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Ubiquitous health-care monitoring systems can provide continuous surveillance to a person using various sensors, including wearables and implantable and fabric-woven sensors. By assessing the state of many physiological characteristics of the patient’s body, continuous monitoring can assist in preparing for the impending emergency. To address this issue, this study aims to propose a health-care system that integrates the treatment of the impending heart, stress and alcohol emergencies. For this purpose, this study uses readings from sensors used for electrocardiography, heart rate, respiration rate, blood alcohol content percentage and blood pressure of a patient’s body.

For heart status, stress level and alcohol detection, the parametric values obtained from these sensors are preprocessed and further divided into four, five and six phases, respectively. A final integrated emergency stage is derived from the stages that were interpreted to examine at a person’s state of emergency. A thorough analysis of the proposed model is carried out using four classification techniques, including decision trees, support vector machines, k nearest neighbors and ensemble classifiers. For all of the aforementioned detections, four metrics are used to evaluate performance: classification accuracy, precision, recall and fmeasure.

Eventually, results are validated against the existing health-care systems. The empirical results received reveal that the proposed model outperforms the existing health-care models in the context of metrics above for different detections taken into consideration.

This study proposes a health-care system capable of performing data processing using wearable sensors. It is of great importance for real-time systems. This study assures the originality of the proposed system.

A vast body of literature has documented the negative consequences of stress on employee performance and well-being. These deleterious effects are particularly pronounced for service agents who need to constantly endure and manage customer emotions. The purpose of this paper is to introduce and describe a deep learning model to predict in real-time service agent stress from emotion patterns in voice-to-voice service interactions.

A deep learning model was developed to identify emotion patterns in call center interactions based on 363 recorded service interactions, subdivided in 27,889 manually expert-labeled three-second audio snippets. In a second step, the deep learning model was deployed in a call center for a period of one month to be further trained by the data collected from 40 service agents in another 4,672 service interactions.

The deep learning emotion classifier reached a balanced accuracy of 68% in predicting discrete emotions in service interactions. Integrating this model in a binary classification model, it was able to predict service agent stress with a balanced accuracy of 80%.

Service managers can benefit from employing the deep learning model to continuously and unobtrusively monitor the stress level of their service agents with numerous practical applications, including real-time early warning systems for service agents, customized training and automatically linking stress to customer-related outcomes.

The present study is the first to document an artificial intelligence (AI)-based model that is able to identify emotions in natural (i.e. nonstaged) interactions. It is further a pioneer in developing a smart emotion-based stress measure for service agents. Finally, the study contributes to the literature on the role of emotions in service interactions and employee stress.

Glass material is largely used for load-bearing components in buildings. For this reason, standardized calculation methods can be used in support of safe structural design in common loading and boundary conditions. Differing from earlier literature efforts, the present study elaborates on the load-bearing capacity, failure time and fire endurance of ordinary glass elements under fire exposure and sustained mechanical loads, with evidence of major trends in terms of loading condition and cross-sectional layout. Traditional verification approaches for glass in cold conditions (i.e. stress peak check) and fire endurance of load-bearing members (i.e. deflection and deflection rate limits) are assessed based on parametric numerical simulations.

The mechanical performance of structural glass elements in fire still represents an open challenge for design and vulnerability assessment. Often, special fire-resisting glass solutions are used for limited practical applications only, and ordinary soda-lime silica glass prevails in design applications for load-bearing members. Moreover, conventional recommendations and testing protocols in use for load-bearing members composed of traditional constructional materials are not already addressed for glass members. This paper elaborates on the fire endurance and failure detection methods for structural glass beams that are subjected to standard ISO time–temperature for fire exposure and in-plane bending mechanical loads. Fire endurance assessment methods are discussed with the support of Finite Element (FE) numerical analyses.

Based on extended parametric FE analyses, multiple loading, geometrical and thermo-mechanical configurations are taken into account for the analysis of simple glass elements under in-plane bending setup and fire exposure. The comparative results show that – in most of cases – thermal effects due to fire exposure have major effects on the actual load-bearing capacity of these members. Moreover, the conventional stress peak verification approach needs specific elaborations, compared to traditional calculations carried out in cold conditions.

The presented numerical results confirm that the fire endurance analysis of ordinary structural glass elements is a rather complex issue, due to combination of multiple aspects and influencing parameters. Besides, FE simulations can provide useful support for a local and global analysis of major degradation and damage phenomena, and thus support the definition of simple and realistic verification procedures for fire exposed glass members.

Laser cladding deposition is limited in industrial application by the micro-defects and residual tensile stress for the thermal forming process, leading to lower fatigue strength compared with that of the forging. The purpose of this paper is to develop an approach to reduce stress and defects.

