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The identification of fatigue status and early intervention to mitigate fatigue can reduce the risk of workplace injuries. Off-the-shelf wearable sensors capable of assessing multiple parameters are available. However, using numerous variables in the fatigue prediction model can elicit data issues. This study aimed at identifying the most relevant variables for measuring occupational fatigue among entry-level construction workers by using common wearable sensor technologies, such as electrocardiogram and actigraphy sensors.

Twenty-two individuals were assigned different task workloads in repeated sessions. Stepwise logistic regression was used to identify the most parsimonious fatigue prediction model. Heart rate variability measurements, standard deviation of NN intervals and power in the low-frequency range (LF) were considered for fatigue prediction. Fast Fourier transform and autoregressive (AR) analysis were employed as frequency domain analysis methods.

The log-transformed LF obtained using AR analysis is preferred for daily fatigue management, whereas the standard deviation of normal-to-normal NN is useful in weekly fatigue management.

This study was conducted with entry-level construction workers who are involved in manual material handling activities. The findings of this study are applicable to this group.

This is the first study to investigate all major measures obtainable through electrocardiogram and actigraphy among current mainstream wearables for monitoring occupational fatigue in the construction industry. It contributes knowledge on the use of wearable technology for managing occupational fatigue among entry-level construction workers engaged in material handling activities.

The aim was to test the feasibility of using automated data, and evaluate the impact of an emergency cardiac decision unit (CDU) on the overall outcomes of patients seen for chest discomfort. We used a retrospective, quasi‐experimental design to identify patients who had cardiac enzymes measured and an electrocardiogram performed during an ED visit in two six‐month periods, pre‐CDU (1 January‐30 June 1995) and post‐CDU (1 January‐ 30 June 1996). A total of 4,336 patients had outcomes assessed. After opening, 14.8 per cent of all chest pain cases were treated in the CDU. Hospital admission rates were reduced from 81.1 per cent to 66.7 per cent. Length of stay, myocardial infarction rates, and mortality were unchanged. The 14‐day revisit rates increased from 5.3 per cent to 10.3 per cent. We conclude that cardiac decision units decrease hospital admissions but increase ED revisit rates as a consequence of this now frequently used care pathway.

The purpose of this paper is to design a low-power human physiological parameters monitoring system which can monitor six vital parameters simultaneously based on wearable body sensor network.

This paper presents a low-power multiple physiological parameters monitoring system (MPMS) which comprises four subsystems. These are: electrocardiogram (ECG)/respiration (RESP) parameters monitoring subsystem with embedded algorithms; blood oxygen (SpO₂)/pulse rate (PR)/body temperature (BT)/blood pressure (BP) parameters monitoring subsystem with embedded algorithms; main control subsystem which is in charge of system-level power management, communication and interaction design; and upper computer software subsystem which manipulates system function and analyzes data.

Results have successfully demonstrated monitoring human ECG, RESP, PR, SpO₂, BP and BT simultaneously using the MPMS device. In addition, the power reduction technique developed in this work at the physical/hardware level is effective. Reliability of algorithms developed for monitoring these parameters is assessed by Fluke Prosim8 Vital Signs Simulators (produced by Fluke Corp. USA).

The MPMS device provides long-term health monitoring without interference from normal personal activities, which potentially allows applications in real-time daily healthcare monitoring, chronic diseases monitoring, elderly monitoring, human emotions recognization and so on.

First, a power reduction technique at the physical/hardware level is designed to realize low power consumption. Second, the proposed MPMS device enables simultaneously monitoring six key parameters. Third, unlike most monitoring systems in bulk size, the proposed system is much smaller (118 × 58 × 18.5 mm3, 140 g total weight). In addition, a comfortable smart shirt is fabricated to accommodate the portable device, offering reliable measurements.

This survey was initially conceived as an audit to highlight the National Institute for Clinical Excellence (UK), guidelines on depression in 2004. In particular, the audit was to examine the guidance on the prescription of Venlafaxine, which required an electrocardiogram (ECG), and blood pressure measurements.

A retrospective case note review by the authors, of 123 patients prescribed Venlafaxine within Leicestershire Partnership NHS Trust was completed.

Nearly 50 per cent of patients had a blood pressure measurement prior to starting Venlafaxine compared to only 6 per cent of patients receiving an ECG.

The audit cycle could not be completed following the publication of a further set of guidelines that removed the need for ECG recordings.

This paper highlights how difficult it is for clinicians to keep up‐to‐date, and implement rapidly changing guidelines.

This study has demonstrated how technology may contribute to integrated care solutions by comparing conventional ward telemetry (WT) to a wearable ECG monitor (S-Patch) to detect atrial fibrillation (AF) in patients with stroke.

51 patients admitted for stroke workup were recruited across two major tertiary centres to compare WT monitoring for two days versus S-Patch for four days in the detection of AF. The efficacy to detect AF using both technologies was assessed via data extractions and medical officer review. A matrix was used to measure nursing/patient satisfaction and setup/resource times were assessed.

Patients (84–94%) and nursing staff (75–95%) preferred the S-Patch wearable technology. Non-parametric tests indicated significant time saving for removal of S-Patch versus WT [2.2 min vs 5.1 min (p = 0.00)]. Efficacy of S-Patch to detect AF following medical officer review was greater than WT, with seven patients identified with AF by S-Patch versus one using WT. The S-patch had a false positive rate of 78%.

