Human-machine Interaction Research Research Articles

Flexible and stretchable implantable devices for peripheral neuromuscular electrophysiology.

The peripheral nervous and muscular system, a cornerstone of human physiology, plays a pivotal role in ensuring the seamless functioning of the human body. This intricate network, comprising nerves and muscles extending throughout the body, is essential for motor control, sensory feedback, and the regulation of autonomic bodily functions. The qualified implantable peripheral interface can accurately monitor the biopotential of the target tissue and conduct treatment with stimulation, enhancing the human-machine interaction and new achievements in disease cure. Implantable electrodes have revolutionized the field of neuromuscular interfaces, offering precise bidirectional communication between the neuromuscular system and external devices. They enable natural control for individuals with limb loss, bridging the gap between mind and machine and aiding neuromuscular rehabilitation. In research and medical diagnostics, implantable electrodes provide invaluable tools for studying neuromuscular function and the development of therapies. However, traditional rigid electrodes face challenges due to the dynamic nature of the peripheral neuromuscular system. Flexible and stretchable devices show immense promise in accommodating dynamic alterations, offering adaptability, and accurate monitoring of electrophysiological signals. This review delves into the challenges associated with the peripheral interface, primarily focusing on monitoring and stimulation. It then provides a summary of common materials and structural design optimizations, discusses technologies for enhancing interface adhesion and surface functionalization, and explores encapsulation methods for implanted devices. Recent advancements in energy supply and the applications of implantable, flexible, and stretchable devices are also comprehensively reviewed, with due consideration given to ethical concerns and signal analysis. The promising directions are finally presented to provide enlightenment for high-performance sensor-tissue interfaces in the future, which will promote profound progress in clinical and human-machine interaction research. Flexible and stretchable devices are at the forefront of healthcare, with the potential to transform the treatment of neuromuscular disorders and enhance human augmentation, blurring the lines between natural and artificial limbs. They represent a promising avenue for the future, with exciting applications in healthcare, science, and technology, promising to bring us closer to the seamless integration of human and machine in the realm of neuromuscular interfaces.

Intention recognition-based human–machine interaction for mixed flow assembly

Mixed-flow assembly provides a highly adaptable solution for production processes, catering to the extensive customization requirements in product manufacturing. Human–machine interaction (HMI) can significantly enhance the efficiency, flexibility, and responsiveness of mixed-flow production lines by enabling operators to collaborate with machines. However, current HMI research primarily focuses on temporal or spatial interaction, where tasks are completed independently by the operator and machine in a predetermined sequence, with emphasis on collision avoidance in a shared workspace. Research on spatial–temporal HMI is currently lacking. This paper addresses this gap through three comprehensive steps. Firstly, we develop an intention recognition model based on Gaussian mixture distribution to effectively identify the user’s operational intention. Subsequently, we design a hybrid industrial communication architecture to facilitate intent sharing among various production elements. Finally, an empirical study is conducted to assess the impact of different forms of HMI on assembly efficiency and error rates. Our experimental results demonstrate that spatial–temporal HMI can enhance assembly efficiency by approximately 15% and reduce the defective rate. In future studies, the proposed spatial–temporal cooperation platform could integrate additional perception technologies to alleviate assembly fatigue and improve operators’ well-being.

Visual Static Hand Gesture Recognition Using Convolutional Neural Network

Hand gestures are an essential part of human-to-human communication and interaction and, therefore, of technical applications. The aim is increasingly to achieve interaction between humans and computers that is as natural as possible, for example, by means of natural language or hand gestures. In the context of human-machine interaction research, these methods are consequently being explored more and more. However, the realization of natural communication between humans and computers is a major challenge. In the field of hand gesture recognition, research approaches are being pursued that use additional hardware, such as special gloves, to classify gestures with high accuracy. Recently, deep learning techniques using artificial neural networks have been increasingly proposed for the problem of gesture recognition without using such tools. In this context, we explore the approach of convolutional neural network (CNN) in detail for the task of hand gesture recognition. CNN is a deep neural network that can be used in the fields of visual object processing and classification. The goal of this work is to recognize ten types of static hand gestures in front of complex backgrounds and different hand sizes based on raw images without the use of extra hardware. We achieved good results with a CNN network architecture consisting of seven layers. Through data augmentation and skin segmentation, a significant increase in the model’s accuracy was achieved. On public benchmarks, two challenging datasets have been classified almost perfectly, with testing accuracies of 96.5% and 96.57%.