A hybrid process of laser cladding deposition and shot peening is presented to transform surface strengthening technology to the overall strengthening technology through layer-by-layer forming and achieve enhancement.

The results show that the surface stress of the sample formed by the hybrid process changed from tensile stress to compressive stress, and the surface compressive stress introduced could reach more than four times the surface tensile stress of the laser cladding sample. At the same time, internal micro-defects such as pores were reduced. The porosity of the sample formed by the hybrid process was reduced by 90.12% than that of the laser cladding sample, and the surface roughness was reduced by 43.16%.

The authors believe that the hybrid process proposed in this paper can significantly expand the potential application of laser cladding deposition by solving its limitations, promoting its efficiency and applicability in practical cases.

In this project, we propose and test a new device – wearable sociometric badges containing small microphones – as a low-cost and relatively unobtrusive tool for measuring stress response to group processes. Specifically, we investigate whether voice pitch, measured using the microphone of the sociometric badge, is associated with physiological stress response to group processes.

We collect data in a laboratory setting using participants engaged in two types of small-group interactions: a social interaction and a problem-solving task. We examine the association between voice pitch (measured by fundamental frequency of the participant’s speech) and physiological stress response (measured using salivary cortisol) in these two types of small-group interactions.

We find that in the social task, participants who exhibit a stress response have a statistically significant greater deviation in voice pitch (from their overall average voice pitch) than those who do not exhibit a stress response. In the problem-solving task, participants who exhibit a stress response also have a greater deviation in voice pitch than those who do not exhibit a stress response, however, in this case, the results are only marginally significant. In both tasks, among participants who exhibited a stress response, we find a statistically significant correlation between physiological stress response and deviation in voice pitch.

We conclude that wearable microphones have the potential to serve as cheap and unobtrusive tools for measuring stress response to group processes.

Concentration is the key to safer driving. Ideally, drivers should focus mainly on front views and side mirrors. Typical distractions are eating, drinking, cell phone use, using and searching things in car as well as looking at something outside the car. In this paper, distracted driving detection algorithm is targeting on nine scenarios nodding, head shaking, moving the head 45° to upper left and back to position, moving the head 45° to lower left and back to position, moving the head 45° to upper right and back to position, moving the head 45° to lower right and back to position, moving the head upward and back to position, head dropping down and blinking as fundamental elements for distracted events. The purpose of this paper is preliminary study these scenarios for the ideal distraction detection, the exact type of distraction.

The system consists of distraction detection module that processes video stream and compute motion coefficient to reinforce identification of distraction conditions of drivers. Motion coefficient of the video frames is computed which follows by the spike detection via statistical filtering.

The accuracy of head motion analyzer is given as 98.6 percent. With such satisfactory result, it is concluded that the distraction detection using light computation power algorithm is an appropriate direction and further work could be devoted on more scenarios as well as background light intensity and resolution of video frames.

The system aimed at detecting the distraction of the public transport driver. By providing instant response and timely warning, it can lower the road traffic accidents and casualties due to poor physical conditions. A low latency and lightweight head motion detector has been developed for online driver awareness monitoring.

A tower crane mainly ensures the success or efficiency of building construction. Fatigue crack analysis is important for tower crane components to prevent any accidents to workers in construction sites caused by component failure and to ease the maintenance or replacement of failed components. This work aimed to characterise the damage of failed components, analyse the relationship between the metal magnetic memory (MMM) result and the damage of failed components, and to validate the relationship between MMM and finite element analysis (FEA).

MMM was used in this work to detect any irregularities or early failure on the basis of the high stress concentration zone of ferromagnetic steel using magnetic flux leakage. Magnetic flux leakage was used on the MMM device to achieve the first objective using the MMM system by detecting the irregularities. The results of MMM analysis were validated through comparison with FEA results by determining their relationship.

MMM results show that the position of defects on the tower crane pulley is within the stress area shown on FEA.

Hence, MMM method is a potential tool in monitoring failure mechanism in construction site.

An electroencephalography (EEG) examination may cause psychological stress in children with autism that can interfere with the examination results. The objective information on the presence or absence of psycho-emotional stress in patients can help interpret electroencephalograms. This paper aimed to demonstrate the potential of noninvasive objective diagnostics of emotional stress in autistic children undergoing an EEG examination based on analysis of saliva.

This study involved 19 children with autism spectrum disorder (ASD) (ICD-10 F84.0); the mean age was seven years. During EEG examination of the children, behavioral parameters were assessed. The activity of cytochrome P450 reductase (CYPOR) in saliva was measured before and after the EEG procedure using lucigenin-enhanced nicotinamide adenine dinucleotide phosphate-stimulated chemiluminescence assay.