The S-Patch is sensitive in the detection of AF; however, it showed a high false-positive rate with automated reporting. This study has provided insight into the details of delivery of integrated healthcare using wearable technology.

The technology and partnership were the first-in-kind in Australia. The S-Patch had a higher detection rate of AF compared to WT which allows patients to be anti-coagulated appropriately for the prevention of further stroke. The results of this study will be ideally placed to inform future policy in integrated healthcare using new technologies.

This paper briefly describes the technical aspects of ECG interpretation by computer, and thereafter discusses in detail the considerations involved in introducing the method into a large hospital. One of the most important of these is that of educating physicians to accept 3‐lead ECG interpretations. Technical problems are discussed together with the question of staffing, and it is emphasized that the introduction of automation does not normally lead to any staff redundancy. The various shortcomings of existing methods are described but the benefits accruing from automation are discussed. The conclusion drawn is that the technique will prove to be of value in the near future when regional centres are established to cope with the ever increasing demand for ECG interpretations.

The purpose of this paper is to develop a health monitoring system that can measure human vital signs and recognize human activity based on body sensor network (BSN).

The system is mainly composed of electrocardiogram (ECG) signal collection node, blood oxygen signal collection node, inertial sensor node, receiving node and upper computer software. The three collection nodes collect ECG signals, blood oxygen signals and motion signals. And then collected signals are transmitted wirelessly to receiving node and analyzed by software in upper computer in real-time.

Experiment results show that the system can simultaneously monitor human ECG, heart rate, pulse rate, SpO₂ and recognize human activity. A classifier based on coupled hidden Markov model (CHMM) is adopted to recognize human activity. The average recognition accuracy of CHMM classifier is 94.8 percent, which is higher than some existent methods, such as supported vector machine (SVM), C4.5 decision tree and naive Bayes classifier (NBC).

The monitoring system may be used for falling detection, elderly care, postoperative care, rehabilitation training, sports training and other fields in the future.

First, the system can measure human vital signs (ECG, blood pressure, pulse rate, SpO₂, temperature, heart rate) and recognizes some specific simple or complex activities (sitting, lying, go boating, bicycle riding). Second, the researches of using CHMM for activity recognition based on BSN are extremely few. Consequently, the classifier based on CHMM is adopted to recognize activity with ideal recognition accuracies in this paper.

Recent developments in wearable technologies have paved the way for continuous monitoring of the electrocardiogram (ECG) signal, without the need for any laboratory settings. A number of wearable sensors ranging from wet electrode sensors to dry sensors, textile-based sensors, knitted integrated sensors (KIS) and planar fashionable circuit boards are used in ECG measurement. The purpose of this study is to carry out a comparative study of the different sensors used for ECG measurements. The current challenges faced in developing wearable ECG sensors are also reviewed.

This study carries out a comparative analysis of different wearable ECG sensors on the basis of four important aspects: materials and methods used to develop the sensors, working principle, implementation and performance. Each of the aspects has been reviewed with regard to the main types of wearable ECG sensors available.

A comparative study of the sensors helps understand the differences in their operating principles. While some sensors may have a higher efficiency, the others might ensure more user comfort. It is important to strike the right balance between the various aspects influencing the sensor performance.

Wearable ECG sensors have revolutionized the world of ambulatory ECG monitoring and helped in the treatment of many cardiovascular diseases. A comparative study of the available technologies will help both doctors and researchers gain an understanding of the shortcomings in the existing systems.

This study was performed to confirm that autonomic nervous activity is affected by breathing speed. I hypothesized that prolonged expiratory breathing would promote parasympathetic dominance, whereas rapid breathing would promote sympathetic dominance. Ten healthy men, ages 21-28 years old, were instructed to perform prolonged expiratory breathing (6 seconds expiration, 4 seconds inspiration) after spontaneous breathing and rapid breathing (1 second expiration, 1 second inspiration) after spontaneous breathing; changes in high frequency (HF) and low frequency (LF)/HF of heart rate variability (HRV) were measured during each type of breathing. During prolonged expiratory breathing, parasympathetic nervous function was significantly activated. Conversely, during rapid breathing, parasympathetic nervous function was significantly suppressed. The HRV method assessing sympathetic and parasympathetic modulation in this study is an indirect, noninvasive method with clear limitations. The use of additional techniques should be considered to clarify the relationships between the breathing speed and the mind.

This paper aims to concentrate on recent innovations in flexible wearable sensor technology tailored for monitoring vital signals within the contexts of wearable sensors and systems developed specifically for monitoring health and fitness metrics.

In recent decades, wearable sensors for monitoring vital signals in sports and health have advanced greatly. Vital signals include electrocardiogram, electroencephalogram, electromyography, inertial data, body motions, cardiac rate and bodily fluids like blood and sweating, making them a good choice for sensing devices.

This report reviewed reputable journal articles on wearable sensors for vital signal monitoring, focusing on multimode and integrated multi-dimensional capabilities like structure, accuracy and nature of the devices, which may offer a more versatile and comprehensive solution.

The paper provides essential information on the present obstacles and challenges in this domain and provide a glimpse into the future directions of wearable sensors for the detection of these crucial signals. Importantly, it is evident that the integration of modern fabricating techniques, stretchable electronic devices, the Internet of Things and the application of artificial intelligence algorithms has significantly improved the capacity to efficiently monitor and leverage these signals for human health monitoring, including disease prediction.