Assessment of human motivation through analysis of physiological and emotional signals in Industry 4.0 scenarios

Bordel, Borja

doi:10.1007/s12652-017-0664-4

Cited by 19 publications

(15 citation statements)

References 27 publications

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“…Advanced learning models for robots are, therefore crucial for people and machines to develop skills that complement each other under any working conditions (Zhong, Xu, Klotz, & Newman, 2017). From the perspective of robotization, the new companies do not focus only on performance and automatization but also on the search of the interconnection of the robotic and human work (Bordel, 2017), on the emotions and effective and discreet acts of the robot that is placed at the workplace next to a human being (Mattsson, 2016). Researchers should aim to create and optimize innovative solutions for different types of businesses in order to support their uptake (Modrak, Soltysova, & Poklemba, 2019).…”

Section: Introductionmentioning

confidence: 99%

Division of Enterprises and Their Strategies in Relation to Industry 4.0

Vrchota¹,

Vlčková²,

Frantíková³

2020

CEBR

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In today's turbulent times, businesses must cope with an increasing focus on digital technology, automation and robotics systems. It represents the fourth industrial revolution, called Industry 4.0. The goals of Industry 4.0 are achieving higher levels of operational efficiency and productivity, as well as a higher level of automation. Concerning Industry 4.0, therefore, not only the technological aspect but also the size of the organization, the scope of business by industry and other elements are considered and analyzed based on Mann-Whitney U test and Fisher test. According to statistical analysis, it was found that the size of the company affects its relationship to Industry 4.0, and we can say that the larger the business, the more it is affected by Industry 4.0. This influence was manifested primarily in companies operating in the field of commercial activities, or Agriculture and Construction. In terms of the relationship between the written strategy and Industry 4.0, it can be stated that the link between them has occurred in small and medium-sized enterprises operating in the areas of Professional and Administrative Activities, Manufacturing and Wholesale, Transport and Accommodation. In contrast, this effect has not been proven in micro-enterprises. Implications for a Central European audience: The paper primarily deals with the analysis of the relationship between enterprise size and Industry 4.0 and the study of the relationship between the division of enterprises by industry in relation to CZ-NACE. A significant difference was found between the enterprises affected and unaffected by Industry 4.0 when evaluating the size of the enterprise. Enterprises involved by Industry 4.0 employ more workers, which may be due to the introduction of automation and robotic automation, which means the initial phase. As the results further show, the written strategy is mostly the domain of medium-sized enterprises, with only 29 % of micro-enterprises and 41 % of small enterprises having it defined.

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Section: Introductionmentioning

confidence: 99%

Division of Enterprises and Their Strategies in Relation to Industry 4.0

Vrchota¹,

Vlčková²,

Frantíková³

2020

CEBR

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“…In particular, we are investigating solutions based on Bayesian trees [44] and other specific solutions for biometric signals [45]. On the other hand, algorithms based on signal processing technologies for signal reconstruction are also being evaluated [46]. All these algorithms may be easily integrated into the proposed schemes, by replacing the current reconstruction technique for any of these proposals.…”

Section: Discussionmentioning

confidence: 99%

A Predictor-Corrector Algorithm Based on Laurent Series for Biological Signals in the Internet of Medical Things

Bordel

You

2020

IEEE Access

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In future engineered systems for medical applications, a tight real-time integration between physical and computational processes will be required. That integration is achieved using feedback control loops which need high quality input data streams. However, hardware platforms can barely provide such high-quality data sequences (especially if mobile nodes are considered), and mechanisms to improve and polish physical and biological signals are then necessary. This paper proposes a predictor-corrector algorithm to improve the quality and precision of data (biological) signals in Internet of Medical Things deployments, especially if composed of mobile nodes. The proposed algorithm employs an Artificial Intelligence approach and statistical learning techniques to predict future data samples and correct errors in received information. Employed mathematical models follow a prediction-correction scheme and are based on complex functions, Laurent series and the idea of complex envelope. Simulation techniques are used to evaluate the performance of the proposed solution, showing that it improves the precision of traditional linear interpolation techniques up to 85%, and cubic splines up to 20%. Processing delay during operation is, for the referred precision, around 200ms.

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“…Other proposals based on brain signals have also been reported [19], and works discussing how to apply psychology to humanize computing [20] and software [21] are also common. Initial applications of physiological theories to Industry 4.0 have also been reported that consider human motivation and Maslow's proposals [22]. A very large group within this area is human task recognition.…”

Section: State Of the Artmentioning

confidence: 99%

“…Thus, in order to maintain a constant level of wellbeing, these needs must be satisfied in a continuous and increasing manner. Many works on human motivation, wellbeing, and behavior, such as Maslow's proposals [22], describe human needs as a stair or pyramid (see Figure 2). After a certain time at the same "level", people's wellbeing starts to diminish.…”

Section: Mathematical Formalizationmentioning

confidence: 99%

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People-as-a-Service Dilemma: Humanizing Computing Solutions in High-Efficiency Applications

Bordel¹,

Hernández²

2019

13th International Conference on Ubiquitous Computing and Ambient ‪Intelligence UCAmI 2019‬

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Next-generation computing solutions, such as cyber-physical systems or Industry 4.0, are focused on increasing efficiency in process execution as much as possible. Removing unproductive delays or keeping infrastructures operating at their total capacity are typical objectives in these future systems. Decoupling infrastructure providers and service providers using Anything-as-a-Service (XaaS) paradigms is one of the most common approaches to address this challenge. However, many real scenarios not only include machines or controllers but also people and workers. In this case, deploying process execution algorithms and XaaS solutions degenerates in a People-as-a-Service scenario, which poses a critical dilemma: Can highly efficient production scenarios guarantee people’s wellbeing? In this paper, we address this problem and propose a new process execution algorithm based on a novel understanding of efficiency. In this case, a humanized efficiency definition combining traditional efficiency ratios and wellbeing indicators is used to allocate tasks and assign them to different existing workers. In order to evaluate the proposed solution, a simulation scenario including social and physical elements was built. Using this scenario, a first experimental validation was carried out.

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Assessment of human motivation through analysis of physiological and emotional signals in Industry 4.0 scenarios

Cited by 19 publications

References 27 publications

Division of Enterprises and Their Strategies in Relation to Industry 4.0

Division of Enterprises and Their Strategies in Relation to Industry 4.0

A Predictor-Corrector Algorithm Based on Laurent Series for Biological Signals in the Internet of Medical Things

People-as-a-Service Dilemma: Humanizing Computing Solutions in High-Efficiency Applications

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