Work metabolism (WM) can be accurately estimated by oxygen consumption (VO2), which is commonly assessed by heart rate (HR) in field studies. However, the VO2–HR relationship is influenced by individual capacity and activity characteristics. The purpose of this study was to evaluate three models for estimating WM compared with indirect calorimetry, during simulated work activities. The techniques were: the HR-Flex model; HR branched model, combining HR with hip-worn accelerometers (ACC); and HR + arm-leg ACC model, combining HR with wrist- and thigh-worn ACC. Twelve participants performed five simulated work activities and three submaximal tests. The HR + arm-leg ACC model had the overall best performance with limits of agreement (LoA) of −3.94 and 2.00 mL/min/kg, while the HR-Flex model had −5.01 and 5.36 mL/min/kg and the branched model, −6.71 and 1.52 mL/min/kg. In conclusion, the HR + arm-leg ACC model should, when feasible, be preferred in wearable systems for WM estimation.

Preventive healthcare has attracted much attention recently. Improving people's lifestyles and promoting a healthy diet and wellbeing are important, but the importance of work-related diseases should not be undermined. Musculoskeletal disorders (MSDs) are among the most common work-related health problems. Ergonomists already assess MSD risk factors and suggest changes in workplaces. However, existing methods are mainly based on visual observations, which have a relatively low reliability and cover only part of the workday. These suggestions concern the overall workplace and the organization of work, but rarely includes individuals' work techniques. In this work, we propose a precise and pervasive ergonomic platform for continuous risk assessment. The system collects data from wearable sensors, which are synchronized and processed by a mobile computing layer, from which exposure statistics and risk assessments may be drawn, and finally, are stored at the server layer for further analyses at both individual and group levels. The platform also enables continuous feedback to the worker to support behavioral changes. The deployed cloud platform in Amazon Web Services instances showed sufficient system flexibility to affordably fulfill requirements of small to medium enterprises, while it is expandable for larger corporations. The system usability scale of 76.6 indicates an acceptable grade of usability.

The interconnection between hard electronics and soft textiles remains a noteworthy challenge in regard to the mass production of textile-electronic integrated products such as sensorized garments. The current solutions for this challenge usually have problems with size, flexibility, cost, or complexity of assembly. In this paper, we present a solution with a stretchable and conductive carbon nanotube (CNT)-based paste for screen printing on a textile substrate to produce interconnectors between electronic instrumentation and a sensorized garment. The prototype connectors were evaluated via electrocardiogram (ECG) recordings using a sensorized textile with integrated textile electrodes. The ECG recordings obtained using the connectors were evaluated for signal quality and heart rate detection performance in comparison to ECG recordings obtained with standard pre-gelled Ag/AgCl electrodes and direct cable connection to the ECG amplifier. The results suggest that the ECG recordings obtained with the CNT paste connector are of equivalent quality to those recorded using a silver paste connector or a direct cable and are suitable for the purpose of heart rate detection.

In this work, we want to introduce combined heart rate and respiration monitoring for more accurate energy expenditure tracking on free-living subjects. We have developed a wearable cardiorespiratory monitoring system with unobtrusive heart rate measurement and ventilation estimation function for this purpose. The system is based on a garment with integrated textile electrodes for one-lead electrocardiogram and impedance pneumography measurements. A pilot experiment has been performed to prove the concept and to evaluate the characteristics of heart rate and ventilation estimated by our system in relation to energy expenditure. In the experiment, ventilation shows a better linearity in relation to the energy expenditure at the low intensity region than heart rate. Based on these characteristics, a model combining heart rate and ventilation for energy expenditure estimation is proposed which shows a significantly lower estimation error than the heart rate only model.

