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.

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.

Proactive Spectrum Monitoring for Suspicious Wireless Powered Communications in Dynamic Spectrum Sharing Networks

This paper studies the proactive spec-trum monitoring with one half-duplex spectrum moni-tor(SM)to cope with the potential suspicious wireless powered communications(SWPC)in dynamic spec-trum sharing networks.The jamming-assisted spec-trum monitoring scheme via spectrum monitoring data(SMD)transmission is proposed to maximize the sum ergodic monitoring rate at SM.In SWPC,the suspi-cious communications of each data block occupy mul-tiple independent blocks,with a block dedicated to the wireless energy transfer by the energy-constrained suspicious nodes with locations in a same cluster(symmetric scene)or randomly distributed(asymmet-ric scene)and the remaining blocks used for the in-formation transmission from suspicious transmitters(STs)to suspicious destination(SD).For the sym-metric scene,with a given number of blocks for SMD transmission,namely the jamming operation,we first reveal that SM should transmit SMD signal(jam the SD)with tolerable maximum power in the given blocks.The perceived suspicious signal power at SM could be maximized,and thus so does the correspond-ing sum ergodic monitoring rate.Then,we further reveal one fundamental trade-off in deciding the op-timal number of given blocks for SMD transmission.For the asymmetric scene,a low-complexity greedy block selection scheme is proposed to guarantee the optimal performance.Simulation results show that the jamming-assisted spectrum monitoring schemes via SMD transmission achieve much better perfor-mance than conventional passive spectrum monitor-ing,since the proposed schemes can obtain more accu-rate and effective spectrum characteristic parameters,which provide basic support for fine-grained spectrum management and a solution for spectrum security in dynamic spectrum sharing network.

Automatic prediction of time to failure of open pit mine slopes based on radar monitoring and inverse velocity method

Radar slope monitoring is now widely used across the world, for example, the slope stability radar(SSR)and the movement and surveying radar(MSR) are currently in use in many mines around the world.However, to fully realize the effectiveness of this radar in notifying mine personnel of an impending slope failure, a method that can confidently predict the time of failure is necessary. The model developed in this study is based on the inverse velocity method pioneered by Fukuzono in 1985. The model named the slope failure prediction model(SFPM) was validated with the displacement data from two slope failures monitored with the MSR. The model was found to be very effective in predicting the time to failure while providing adequate evacuation time once the progressive displacement stage is reached.

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.

Development of All-Weather and Real-Time Bottom-Mounted Monitor of Bed Load Quantity

Quantity of bed load is an important physical parameter in sediment transport research. Aiming at the difficulties in the bed load measurement, this paper develops a bottom-mounted monitor to measure the bed load transport rate by adopting the sedimentation pit method and resolving such key problems as weighing and desilting, which can achieve long-time, all-weather and real-time telemeasurement of the bed load transport rate of plain rivers, estuaries and coasts. Both laboratory and field tests show that this monitor is reasonable in design, stable in properties and convenient in measurement, and it can be used to monitor the bed load transport rate in practical projects.

Characteristic of Ambulatory Blood Pressure in Normotensive Subjects With Type 2 Diabetes Mellitus

Objectives To observe the characteristic of ambulatory blood pressure monitoring in normotensive diabetic subjects with normoalbuminuria or microalbuminuria. Methods Fifty-two normotensive patients with type 2 diabetes received ambulatory blood pressure monitoring were divided into normoalbuminuric and microalbuminuric groups according to their albumin excretion rate, the other 28 normotensive subjects without diabetes were contributed as control group. Ambulatory blood pressure monitoring was performed on a working day and measurement of blood pressure circadian rhythm was analyzed. Results Normotensive microalbuminuric diabetic patients had higher night-time systolic blood pressure and more blood pressure burden than normotensive normoalbuminuric diabetic patients. Additionally, the microalbuminuric patients had a higher frequency of non-dippers than normoalbuminuric ones, although they were all normotensive. Compared to the normotensive non-diabetic control subjects, the night- time systolic blood pressure and frequency of non- dippers of the normoalbuminuric diabetic patients were significantly higher. Conclusions Intensive attention should be paid in control of blood pressure in diabetic patients to prevent and limit damage of target organ including kidney, even in those normotensive subjects.

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.

Review on occupancy detection and prediction in building simulation

Energy simulation results for buildings have significantly deviated from actual consumption because of the uncertainty and randomness of occupant behavior.Such differences are mainly caused by the inaccurate estimation of occupancy in buildings.Therefore,the error between reality and prediction could be largely reduced by improving the accuracy level of occupancy prediction.Although various studies on occupancy have been conducted,there are still many differences in the approaches to detection,prediction,and validation.Reports published within this domain are reviewed in this article to discover the advantages and limitations of previous studies,and gaps in the research are identified for future investigation.Six methods of monitoring and their combinations are analyzed to provide effective guidance in choosing and applying a method.The advantages of deterministic schedules,stochastic schedules,and machine-learning methods for occupancy prediction are summarized and discussed to improve prediction accuracy in future work.Moreover,three applications of occupancy models—improving building simulation software,facilitating building operation control,and managing building energy use—are examined.This review provides theoretical guidance for building design and makes contributions to building energy conservation and thermal comfort through the implementation of intelligent control strategies based on occupancy monitoring and prediction.

Real-time grain breakage sensing for rice combine harvesters using machine vision technology

Breakage rate is one of the most important indicators to evaluate the harvesting performance of a combine harvester.It is affected by operating parameters of a combine such as feeding rate,the peripheral speed of the threshing cylinder and concave clearance,and shows complex non-linear law.Real-time acquisition of the breakage rate is an effective way to find the correlation of them.In addition,real-time monitoring of the breakage rate can help the driver optimize and adjust the operating parameters of a combine harvester to avoid the breakage rate exceeding the standard.In this study,a real-time monitoring method for the grain breakage rate of the rice combine harvester based on machine vision was proposed.The structure of the sampling device was designed to obtain rice kernel images of high quality in the harvesting process.According to the working characteristics of the combine,the illumination and installation of the light source were optimized,and the lateral lighting system was constructed.A two-step method of“color training-verification”was applied to identify the whole and broken kernels.In the first step,the local threshold algorithm was used to get the edge of kernel particles in a few training images with binary transformation,extract the color spectrum of each particle in color-space HSL and output the recognition model file.The second step was to verify the recognition accuracy and the breakage rate monitoring accuracy through grabbing and processing images in the laboratory.The experiments of about 2300 particles showed that the recognition accuracy of 96%was attained,and the monitoring values of breakage rate and the true artificial monitoring values had good trend consistency.The monitoring device of grain breakage rate based on machine vision can provide technical supports for the intellectualization of combine harvester.