scholarly journals Extraction and Analysis of Respiratory Motion Using a Comprehensive Wearable Health Monitoring System

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1393
Author(s):  
Uduak Z. George ◽  
Kee S. Moon ◽  
Sung Q. Lee
Keyword(s):  
Tidal Volume ◽  
Respiration Rate ◽  
Spline Interpolation ◽  
Vital Signs ◽  
Area Under The Curve ◽  
Sensor Technology ◽  
Wearable Sensor ◽  
Lung Sound ◽  
Health Monitoring System ◽  
Electrocardiogram Ecg

Respiratory activity is an important vital sign of life that can indicate health status. Diseases such as bronchitis, emphysema, pneumonia and coronavirus cause respiratory disorders that affect the respiratory systems. Typically, the diagnosis of these diseases is facilitated by pulmonary auscultation using a stethoscope. We present a new attempt to develop a lightweight, comprehensive wearable sensor system to monitor respiration using a multi-sensor approach. We employed new wearable sensor technology using a novel integration of acoustics and biopotentials to monitor various vital signs on two volunteers. In this study, a new method to monitor lung function, such as respiration rate and tidal volume, is presented using the multi-sensor approach. Using the new sensor, we obtained lung sound, electrocardiogram (ECG), and electromyogram (EMG) measurements at the external intercostal muscles (EIM) and at the diaphragm during breathing cycles with 500 mL, 625 mL, 750 mL, 875 mL, and 1000 mL tidal volume. The tidal volumes were controlled with a spirometer. The duration of each breathing cycle was 8 s and was timed using a metronome. For each of the different tidal volumes, the EMG data was plotted against time and the area under the curve (AUC) was calculated. The AUC calculated from EMG data obtained at the diaphragm and EIM represent the expansion of the diaphragm and EIM respectively. AUC obtained from EMG data collected at the diaphragm had a lower variance between samples per tidal volume compared to those monitored at the EIM. Using cubic spline interpolation, we built a model for computing tidal volume from EMG data at the diaphragm. Our findings show that the new sensor can be used to measure respiration rate and variations thereof and holds potential to estimate tidal lung volume from EMG measurements obtained from the diaphragm.

scholarly journals An Early Predictive Scoring Model for In-Hospital Cardiac Arrest of Emergent Hemodialysis Patients

2021 ◽  
Vol 10 (15) ◽  
pp. 3241
Author(s):  
Shih-Hao Chen ◽  
Ya-Yun Cheng ◽  
Chih-Hao Lin
Keyword(s):  
Cardiac Arrest ◽  
Risk Group ◽  
Vital Signs ◽  
Area Under The Curve ◽  
Hemodialysis Patients ◽  
High Risk Group ◽  
Predictive Score ◽  
Primary Group ◽  
Sum Scores ◽  
Hospital Cardiac Arrest

Background: Patients undergoing hemodialysis are prone to cardiac arrests. Methods: This study aimed to develop a risk score to predict in-hospital cardiac arrest (IHCA) in emergency department (ED) patients undergoing emergency hemodialysis. Patients were included if they received urgent hemodialysis within 24 h after ED arrival. The primary outcome was IHCA within three days. Predictors included three domains: comorbidity, triage information (vital signs), and initial biochemical results. The final model was generated from data collected between 2015 and 2018 and validated using data from 2019. Results: A total of 257 patients, including 52 with IHCA, were analyzed. Statistical analysis selected significant variables with higher sensitivity cutoff, and scores were assigned based on relative beta coefficient ratio: K > 5.5 mmol/L (score 1), pH < 7.35 (score 1), oxygen saturation < 85% (score 1), and mean arterial pressure < 80 mmHg (score 2). The final scoring system had an area under the curve of 0.78 (p < 0.001) in the primary group and 0.75 (p = 0.023) in the validation group. The high-risk group (defined as sum scores ≥ 3) had an IHCA risk of 47.2% and 41.7%, while the low-risk group (sum scores < 3) had 18.3% and 7%, in the primary and validation databases, respectively. Conclusions: This predictive score model for IHCA in emergent hemodialysis patients could help healthcare providers to take necessary precautions and allocate resources.