IntroductionFetal heart rate short term variation (STV) decreases with severe chronic hypoxia in the antenatal period. However, only limited research has been done on STV during labor. We have tested a novel algorithm for a valid baseline estimation and calculated STV. To explore the value of STV during labor, we compared STV with fetal scalp blood (FBS) lactate concentration, an early marker in the hypoxic process. Material and methodsSoftware was developed which estimates baseline frequency using a novel algorithm and thereby calculates STV according to Dawes and Redman in up to four 30-minute blocks prior to each FBS. Cardiotocography traces from 1070 women in labor who had had FBS performed on 2134 occasions were analyzed. ResultsIn acidemic cases (lactate >4.8mmol/L; Lactate Pro), median STV 30minutes prior to FBS was 7.10milliseconds compared with 6.09milliseconds in the preacidemic (4.2-4.8mmol/L) and 5.23milliseconds in the normal (<4.2mmol/L) groups (P<.05). There was a positive correlation between lactate and STV (rho=0.16-0.24; P<.05). Median lactate concentration in cases with STV <3.0milliseconds (n=160) was 2.3mmol/L. When 2 FBS were performed within 60minutes the change rate of lactate correlated to STV (rho=0.33; P<.001). Cases with increasing lactate concentration had a median STV of 5.29milliseconds vs 4.41milliseconds in those with decreasing lactate (P<.001). ConclusionsIn the early stages of intrapartum hypoxia, STV increases, contrary to findings regarding chronic hypoxia in the antenatal period. The increase in the adrenergic surge is a likely explanation.

This paper presents a new method that integrates heart rate, respiration, and motion information obtained from a wearable sensor system to estimate energy expenditure. The system measures electrocardiography, impedance pneumography, and acceleration from upper and lower limbs. A multilayer perceptron neural network model was developed, evaluated, and compared to two existing methods, with data from 11 subjects (mean age, 27 years, range, 21–65 years) who performed a 3-h protocol including submaximal tests, simulated work tasks, and periods of rest. Oxygen uptake was measured with an indirect calorimeter as a reference, with a time resolution of 15 s. When compared to the reference, the new model showed a lower mean absolute error (MAE = 1.65 mL/kg/min, R2 = 0.92) than the two existing methods, i.e., the flex-HR method (MAE = 2.83 mL/kg/min, R2 = 0.75), which uses only heart rate, and arm-leg HR+M method (MAE = 2.12 mL/kg/min, R2 = 0.86), which uses heart rate and motion information. As indicated, this new model may, in combination with a wearable system, be useful in occupational and general health applications. 

Work-related musculoskeletal and cardiovascular disorders are still prevalent in today's working population. Nowadays, risk assessments are usually performed via self-reports or observations, which have relatively low reliability. Technology developments in textile electrodes (textrodes), inertial measurement units, and the communication and processing capabilities of smart phones/tablets provide wearable solutions that enable continuous measurements of physiological and musculoskeletal loads at work with sufficient reliability and resource efficiency. In this paper, a wearable system integrating textrodes, motion sensors, and real-time data processing through a mobile application was developed as a demonstrator of risk assessment related to different types and levels of workload and activities. The system was demonstrated in eight subjects from four occupations with various workload intensities, during which the heart rate and leg motion data were collected and analyzed with real-time risk assessment and feedback. The system showed good functionality and usability as a risk assessment tool. The results contribute to designing and developing future wearable systems and bring new solutions for the prevention of work-related disorders.

Introduction.Reliability in visual cardiotocography interpretation isunsatisfying, which has led to the development of computerizedcardiotocography. Computerized analysis is well established for antenatal fetalsurveillance but has yet not performed sufficiently during labor. We aimed toinvestigate the capacity of a new computerized algorithm compared with visualassessment in identifying intrapartum fetal heart rate baseline anddecelerations.Material and methods.In all, 312 intrapartum cardiotocographytracings with variable decelerations were analyzed by the computerizedalgorithm and visually examined by two observers, blinded to each other andthe computer analysis. The width, depth and area of each deceleration wasmeasured. Four cases (>100 variable decelerations) were subjected to in-depthdetailed analysis. The outcome measures were bias in seconds (width), beatsper minute (depth), and beats (area) between computer and observers usingBland–Altman analysis. Interobserver reliability was determined by calculatingintraclass correlation and Spearman rank analysis.Results.The analysis (312cases) showed excellent intraclass correlation (0.89–0.95) and very strongSpearman correlation (0.82–0.91). The detailed analysis of>100 decelerationsin four cases revealed low bias between the computer and the two observers;width 1.4 and 1.4 seconds, depth 5.1 and 0.7 beats per minute, and area 0.1and–1.7 beats. This was comparable to the bias between the two observers:0.3 seconds (width), 4.4 beats per minute (depth) and 1.7 beats (area). Theintraclass correlation was excellent (0.90–.98).Conclusion.A novelcomputerized algorithm for intrapartum cardiotocography analysis is asaccurate as gold standard visual assessment, with high correlation and low bias.