Comparison of heart rate monitoring with indirect calorimetry for energy expenditure evaluation

Purpose:The purpose of this study was to compare established methods with newly-developed methods for estimating the total energy expenditure(TEE). Methods:The study subjects comprised 46 individuals,including 16 middle-aged men(mean age 51.4 years),14 middle-aged women(mean age 49.9 years) and 16 young women(mean age 19.1 years).The TEE was estimated from 24-h heart rate(HR) data using newly-developed software (MoveSense HRAnalyzer 201 la,RC1.Suunto Oy,Vantaa,Finland),and was compared against the TEE determined using doubly labeled water (DLW).Agreement between the two methods was analyzed using Bland and Altman plots. Results:The HR method yielded similar TEE values as the DLW method at the group level,with an average of 8.6 kcal/day in the difference in the mean,but with large individual variations.Forty-four(96%) out of 46 subjects fell within±2SD of the mean difference in TEE comparisons, and there was no tendency towards under- or over-estimation. Conclusion:Our results indicate that the current software using HR analysis for the estimation of daily TEE needs further development for use with free-living individuals.

Ambulatory pulmonary artery pressure monitoring in advanced heart failure patients

Heart failure (HF) is an emerging epidemic associate with significant morbidity, mortality, and health care expenditure. Although there were major advances in pharmacologic and device based therapies for the management of HF, mortality of this condition remains high. Accurate monitoring of HF patients for exacerbations is very important to reduce recurrent hospitalizations and its associated complications. With the failure of clinical signs, tele-monitoring, and laboratory bio-markers to function as early markers of HF exacerbations, more sophisticated techniques were sought to accurately predict the circulatory status in HF patients in order to execute timely pharmacological intervention to reduce frequent hospitalizations. CardioMEMSTM (St. Jude Medical, Inc., Saint Paul, Minnesota) is an implantable, wireless pulmonary arterial pressure (PAP) monitoring system which transmits the patient’s continuous PAPs to the treating health care provider in the ambulatory setting. PAP-guided medical therapy modification has been shown to significantly reduce HF-related hospitalization and overall mortality. In advanced stages of HF, wireless access to hemodynamic information correlated with earlier left ventricular assist device implantation and shorter time to heart transplantation.

An Intelligent Sensor Based System for Real Time Heart Rate Monitoring (HRM)

A method and apparatus for monitoring heart rate of the heart using a wearable system is designed and implemented in this paper. A heart rate receives from heart beat signals and stores the data to a database and after a time period this method can determine an idle heart rate of the monitoring body. This idle heart rate is compared with the stored data and can determine the normal and abnormal heart rate variability. After the certain time period this system can detect the heart rate and also can send a signal to the user in time of abnormalities. Consequent estimations of heart rate variability are contrasted with this.

Monitoring Heart Rate Variability Based on Self-powered ECG Sensor Tag

This paper proposes a batteryless sensing and computational device to collect and process electrocardiography(ECG)signals for monitoring heart rate variability(HRV).The proposed system comprises of a passive UHF radio frequency identification(RFID)tag,an extreme low power microcontroller,a low-power ECG circuit,and a radio frequency(RF)energy harvester.The microcontroller and ECG circuits consume less power of only~30μA and~3 mA,respectively.Therefore,the proposed RF harvester operating at frequency band of 902 MHz~928 MHz can sufficiently collect available energy from the RFID reader to supply power to the system within a maximum distance of~2 m.To extract R-peak of the ECG signal,a robust algorithm that consumes less time processing is also developed.The information of R-peaks is stored into an Electronic Product Code(EPC)Class 1st Generation 1st compliant ID of the tag and read by the reader.This reader is functioned to collected the R-peak data with sampling rate of 100ms;therefore,the user application can monitor fully range of HRV.The performance of the proposed system shows that this study can provide a good solution in paving the way to new classes of healthcare applications.

