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"human–machine interaction"
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Driver assist system for human–machine interaction
A haptic driver–vehicle steering interface is introduced that interacts with the driver through environmentally mediated torque and stiffness changes, thereby communicating the vehicle’s proximity to constraints in the driving environment. The system design is based on principles of distributed cognition, which are used to shape the force characteristics to provide the driver with a tangible, rich, distributed representation of the task constraints. This mapping of the task constraints aids the human operator in satisfying a variety of needs, including managing risk, maintaining contextual awareness and achieving a satisfactory level of performance. The proposed design philosophy was applied to implement a haptic steering system in a real-world test vehicle to assist drivers in navigating through an experimental course with tight passages. In tests conducted with 12 participants, not only did most drivers show improved performance, but activities identified as epistemic behaviors were also observed; in the context of driving, epistemic behaviors are actions through which a driver probes the environment to maintain contextual awareness for the purpose of actively maintaining a satisfactory balance between performance and risk. These findings indicate that the proposed system design allows humans to actively integrate the haptic interface system into their cognitive loops and that the resulting human–machine system achieves higher performance than the human alone. The observed human–machine system interaction is interpreted as achieving improved resilience against variations in environmentally imposed risks.
Journal Article
Tackling Faults in the Industry 4.0 Era—A Survey of Machine-Learning Solutions and Key Aspects
The recent advancements in the fields of artificial intelligence (AI) and machine learning (ML) have affected several research fields, leading to improvements that could not have been possible with conventional optimization techniques. Among the sectors where AI/ML enables a plethora of opportunities, industrial manufacturing can expect significant gains from the increased process automation. At the same time, the introduction of the Industrial Internet of Things (IIoT), providing improved wireless connectivity for real-time manufacturing data collection and processing, has resulted in the culmination of the fourth industrial revolution, also known as Industry 4.0. In this survey, we focus on the vital processes of fault detection, prediction and prevention in Industry 4.0 and present recent developments in ML-based solutions. We start by examining various proposed cloud/fog/edge architectures, highlighting their importance for acquiring manufacturing data in order to train the ML algorithms. In addition, as faults might also occur from sources beyond machine degradation, the potential of ML in safeguarding cyber-security is thoroughly discussed. Moreover, a major concern in the Industry 4.0 ecosystem is the role of human operators and workers. Towards this end, a detailed overview of ML-based human–machine interaction techniques is provided, allowing humans to be in-the-loop of the manufacturing processes in a symbiotic manner with minimal errors. Finally, open issues in these relevant fields are given, stimulating further research.
Journal Article
In our own image : savior or destroyer?
Exploring the history and future, as well as the societal and ethical implications, of Artificial Intelligence (AI), the author, who has a PhD in AI, explains its history, technology and potential; its manifestations in intelligent machines; its connections to neurology and conscious; and what AI reveals about us human beings.
Book
The evolution of man-machine interaction: the role of human in Industry 4.0 paradigm
Industry 4.0 is a new paradigm in the manufacturing world and it has deeply changed the Human-machine interaction. This paper focus is on the nature of this interaction, which is made possible thanks to the Internet of Things (IoT), and Cyber-Physical System (CPS). These Industry 4.0 key technologies are studied related to the standard Deming cycle, in order to underline the importance of Human-machine interaction. The Fourth Industrial Revolution involves several changes in the workforce's key features. In this paper, a new perspective based on the centrality of humans is given in the new Industry era. The importance of the human factor will be deeply studied through the implementation of the 'Sand Cone Model'. A new framework is proposed in order to explain the quality measures addiction on the workforce quality skills, and how it engraves on improving efficiency and effectiveness of an industrial process.
Journal Article
5G NB‐IoT System Integrated with High‐Performance Fiber Sensor Inspired by Cirrus and Spider Structures
Real‐time telemedicine detection can solve the problem of the shortage of public medical resources caused by the coming aging society. However, the development of such an integrated monitoring system is hampered by the need for high‐performance sensors and the strict‐requirement of long‐distance signal transmission and reproduction. Here, a bionic crack‐spring fiber sensor (CSFS) inspired by spider leg and cirrus whiskers for stretchable and weavable electronics is reported. Trans‐scale conductive percolation networks of multilayer graphene around the surface of outer spring‐like Polyethylene terephthalate (PET) fibers and printing Ag enable a high sensitivity of 28475.6 and broad sensing range over 250%. The electromechanical changes in different stretching stages are simulated by Comsol to explain the response mechanism. The CSFS is incorporated into the fabric and realized the human‐machine interactions (HMIs) for robot control. Furthermore, the 5G Narrowband Internet of Things (NB‐IoT) system is developed for human healthcare data collection, transmission, and reproduction together with the integration of the CSFS, illustrating the huge potential of the approach in human–machine communication interfaces and intelligent telemedicine rehabilitation and diagnosis monitoring. Trans‐scale conductive percolation networks are constructed on the high‐performance bionic crack‐spring fiber sensor (CSFS) with high sensitivity and broad sensing range for human–machine communication interfaces and long‐distance information transmission with the assistance of 5G NB‐IoT system.
Journal Article
Bioinspired sensor system for health care and human‐machine interaction
Bioinspired sensor system leads the development of new generation sensor technology with remarkable features like ultra‐sensitivity, low‐power consumption and self‐adaptability. With the help of bioinspired sensor systems, human perception can be quantified and machines can be endowed with specific perception. As an emerging technology, bioinspired sensor system has been widely used in various fields such as industrial, medical, food safety, military and robotic. This review summarizes the recent process of bioinspired sensor system. First, three bionic strategies are defined as bionic materials, bionic structures, and functional bionic according to the sources of bionic inspiration. Second, bioinspired sensor systems with different working mechanisms are summarized and classified into piezoresistive, capacitive, triboelectric, piezoelectric, and other types. Afterward, for applications, the representative works of bioinspired sensor system for health care and human‐machine interaction are focused and introduced, respectively. Finally, the current challenges and prospects of bioinspired sensor system are also discussed. Bioinspired sensor system possesses sensing performance rivaling nature and various bionic functionalities. This review summarizes different bionic strategies and working principles of bionic sensor systems. The representative works of the bioinspired sensor system for health care and human‐machine interaction are focused and introduced, respectively. In addition, the current challenges and prospects of the bioinspired sensor system are also discussed.
Journal Article