Research Perspectives into Translation Technologies

This paper presents the core ideas discussed by the #tradumatica20_research panel held online on 17 October 2022. The panel was moderated by Ricardo Muñoz Martín and featured, in order of intervention, Vanessa Enríquez Raído, Miguel A. Jiménez-Crespo, Christian Olalla-Soler and Pilar Sánchez-Gijón as participants. The paper follows the sequence of interview questions posed to the panelists, which focused on new challenges and opportunities in translation and interpreting technology, the importance of the "human factor" in this field, and the value of human-machine interaction research. The paper concludes with reflections on changes in translation and interpreting and how they are addressed in academic research.

A Hybrid Time-Distributed Deep Neural Architecture for Speech Emotion Recognition.

In recent years, speech emotion recognition (SER) has emerged as one of the most active human-machine interaction research areas. Innovative electronic devices, services and applications are increasingly aiming to check the user emotional state either to issue alerts under some predefined conditions or to adapt the system responses to the user emotions. Voice expression is a very rich and noninvasive source of information for emotion assessment. This paper presents a novel SER approach based on that is a hybrid of a time-distributed convolutional neural network (TD-CNN) and a long short-term memory (LSTM) network. Mel-frequency log-power spectrograms (MFLPSs) extracted from audio recordings are parsed by a sliding window that selects the input for the TD-CNN. The TD-CNN transforms the input image data into a sequence of high-level features that are feed to the LSTM, which carries out the overall signal interpretation. In order to reduce overfitting, the MFLPS representation allows innovative image data augmentation techniques that have no immediate equivalent on the original audio signal. Validation of the proposed hybrid architecture achieves an average recognition accuracy of 73.98% on the most widely and hardest publicly distributed database for SER benchmarking. A permutation test confirms that this result is significantly different from random classification ([Formula: see text]). The proposed architecture outperforms state-of-the-art deep learning models as well as conventional machine learning techniques evaluated on the same database trying to identify the same number of emotions.

Incorporating Virtual Reality Technology in Safety Training Solution for Construction Site of Urban Cities

The lack of experiential training has become the primary cause of incidents that could have been easily avoided in construction areas, and the implementation of experimental training is crucial in minimizing incidents at construction sites. The limitation on the available training tools has made it impossible to build up a real test site or reproduce the constructing scenarios. This research aims to develop an immersive and interactive multiplayer-based training platform that incorporates virtual reality (VR) technology to improve the safety awareness of workers. The developed simulation platform serves as a training solution, enabling the provision of repeatable and flexible procedures within a secure environment. An evaluation survey was conducted to make a comparison between traditional training methods and the proposed VR solution. Promisingly, the results indicate that workers were better trained under the developed immersive environment, and they could memorize critical points more effectively because the implementation of VR technology can allow people to experience hazardous situations without being physically injured, thus creating a safer and more efficient training environment. This study reveals that the nomination of the proposed VR platform could reap many benefits and become an advantageous tool for construction training, as well as stimulate human-machine interaction research.

The importance of procedural justice in Human–Machine Interactions: Intelligent systems as new decision agents in organizations

In the present study, the effects of procedural justice (fair or unfair) and the type of decision agent (human, robot, or computer) on employee behavior and attitudes (e.g., job satisfaction, organizational citizenship behavior, or counterproductive work behaviors) were examined. It was predicted that the type of decision agent (or the source of justice) would moderate the relationship between procedural justice and employee behavior and attitudes, with the relationship being strongest when the decision agent is a human team leader, medium when the decision agent is a humanoid robot, and weakest when the agent is a computer system. This research question was investigated with a between-subjects design in two experiments (N1 = 149 and N2 = 145) that displayed two different decision situations in organizations (allocation of new tasks and allocation of further vocational trainings). Results of both studies showed significant effects of procedural justice on employee behavior and attitudes, confirming the importance of procedural justice at the workplace for both human and system decision agents. Furthermore, both studies failed to verify any interaction effects of procedural justice and the decision agent. This further emphasizes the importance of procedural justice in decision situations because there is no difference in reactions to procedural justice of human or system decisions. Limitations and implications for future research and the integration of justice and human–machine interaction research are discussed.