Patient Centered Real-Time Mobile Health Monitoring System

In this paper, we introduce a system architecture for a patient centered mobile health monitoring (PCMHM) system that deploys different sensors to determine patients’ activities, medical conditions, and the cause of an emergency event. This system combines and analyzes sensor data to produce the patients’ detailed health information in real-time. A central computational node with data analyzing capability is used for sensor data integration and analysis. In addition to medical sensors, surrounding environmental sensors are also utilized to enhance the interpretation of the data and to improve medical diagnosis. The PCMHM system has the ability to provide on-demand health information of patients via the Internet, track real-time daily activities and patients’ health condition. This system also includes the capability for assessing patients’ posture and fall detection.

STUDY AND APPLICATION ABOUT COMPUTED SYSTEM FOR EXTERNAL CARDIAC MASSAGE, MONITOR OF HEART AND BODY TEMPERATURE

Objective:To make and study computed system for external cardiac massage,monitor of heart and body temperature and observe its clinical effect.Method:The system was made and applied.Result:The effect of system was obvious.Conclusion: The system was an effective clinical equipment in treatment of patient with cardiac arrest.

An 8.12 μW wavelet denoising chip for PPG detection and portable heart rate monitoring in 0.18 μm CMOS

A low power wavelet denoising chip for photoplethysmography （PPG） detection and portable heart rate monitoring is presented. To eliminate noise and improve detection accuracy, Harr wavelet （HWT） is chosen as the processing tool. An optimized finite impulse response structure is proposed to lower the computational complexity of proposed algorithm, which is benefit for reducing the power consumption of proposed chip. The modulus max- ima pair location module is design to accurately locate the PPG peaks. A clock control unit is designed to further reduce the power consumption of the proposed chip. Fabricated with the 0.18μm N-well CMOS 1P6M technol- ogy, the power consumption of proposed chip is only 8.12μW in 1 V voltage supply. Validated with PPG signals in multiparameter intelligent monitoring in intensive care databases and signals acquired by the wrist photoelectric volume detection front end, the proposed chip can accurately detect PPG signals. The average sensitivity and positive prediction are 99.91% and 100%, respectively.

Heart Rate Monitoring Sensor Based on Singlemode-Multimode-Singlemode Fiber

The singlemode-multimode-singlemode(SMS)fiber structure for a heart rate monitoring is proposed and developed.An artificial electrocardiogram(ECG)signal is used to simulate the heart pulse at different rates ranging from 50 beats per minute(bpm)to 200 bpm.The SMS fiber structure is placed at the center of a loudspeaker and it senses the vibration of the pulse.The vibration of the pulse signal applied to the SMS fiber structure changes the intensity of the optical output power.The proposed sensor shows a linear frequency of the heart rate sensing range that matches well with the relevant heart rate from the artificial ECG.This work shows the capability of the SMS fiber structure monitoring the heart rate frequencies for a long term,high stability realization,and reproducibility,and being suitable for the observation in hospitals as well as in other environments.

A Model for Predicting Construction Worker Fatigue

Fatigue impairs workers’judgment,reduces their productivity,and jeop­ardizes their safety.The paper presents a tool to predict workers’fatigue based on their vital signs.An experimental study was conducted in which the heart rate and sleep quality for three individuals were monitored using fitness trackers(wearable sensors).The data collected were used to develop two models based on regression analysis and Artificial Neural Networks(ANN),to predict their fatigue level.A Borg’s scale was used to estimate the Rating of Perceived Exertion(RPE)of the participants.The two models were able to satisfactorily predict the RPE(workers fatigue level)with an average validity of 75%and 80%for the regression ANN models,respec­tively.The developed models can provide project managers and superinten­dents with early warning to avoid potential worker overexertion,injuries,and fatalities.

Flexible Sensor Might One Day Monitor a Heart for Decades

During surgery,a heartbeat doesn’t just tell whether a person is dead or alive-it can warn of big problems that come up quite suddenly.Keeping watch for subtle irregularities in the heart’s electric activity can help save a patient’s life.But today’s technology can’t give as much detail as doctors