Accuracy of Wearable Sensor Technology in Hand Goniometry: A Systematic Review

Hand ◽  
2021 ◽  
pp. 155894472110146
Author(s):  
Francisco R. Avila ◽  
Rickey E. Carter ◽  
Christopher J. McLeod ◽  
Charles J. Bruce ◽  
Davide Giardi ◽  
...  
Keyword(s):  
Systematic Review ◽  
Range Of Motion ◽  
Health Care Professionals ◽  
Inertial Sensors ◽  
Measurement Techniques ◽  
Infrared Camera ◽  
Wearable Devices ◽  
Sensor Technology ◽  
Accuracy Evaluation ◽  
Wearable Sensor

Background Wearable devices and sensor technology provide objective, unbiased range of motion measurements that help health care professionals overcome the hindrances of protractor-based goniometry. This review aims to analyze the accuracy of existing wearable sensor technologies for hand range of motion measurement and identify the most accurate one. Methods We performed a systematic review by searching PubMed, CINAHL, and Embase for studies evaluating wearable sensor technology in hand range of motion assessment. Keywords used for the inquiry were related to wearable devices and hand goniometry. Results Of the 71 studies, 11 met the inclusion criteria. Ten studies evaluated gloves and 1 evaluated a wristband. The most common types of sensors used were bend sensors, followed by inertial sensors, Hall effect sensors, and magnetometers. Most studies compared wearable devices with manual goniometry, achieving optimal accuracy. Although most of the devices reached adequate levels of measurement error, accuracy evaluation in the reviewed studies might be subject to bias owing to the use of poorly reliable measurement techniques for comparison of the devices. Conclusion Gloves using inertial sensors were the most accurate. Future studies should use different comparison techniques, such as infrared camera–based goniometry or virtual motion tracking, to evaluate the performance of wearable devices.

scholarly journals Personalized Human Activity Recognition Based on Integrated Wearable Sensor and Transfer Learning

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 885 ◽  
Author(s):  
Zhongzheng Fu ◽  
Xinrun He ◽  
Enkai Wang ◽  
Jun Huo ◽  
Jian Huang ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Sensor Node ◽  
Learning Algorithm ◽  
Joint Probability ◽  
Human Activity Recognition ◽  
Sensor Technology ◽  
Measurement Unit ◽  
Wearable Sensor

Human activity recognition (HAR) based on the wearable device has attracted more attention from researchers with sensor technology development in recent years. However, personalized HAR requires high accuracy of recognition, while maintaining the model’s generalization capability is a major challenge in this field. This paper designed a compact wireless wearable sensor node, which combines an air pressure sensor and inertial measurement unit (IMU) to provide multi-modal information for HAR model training. To solve personalized recognition of user activities, we propose a new transfer learning algorithm, which is a joint probability domain adaptive method with improved pseudo-labels (IPL-JPDA). This method adds the improved pseudo-label strategy to the JPDA algorithm to avoid cumulative errors due to inaccurate initial pseudo-labels. In order to verify our equipment and method, we use the newly designed sensor node to collect seven daily activities of 7 subjects. Nine different HAR models are trained by traditional machine learning and transfer learning methods. The experimental results show that the multi-modal data improve the accuracy of the HAR system. The IPL-JPDA algorithm proposed in this paper has the best performance among five HAR models, and the average recognition accuracy of different subjects is 93.2%.

scholarly journals Early prediction of hospital admission for emergency department patients: a comparison between patients younger or older than 70 years