Facial Expression Analysis using Active Shape Model

Facial expressions analysis is a vital part of the research in human-machine interaction. This chapter introduces an automatic recognition system for facial expression from a front view human face image. Obtaining an effective facial representation from initial face images is an essential phase for strong and efficient facial expression system. In this chapter we have developed a facial expression analysis system. Firstly we have tried to evaluate facial analysis base on Active shape model (ASM). In order to detect the face images, Adaboost classifier and Haar-Like feature has been adopted to achieve face detection and tracking. The ASM then has been automatically initiated in the detected face image. Then, discriminates and reliable facial feature points has been extracted applying ASM fitting technique. The geometric displacement among the projected ASM feature points coordinates and the mean shape of ASM were used to evaluate facial expression. Using support vector machine (SVM) classifier, the obtained results has reached a recognition rate of 93 %.

Standing Office Furniture Design Based on Human-Machine Interaction Research

Based on the current status of the development of the market for office furniture, an entry point is to study the design of man-machine relationship, research and analyze the defects and problems about existing office furniture, Thus devise a sit, stand dual desk, It can not only achieve the basic functions of their office, but also allows to bring workers a different sports experience in the office at the same time, and improve work efficiency.

Cognitive Computation Special Issue on Cognitive Behavioural Systems

Cognitive processes, such as inference, categorization, and memory, are not independent from their physical instantiations. Individuals’ choices, perception, and actions emerge and are dynamically affected and enhanced by the interaction between sensory-motor systems and the inhabited environment that includes the organizational, cultural, and physical context). This interplay brings up instantiations of cognitive behavioural systems. The above considerations led to the idea to dedicate a special issue of Cognitive Computation to this topic, with the aims to initiate a discussion on what has been achieved to-date and look at future perspectives. The volume originated in the European Science Foundation (ESF) Research Network COST Action 2102: Cross Modal Analysis of Verbal and Nonverbal Communication (www.cost.esf.org/domains_actions/ict/Actions/2102) funded by the European Cooperation in Science and Technology (COST, www.cost.esf.org). COST is an European instrument funded by the ESF supporting cooperation among scientists and researchers across Europe, allowing the coordination of nationally-funded research on a European level, increasing the mobility of Early Stage Researchers and fostering the establishment of scientific excellence in nine key domains, including Information Communication Technology (ICT)—of which COST Action 2102 is part of. The main objective of COST 2102 was to develop an advanced acoustical, perceptual and psychological analysis of verbal and nonverbal communication signals originating in spontaneous body-to-body interaction, in order to identify algorithms and automatic procedures capable of automatically perceiving and distinguishing human emotional states. Researchers involved in COST 2102 are convinced emotions drive our daily life and that research in HumanMachine Interaction cannot continue to neglect this aspect of human behaviour in developing future communication systems. Several key aspects were considered, such as the integration of the developed algorithms and procedures for application in telecommunications, and for the recognition of emotional states, gestures, speech and facial expressions—in anticipation of the implementation of intelligent avatars and interactive dialogue systems that could be exploited to improve user access to future telecommunication services. The Action profited from two former COST Actions (COST 277 and COST 278) that identified new appropriate mathematical models and algorithms to drive the implementation of the next generation of telecommunication services such as remote health monitoring systems, interactive dialogue systems, and intelligent avatars. A. Esposito (&) Department of Psychology and IIASS, Second University of Naples, Naples, Italy e-mail: iiass.annaesp@tin.it

A methodology for human-machine systems research: knowledge engineering, modeling, and simulation

This paper proposes a methodology for carrying out human-machine systems engineering research and in particular focuses on the development of knowledge-based support for training and aiding. The process is characterized by the definition of knowledge requirements, a normative model structure of human-machine interaction, a knowledge architecture that implements the model, and an interactive real-time simulation environment. The knowledge requirements largely specify the model, knowledge architecture, and simulation. The model and the knowledge architecture are the basis for the design of intelligent systems. The simulation serves as an experimental testbed that provides a feasible, safe, and effective means for exploring alternative human-machine systems designs and also facilitates technology transfer. The aim is to create an understanding of a complex work domain that forms the context for ongoing research in human-machine interaction. A specific example from satellite ground control is described. >

Designing for Telepresence: The Interdependence of Movement and Visual Perception Implemented

In this paper an overview of the Delft depth television system is given, referring to definitions of concepts from robotics and perception psychology that are crucial in order to explain the working principle of the system, as well as the system’s performance measurements. The second section refers to the theoretical background. What is the relevant visual input in optic flow to allow for depth perception and, therefore, for telemanipulation and for enhanced telepresence? The third section discusses telepresence applications using the Delft depth television system. The paper intends to emphasize the critical human-machine interaction research problems from the perspective of the Gibsonian theory of perception. It is parochial in that it deals with research and design engineering problems tackled at our laboratory. Our research effort concerns the role of active (observer-generated) movement in perception and its application in technology.