that without correction.
... All the studies carried out on Photoplethysmographic imaging have greatly improved its performance in terms of reliability and robustness in case of controlled condition (good lighting and motionless subject) [12,17,4,20,18]. However, at present most of these methods present a weakness in the case of uncontrolled measurement conditions, in particular the subject's motions and low lighting conditions as well as very dark skin (phototype 6) [1,14]. In this field, deep learning based methods show better performance than conventional state-of-the-art algorithms based on image and signal processing [11,21]. ...
We present in this paper the LCOMS Lab's approach to the 1st Vision For Vitals (V4V) Challenge organized within ICCV2021. The V4V challenge was focused on computer vision methods for vitals signs measurement from facial videos, including pulse rate (PR) and respiratory rate. We propose a novel end-to-end architecture based on a deep spatiotemporal network for pulse rate estimation from facial video recordings. Unlike existing methods, we predict the pulse rate value directly without passing by iPPG signal extraction and without incorporating any prior knowledge or additional processing steps. We built our network using 3D Depthwise Separable Convolution layers with residual connections to extract spatial and temporal features simultaneously. This is very suitable for real-time measurement because it requires a reduced number of parameters and a short video fragment. The obtained results seem very satisfactory and promising, especially since the experiments were conducted in challenging dataset collected in uncontrolled conditions.
... Currently, the use of non-contact method for measuring vital signs has gained popularity due to the flexibility and non-invasive procedure [1]. The research community is currently focused on the 5th International Conference on Electronic Design (ICED) 2020 Journal of Physics: Conference Series 1755 (2021) 012038 IOP Publishing doi: 10.1088/1742-6596/1755/1/012038 2 use of non-contact technique for the detection of breathing rate, such as magnetic induction sensing methods [2], radio-wave monitoring method [3] [4] and image-based monitoring methods [4] [5]. ...
Respiratory is one of the vital signs used to monitor the progression of the illness that are important for clinical and health care fields. From home rehabilitation to intensive care monitoring, the rate of respiration must be constantly monitored as it offers a proactive approach for early detection of patient deterioration that can be used to trigger therapeutic procedures alarms. The use of invasive procedures based on contact transducers is typically necessary to measure the quantity. Nevertheless, these procedures might be troublesome due to the inconvenience and sensitivity of physical contact. Therefore, non-contact human breathing monitoring as a non-invasive procedure is important in long term intensive-care and home healthcare applications. In this paper, respiratory signals from two type of resting activities had been acquired and proposed a Deep Neural Network (DNN) model that can classify the respiratory signal into inhalation and exhalation signal. Several pre-processing techniques has been done onto the signal before it is implemented into the proposed model. The average recognition rate of the respiratory signal using the proposed method was 84.1% when the subject was sleeping and 83.8% when awake.
... Monitoring respiration can give valuable information about neural and pulmonary conditions. In this context, monitoring human respiratory rate becomes an important task on a regular basis for monitoring the health of a person [1]. ...
... rPPG checking has two steps: region of interest detection and signal generation to detect a pulse. [4] When the pulsatile blood is circulating in the human cardiovascular system, the oxygenated blood circulation leads to fluctuations in the amount of hemoglobin molecules and proteins causing variations in the optical absorption and scattering across the light spectrum. By emitting light through the skin layers and measuring the amount of light propagating in the tissue, we can get a PPG signal that reflects blood volume changes over time. ...
Photoplethysmography measures vital signs through to extraction of signals from the body. The paper explains the technique for extraction of pulse rate from the videos for three color channels namely; red, green and blue. The DMIMS database is used for experimentation which consists of total 720 videos out of which 25 videos are used for analysis. The results presented in this paper depict that our algorithm works best for blue channel followed by green and then red channel. The main focus of paper is to extract pulse rate from the recorded video and compare the output for different channels and find the best channel for heart rate extraction.
... There are some other approaches, however they have some drawbacks. A respiration monitoring system using image processing may not be able to recognize the chest movements if the driver wears a thick coat [1]. A device sensing the flow of inhaled and exhale air requires mounting close to the driver's mouth, which is inconsistent with the tightness of the car [2]. ...
In this paper, a K-band noninvasive vital signs monitoring system for measuring respiration rate and heart rate is presented. The system consists of a 24 GHz Doppler Radar motion sensor and an ARM-based digital signal processing unit. To demonstrate an automotive application, the system is designed to be installed on the vehicle, monitor the vital signs of the drivers, and activate a warning system automatically in which the vital signs of driver is abnormal. The errors of respiration rate and heart rate measurements are 2.3% and 8.6% respectively when there is a driver in front of the sensor. The error increases to 12.5% of respiration rate and no signal detected of heart rate in case the sensor measures on the side of the driver. The measurement results are stored on an open-source data server for remote monitoring and future investigation.