2017 ◽  
Vol 35 (1) ◽  
pp. 18-27 ◽  
Author(s):  
Jacinta A Lucke ◽  
Jelle de Gelder ◽  
Fleur Clarijs ◽  
Christian Heringhaus ◽  
Anton J M de Craen ◽  
...  
Keyword(s):  
Heart Rate ◽  
Hospital Admission ◽  
Older Patients ◽  
Prediction Models ◽  
Age Groups ◽  
Vital Signs ◽  
Area Under The Curve ◽  
University Hospital ◽  
Multivariable Logistic Regression Analysis ◽  
Younger Patients

ObjectiveThe aim of this study was to develop models that predict hospital admission to ED of patients younger and older than 70 and compare their performance.MethodsPrediction models were derived in a retrospective observational study of all patients≥18 years old visiting the ED of a university hospital during the first 6 months of 2012. Patients were stratified into two age groups (<70 years old and ≥70 years old). Multivariable logistic regression analysis was used to identify predictors of hospital admission among factors available immediately after patient arrival to the ED. Validation of the prediction models was performed on patients presenting to the ED during the second half of the year 2012.Results10 807 patients were included in the derivation and 10 480 in the validation cohorts. The strongest independent predictors of hospital admission among the 8728 patients <70 years old were age, sex, triage category, mode of arrival, performance of blood tests, chief complaint, ED revisit, type of specialist, phlebotomised blood sample and all vital signs. The area under the curve (AUC) of the validation cohort for those <70 years old was 0.86 (95% CI 0.85 to 0.87). Among the 2079 patients ≥70 years, the same factors were predictive, except for gender, type of specialist and heart rate; the AUC was 0.77 (95% CI 0.75 to 0.79). The prediction models could identify a group of 10% of patients with the highest risk in whom hospital admission was predicted at ED triage, with a positive predictive value (PPV) of 71% (95% CI 68% to 74%) in younger patients and PPV of 87% (95% CI 81% to 92%) in older patients.ConclusionDemographic and clinical factors readily available early in the ED visit can be useful in identifying patients who are likely to be admitted to the hospital. While the model for the younger patients had a higher AUC, the model for older patients had a higher PPV in identifying the patients at highest risk for admission. Of note, heart rate was not a useful predictor in the older patients.

scholarly journals RESPIRATORY AND SYMPTOMATIC IMPACT OF ASCITES RELIEF BY PARACENTESIS IN PATIENTS WITH HEPATIC CIRRHOSIS

2020 ◽  
Vol 57 (1) ◽  
pp. 64-68
Author(s):  
Verônica Lourenço WITTMER ◽  
Rozy Tozetti LIMA ◽  
Michele Coutinho MAIA ◽  
Halina DUARTE ◽  
Flávia Marini PARO
Keyword(s):  
Liver Cirrhosis ◽  
Tidal Volume ◽  
Hepatic Cirrhosis ◽  
Blood Pressure Reduction ◽  
Vital Signs ◽  
Meld Score ◽  
Minute Volume ◽  
Respiratory Pattern ◽  
Peripheral Oxygen Saturation ◽  
The Impact

ABSTRACT BACKGROUND: Liver cirrhosis is a highly prevalent disease that, at an advanced stage, usually causes ascites and associated respiratory changes. However, there are few studies evaluating and quantifying the impact of ascites and its relief through paracentesis on lung function and symptoms such as fatigue and dyspnea in cirrhotic patients. OBJECTIVE: To assess and quantify the impact of acute reduction of ascitic volume on respiratory parameters, fatigue and dyspnea symptoms in patients with hepatic cirrhosis, as well as to investigate possible correlations between these parameters. METHODS: Thirty patients with hepatic cirrhosis and ascites who underwent the following pre and post paracentesis evaluations: vital signs, respiratory pattern, thoracoabdominal mobility (cirtometry), pulmonary function (ventilometry), degree of dyspnea (numerical scale) and fatigue level (visual analog scale). RESULTS: There was a higher prevalence of patients classified as CHILD B and the mean MELD score was 14.73±5.75. The comparison of pre and post paracentesis parameters evidenced after paracentesis: increase of predominantly abdominal breathing pattern, improvement of ventilatory variables, increase of the differences obtained in axillary and abdominal cirtometry, reduction of dyspnea and fatigue level, blood pressure reduction and increased peripheral oxygen saturation. Positive correlations found: xiphoid with axillary cirtometry, degree of dyspnea with fatigue level, tidal volume with minute volume, Child “C” with higher MELD score, volume drained in paracentesis with higher MELD score and with Child “C”. We also observed a negative correlation between tidal volume and respiratory rate. CONCLUSION: Since ascites drainage in patients with liver cirrhosis improves pulmonary volumes and thoracic expansion as well as reduces symptoms such as fatigue and dyspnea, we can conclude that ascites have a negative respiratory and symptomatological impact in these patients.

