Forms Of Human-machine Interaction Research Articles

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.

Data glove-based gesture recognition using CNN-BiLSTM model with attention mechanism

As a novel form of human machine interaction (HMI), hand gesture recognition (HGR) has garnered extensive attention and research. The majority of HGR studies are based on visual systems, inevitably encountering challenges such as depth and occlusion. On the contrary, data gloves can facilitate data collection with minimal interference in complex environments, thus becoming a research focus in fields such as medical simulation and virtual reality. To explore the application of data gloves in dynamic gesture recognition, this paper proposes a data glove-based dynamic gesture recognition model called the Attention-based CNN-BiLSTM Network (A-CBLN). In A-CBLN, the convolutional neural network (CNN) is employed to capture local features, while the bidirectional long short-term memory (BiLSTM) is used to extract contextual temporal features of gesture data. By utilizing attention mechanisms to allocate weights to gesture features, the model enhances its understanding of different gesture meanings, thereby improving recognition accuracy. We selected seven dynamic gestures as research targets and recruited 32 subjects for participation. Experimental results demonstrate that A-CBLN effectively addresses the challenge of dynamic gesture recognition, outperforming existing models and achieving optimal gesture recognition performance, with the accuracy of 95.05% and precision of 95.43% on the test dataset.

Responsibility gap or responsibility shift? The attribution of criminal responsibility in human–machine interaction

ABSTRACT The prevalence of automation and advent of intelligent machines have created new constellations in which the attribution of criminal responsibility is complicated. Automation results in complex settings of interaction, while the conduct of technical systems is becoming less determined, predictable, and transparent. There is an ongoing scholarly debate regarding how these developments shape moral and legal agency, as well as the best ways of allocating responsibility. Concerns have been expressed about the emergence of responsibility gaps, calling for ways to ensure accountability. This article explores whether these apprehensions can be empirically substantiated. A factorial survey study (N = 799) conducted in Switzerland was used to research attribution mechanisms in criminal cases involving various forms of human-machine interaction. The results revealed that the level of automation significantly affected the attribution of responsibility. In cases of high automation, attribution became considerably more complex and more actors, especially corporate entities, were called to account. A difference in automation level (i.e., the question of how much humans are still ‘in the loop’) had a stronger effect than did the aspect of technology being described as capable of learning. However, the involvement of intelligent technology seemed to have made the responsibility attribution more arbitrary. According to the respondents, there were no discernable responsibility gaps. However, in the present research, significant shifts among the agents called to account were observable. Yet, since these evasion mechanisms are hardly covered by today's law, there is an ongoing risk of a gap between the desire for punishment and actual legal constructs.

Gesture-Based Human Machine Interaction Using RCNNs in Limited Computation Power Devices.

The use of gestures is one of the main forms of human machine interaction (HMI) in many fields, from advanced robotics industrial setups, to multimedia devices at home. Almost every gesture detection system uses computer vision as the fundamental technology, with the already well-known problems of image processing: changes in lighting conditions, partial occlusions, variations in color, among others. To solve all these potential issues, deep learning techniques have been proven to be very effective. This research proposes a hand gesture recognition system based on convolutional neural networks and color images that is robust against environmental variations, has a real time performance in embedded systems, and solves the principal problems presented in the previous paragraph. A new CNN network has been specifically designed with a small architecture in terms of number of layers and total number of neurons to be used in computationally limited devices. The obtained results achieve a percentage of success of 96.92% on average, a better score than those obtained by previous algorithms discussed in the state of the art.

AN INTERACTIVE ANALYSIS OF 3D TECHNOLOGY AND ITS VAST APPLICATION TO HUMAN

The word 3D is defined as three dimensional models that display a picture or item in a form that appears to be physically present with a designated structure. Three-dimensional (3D), can also describe any object that occurs on a three-axis Cartesian coordinate system, a Cartesian system is basically a fancy way of describing the X and Y axes which are horizontal and vertical axis, but the inclusive of the third axis Z which is make it to be 3D of which the Z axis represent the depth.3D interaction is a form of human-machine interaction where users are able to move and perform interaction in 3D space. Both human and machine process information where the physical position of elements in 3D space. The main features of these 3D applications are immersion, interactivity, and involvement. Immersion holds the user’s attention. Interactivity is related to how responsive the application is to user actions and Involvement has to do with engaging the user’s interest in the underlying activity.3D interaction techniques are selection and manipulation, navigation, system control and symbolic inputs. Its application is in the area of Exploring Complex Data, Visualizations with the Cubic Mouse, Multimodal Interfaces in VEs Multimodal interaction, and VEs for Design Education Architectural design. KEYWORDS:interaction,cartesian,information,application,interface,virtual,physical,technique,immersion,involvement,design,navigation and data.

Perceived stress in human-machine interaction in modern manufacturing environments-Results of a qualitative interview study.

Rapid advances in digitization technologies are changing modern working conditions especially in industrial settings. Consequently, employees are confronted with new forms of human-machine interaction. Whether changes in working conditions in general, and the increasing relevance of human-machine interaction in particular, affect psychosocial working conditions, and employee's health is currently matter of debate, but empirical data are lacking. Therefore, we conducted semistructured interviews with 36 employees working in five different companies. The interviews were aimed at identifying potential stressors associated with the introduction and use of modern technologies in the manufacturing industry. The results show that stressors linked to human-machine interaction are technical problems, poor usability, low situation awareness, and increased requirements on employees' qualification. For example, technical problems such as breakdowns or slowdowns were described as a main stressor when employees were not qualified to handle these problems on their own, thus decelerating work flows and causing additional time pressure. Overall, the results show that problems in human-machine interaction, which have been observed in laboratory and nonindustrial settings, also apply to industrial work places with highly automated working conditions and are a potential source of stress. These factors should be considered in psychosocial risk assessment of work-related stressors.

Development of a toolkit in Silverlight to detect physiognomies in real time

Security demands, forensic practices and the identification of criminals require the detection and recognition of iris and fingerprints and of faces in videos and photographs. Moreover, there is an increasing need for multiple forms of human-machine interaction. Control devices by body stimulus are a need and a trend. For example, currently some computers, laptops, phones and video games provide interaction from their cameras, not only for face detection but also for body movements and the detection of objects. Most devices are Internet accessed, which creates an even greater range of possibilities. These technological trends have prompted the development a toolkit for detecting faces in real time. The choice of Silverlight framework for the development of this toolkit provides these applications with instruments that could be implemented in a web browser. This toolkit may be used for other purposes, such as face and iris recognition, body movement and the monitoring of premises. An application was developed as example and proof of concept.

Integrating Augmented Reality in the Assembly Domain - Fundamentals, Benefits and Applications

Augmented Reality (AR) Is a new form of human-machine interaction which overlays computer-generated information on the real world environment, e.g. through a semi-transparent, head-mounted display. With AR presented information is derived from the real environment and is thus context-sensitive. Augmented Reality can there fore enhance a user's view with virtual information that is Sensitive to the current state of the surrounding real world. While Virtual Reality Technology (VRT) is commonly used in the automotive and aircraft industry, there exists few research approaches in the field of Augmented Reality. This contribution gives a detailed introduction to AR systems, and describes the capabilities and benefits of AR in the field of assembly. As an application, a modular AR system for guiding manual assembly and for use in assembly planning is described. Furthermore, a method for integrating AR into the planning process of manual assembly stations is presented.