Significant differences in CYPOR activity were found between the children who were distressed during an EEG examination and the children without behavioral disturbances (Mann–Whitney test, p = 0.002). Thus, the EEG examination resulted in an increase in CYPOR activity in saliva cells, which may prove the stressful effect of this procedure on autistic children.

The chemiluminescent indices reflecting the activity of microsomal CYPOR in cells presenting in saliva correlate with the absence or presence of psychological stress in children; this phenomenon can be explained by an increased metabolism of the stress hormone, cortisol, by the cytochrome P450 microsomal system. Furthermore, the proposed method is completely safe, noninvasive, rapid (recording time is 20 min), inexpensive and promising for an objective assessment of psycho-emotional stress in autistic children undergoing medical examinations.

Background: Every so often, one experiences different physically unstable situations which may lead to possibilities of suffering through vicious physiological risks and extents. Dynamic physiological activities are such a key metric that they are perceived by means of measuring galvanic skin response (GSR). GSR represents impedance of human skin that frequently changes based on different human respiratory and physical instability. Existing solutions, paved in literature and market, focus on the direct measurement of GSR by two sensor-attached leads, which are then parameterized against the standard printed circuit board mechanism. This process is sometimes cumbersome to use, resulting in lower user experience provisioning and adaptability in livelihood activities. The purpose of this study is to validate the novel development of the cost-effective GSR sensing system for affective usage for smart e-healthcare.

This paper proposes to design and develop a flexible circuit strip, populated with essential circuitry assemblies, to assess and monitor the level of GSR. Ordinarily, this flexible system would be worn on the back palm of the hand where two leads would contact two sensor strips worn on the first finger.

The system was developed on top of Pyralux. Initial goals of this work are to design and validate a flexible film-based GSR system to detect an individual’s level of human physiological activities by acquiring, amplifying and processing GSR data. The measured GSR value is visualized “24 × 7” on a Bluetooth-enabled smartphone via a pre-incorporated application. Conclusion: The proposed sensor-system is capable of raising the qualities such as adaptability, user experience, portability and ubiquity for possible application of monitoring of human psychodynamics in a more cost-effective way, i.e. less than US$50.

Several novel attributes are envisaged in the development process of the GSR system that made it different from and unique as compared to the existing alternatives. The attributes are as follows: (i) use of reproductive sensor-system fabrication process, (ii) use of flexible-substrate for hosting the system as proof of concept, (iii) use of miniaturized microcontroller, i.e. ATTiny85, (iv) deployment of energy-efficient passive electrical circuitry for noise filtering, (v) possible use case scenario of using CR2032 coin battery for provisioning powering up the system, (vi) provision of incorporation of internet of things (IoT)-cloud integration in existing version while fixing related APIs and (vii) incorporation of heterogeneous software-based solutions to validate and monitor the GSR output such as MakerPlot, Arduino IDE, Fritzing and MIT App Inventor 2.

This paper is a revised version R1 of the earlier reviewed paper. The proposed paper provides novel knowledge about the flexible sensor system development for GSR monitoring under IoT-based environment for smart e-healthcare.

The purpose of this paper is to provide an overview of smart agriculture systems and monitor and identify the technologies which can be used for deriving traditional agriculture system to modern agriculture system. It also provides the reader a broad area to work for the advancement in the field of agriculture and also explains the use of advanced technologies such as spectral imaging, robotics and artificial intelligence (AI) in the field of agriculture.

Smart uses of modern technologies were reviewed in the field of agriculture, which helps to monitor stress levels of plants and perform operations according to requirements. Operations can be irrigation, pests spray, monitoring crops, monitoring yield production, etc. Based on the literature review, a smart agriculture system is suggested. The parameters studied were spectral image processing, AI, unmanned aerial vehicle (UAVs) (fixed and rotatory), water or soil moisture, nutrients and pesticides.

The use of autonomous vehicles and AI techniques has been suggested through which the agriculture system becomes much more efficient. The world will switch to the smart agriculture system in the upcoming era completely. The authors conclude that autonomous vehicle in the field of science is time-saving and health efficient for both plants and workers in the fields. The suggested system increases the productivity of crops and saves the assets as well.

This review paper discusses the various contemporary technologies used in the field of agriculture and it will help future researchers to build on this research. This paper reveals that the UAVs along with multispectral, hyperspectral or red, green and blue camera (depends on the need) and AI are more suitable for the advancement of agriculture and increasing yield rate.