scholarly journals IOT Based Health Monitoring System

2021 ◽  
Vol 9 (8) ◽  
pp. 1176-1178
Author(s):  
Sowmya G
Keyword(s):  
Monitoring System ◽  
Health Monitoring ◽  
Social Insurance ◽  
Vital Signs ◽  
Smart Phone ◽  
Smart Devices ◽  
The Internet ◽  
Health State ◽  
Health Monitoring System ◽  
Vital Parameters

Abstract: The increased use of smart phones and smart devices in the health zone has brought on extraordinary effect on the world’s critical care. The Internet of things is progressively permitting to coordinate sensors fit for associating with the Internet and give data on the health condition of patients. These technologies create an amazing change in medicinal services during pandemics. Likewise, many users are beneficiaries of the M-Health (Mobile Health) applications and E-Health (social insurance upheld by ICT) to enhance, help and assist continuously to specialists who help. The main aim of this ‘IOT Health Monitoring System’ is to build up a system fit for observing vital body signs such as body temperature, heart rate, pulse oximetry etc. The System is additionally equipped measuring Room Temperature and Humidity and Atmosphere CO level. To accomplish this, the system involves many sensors to display vital signs that can be interfaced to the doctor’s smart phone as well as caretakers’ smartphone. This prototype will upload the readings from the sensor to a server remotely and the information gathered will be accessible for analysis progressively. It has the capacity of reading and transmitting vital parameters measured to the cloud server and then to any Smartphone configured with Blynk App. These readings can be utilized to recognize the health state of the patient and necessary actions can be taken if the vital parameters are not in prescribed limits for a longer period. Keywords: IOT Health Monitoring System, Vital parameters, Blynk App

scholarly journals SOLUÇÃO DE BAIXO CUSTO PARA O MONITORAMENTO DE SINAIS VITAIS, EM TEMPO REAL, FAZENDO USO DE SENSORES E ARDUINO

2020 ◽  
Vol 12 (2) ◽  
pp. 102-118
Author(s):  
Alexandre dos Santos Gonsalves ◽  
Robson Augusto Siscoutto
Keyword(s):  
Real Time ◽  
Health Monitoring ◽  
Low Cost ◽  
Vital Signs ◽  
Chronic Illnesses ◽  
Wireless Sensor ◽  
Health Monitoring System ◽  
Physical Exercises ◽  
Time Observation ◽  
Real Time Observation

The health monitoring system has become indispensable in the treatment of patients, especially for those who have chronic illnesses and need real-time observation from doctors and specialists. This article presents a low-cost wireless solution for monitoring, in real time, vital signs such as cardiac beats, breathing and blood pressure, collecting and sending data to a remote computer. During development, a wireless sensor box was created, using Arduino Nano and bluetooh sensors, where this box is attached to the patient's body, respecting the patient's flexibility and mobility during physical exercises. During the monitoring, the captured data is transmitted via the bluetooh network. The box uses a battery for its food. After the evaluation, the solution obtained a performance and correctness of the data close to 100%, being considered fit for use. Several experiments were carried out to analyze, quantify and qualify the solution, being discussed and presented in this paper.

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