Human–Machine Integration in Processes within Industry 4.0 Management

Villalba-Díez, Javier; Ordieres-Meré, Joaquín

doi:10.3390/s21175928

Cited by 9 publications

(4 citation statements)

References 58 publications

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“…This allows humans to focus on activities that require uniquely human skills, while machines handle the task of information processing. This task distribution aims to optimize overall performance and improve efficiency by leveraging the complementary abilities of humans and machines [49]. The adoption of Industry 5.0 technologies, therefore, will not undermine human value; on the contrary, it will enable the integration of human intelligence and machine intelligence in a collaborative environment [3].…”

Section: The Human-machine Relationship In the Industry: A Path To Be...mentioning

confidence: 99%

Neoindustrialization—Reflections on a New Paradigmatic Approach for the Industry: A Scoping Review on Industry 5.0

Pereira

Santos

2023

Logistics

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Background: The Industry 5.0 emerges as a new paradigm for the industry by considering sustainability, human-centered approaches, organizational resilience, and interaction between humans and machines as its core values. This new trend for the future of the industry is referred to as neoindustrialization. Due to being a topic in development, there is still no precise consensus on its definition, which prompted the current study to comprehensively investigate and analyze the existing literature on Industry 5.0. Methods: The method employed was a scoping review, examining publications from various databases and academic journals, including those specific to the Brazilian context. Results: The results indicate a transition towards an industry that meets societal demands and respects planetary boundaries, aspects that were overlooked by Industry 4.0. Conclusions: In this new scenario, the industry reassumes its leadership by combining technology with new strategies and organizational models. Furthermore, it undergoes organizational changes to align its structure, operations, human resources, and new practices, aiming to meet the demands of society and all stakeholders involved. To achieve this, it is necessary to create an environment conducive to innovation and entrepreneurship, promoting the development of qualified human capital, investments in research and development, and strengthening partnerships between the public and private sectors. A successful neoindustrialization policy will generate high-quality jobs and foster economic growth. Industry 5.0 is the paradigm that will prevail in the 21st century. It is not a matter of speculation; it is an inseparable and inevitable reality. Otherwise, the industry will be relegated to a secondary role in the process of digital and social transformation.

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Section: The Human-machine Relationship In the Industry: A Path To Be...mentioning

confidence: 99%

Neoindustrialization—Reflections on a New Paradigmatic Approach for the Industry: A Scoping Review on Industry 5.0

Pereira

Santos

2023

Logistics

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“…Industry 4.0, often referred to as the fourth industrial revolution, embodies a landscape where digital technologies intertwine with production processes, redefining the very nature of enterprises and human interaction with the surrounding environment [1]. Within this context, the integration of passive Brain-computer Interface (BCI) technologies may enable direct engagement of the user in the productive and/or industrial process [2,3]. Unlike standard and subjective methods (e.g., questionnaires), these technologies allow for a covert estimation (i.e., without asking anything to the user) of mental and emotional states of the user while engaged in the operational task, allowing for continuous feedback with the surrounding environment to put the user in the loop.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Detection of Covariate Shift Due to Changes in EEG Headset Position: Towards an Effective Out-of-Lab Use of Passive Brain–Computer Interface

Germano,

Sciaraffa,

Ronca

et al. 2023

Applied Sciences

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In the field of passive Brain–computer Interfaces (BCI), the need to develop systems that require rapid setup, suitable for use outside of laboratories is a fundamental challenge, especially now, that the market is flooded with novel EEG headsets with a good quality. However, the lack of control in operational conditions can compromise the performance of the machine learning model behind the BCI system. First, this study focuses on evaluating the performance loss of the BCI system, induced by a different positioning of the EEG headset (and of course sensors), so generating a variation in the control features used to calibrate the machine learning algorithm. This phenomenon is called covariate shift. Detecting covariate shift occurrences in advance allows for preventive measures, such as informing the user to adjust the position of the headset or applying specific corrections in new coming data. We used in this study an unsupervised Machine Learning model, the Isolation Forest, to detect covariate shift occurrence in new coming data. We tested the method on two different datasets, one in a controlled setting (9 participants), and the other in a more realistic setting (10 participants). In the controlled dataset, we simulated the movement of the EEG cap using different channel and reference configurations. For each test configuration, we selected a set of electrodes near the control electrodes. Regarding the realistic dataset, we aimed to simulate the use of the cap outside the laboratory, mimicking the removal and repositioning of the cap by a non-expert user. In both datasets, we recorded multiple test sessions for each configuration while executing a set of Workload tasks. The results obtained using the Isolation Forest model allowed the identification of covariate shift in the data, even with a 15-s recording sample. Moreover, the results showed a strong and significant negative correlation between the percentage of covariate shift detected by the method, and the accuracy of the passive BCI system (p-value < 0.01). This novel approach opens new perspectives for developing more robust and flexible BCI systems, with the potential to move these technologies towards out-of-the-lab use, without the need for supervision for use by a non-expert user.

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“…Human–machine integration involves a deep interaction among humans, machines, and environments. Tactile (or force information) plays an indispensable role as one of the indispensable media, together with visual and auditory signals [ 1 , 2 , 3 ]. It is well known that humans directly interact with the environment through tactile or force perceptions through hands or fingers [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Origami-Inspired Structure with Pneumatic-Induced Variable Stiffness for Multi-DOF Force-Sensing

Yue

Song

et al. 2022

Sensors

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With the emerging need for human–machine interactions, multi-modal sensory interaction is gradually pursued rather than satisfying common perception forms (visual or auditory), so developing flexible, adaptive, and stiffness-variable force-sensing devices is the key to further promoting human–machine fusion. However, current sensor sensitivity is fixed and nonadjustable after fabrication, limiting further development. To solve this problem, we propose an origami-inspired structure to achieve multiple degrees of freedom (DoFs) motions with variable stiffness for force-sensing, which combines the ductility and flexibility of origami structures. In combination with the pneumatic actuation, the structure can achieve and adapt the compression, pitch, roll, diagonal, and array motions (five motion modes), which significantly increase the force adaptability and sensing diversity. To achieve closed-loop control and avoid excessive gas injection, the ultra-flexible microfiber sensor is designed and seamlessly embedded with an approximately linear sensitivity of ∼0.35 Ω/kPa at a relative pressure of 0–100 kPa, and an exponential sensitivity at a relative pressure of 100–350 kPa, which can render this device capable of working under various conditions. The final calibration experiment demonstrates that the pre-pressure value can affect the sensor’s sensitivity. With the increasing pre-pressure of 65–95 kPa, the average sensitivity curve shifts rightwards around 9 N intervals, which highly increases the force-sensing capability towards the range of 0–2 N. When the pre-pressure is at the relatively extreme air pressure of 100 kPa, the force sensitivity value is around 11.6 Ω/N. Therefore, our proposed design (which has a low fabrication cost, high integration level, and a suitable sensing range) shows great potential for applications in flexible force-sensing development.

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Human–Machine Integration in Processes within Industry 4.0 Management

Cited by 9 publications

References 58 publications

Neoindustrialization—Reflections on a New Paradigmatic Approach for the Industry: A Scoping Review on Industry 5.0

Neoindustrialization—Reflections on a New Paradigmatic Approach for the Industry: A Scoping Review on Industry 5.0

Unsupervised Detection of Covariate Shift Due to Changes in EEG Headset Position: Towards an Effective Out-of-Lab Use of Passive Brain–Computer Interface

Origami-Inspired Structure with Pneumatic-Induced Variable Stiffness for Multi-DOF Force-Sensing

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