Search results

Maintaining the safety of the human is a major concern in factories where humans co-exist with robots and other physical tools. Typically, the area around the robots is monitored using lasers. However, lasers cannot distinguish between human and non-human objects in the robot’s path. Stopping or slowing down the robot when non-human objects approach is unproductive. This research contribution addresses that inefficiency by showing how computer-vision techniques can be used instead of lasers which improve up-time of the robot.

A computer-vision safety system is presented. Image segmentation, 3D point clouds, face recognition, hand gesture recognition, speed and trajectory tracking and a digital twin are used. Using speed and separation, the robot’s speed is controlled based on the nearest location of humans accurate to their body shape. The computer-vision safety system is compared to a traditional laser measure. The system is evaluated in a controlled test, and in the field.

Computer-vision and lasers are shown to be equivalent by a measure of relationship and measure of agreement. R² is given as 0.999983. The two methods are systematically producing similar results, as the bias is close to zero, at 0.060 mm. Using Bland–Altman analysis, 95% of the differences lie within the limits of maximum acceptable differences.

In this paper an original model for future computer-vision safety systems is described which is equivalent to existing laser systems, identifies and adapts to particular humans and reduces the need to slow and stop systems thereby improving efficiency. The implication is that computer-vision can be used to substitute lasers and permit adaptive robotic control in human–robot collaboration systems.

Vision, audition, olfactory, tactile and taste are five important senses that human uses to interact with the real world. As facing more and more complex environments, a sensing system is essential for intelligent robots with various types of sensors. To mimic human-like abilities, sensors similar to human perception capabilities are indispensable. However, most research only concentrated on analyzing literature on single-modal sensors and their robotics application.

This study presents a systematic review of five bioinspired senses, especially considering a brief introduction of multimodal sensing applications and predicting current trends and future directions of this field, which may have continuous enlightenments.

This review shows that bioinspired sensors can enable robots to better understand the environment, and multiple sensor combinations can support the robot’s ability to behave intelligently.

The review starts with a brief survey of the biological sensing mechanisms of the five senses, which are followed by their bioinspired electronic counterparts. Their applications in the robots are then reviewed as another emphasis, covering the main application scopes of localization and navigation, objection identification, dexterous manipulation, compliant interaction and so on. Finally, the trends, difficulties and challenges of this research were discussed to help guide future research on intelligent robot sensors.

In the COVID-19 era, sign language (SL) translation has gained attention in online learning, which evaluates the physical gestures of each student and bridges the communication gap between dysphonia and hearing people. The purpose of this paper is to devote the alignment between SL sequence and nature language sequence with high translation performance.

SL can be characterized as joint/bone location information in two-dimensional space over time, forming skeleton sequences. To encode joint, bone and their motion information, we propose a multistream hierarchy network (MHN) along with a vocab prediction network (VPN) and a joint network (JN) with the recurrent neural network transducer. The JN is used to concatenate the sequences encoded by the MHN and VPN and learn their sequence alignments.

We verify the effectiveness of the proposed approach and provide experimental results on three large-scale datasets, which show that translation accuracy is 94.96, 54.52, and 92.88 per cent, and the inference time is 18 and 1.7 times faster than listen-attend-spell network (LAS) and visual hierarchy to lexical sequence network (H2SNet) , respectively.

In this paper, we propose a novel framework that can fuse multimodal input (i.e. joint, bone and their motion stream) and align input streams with nature language. Moreover, the provided framework is improved by the different properties of MHN, VPN and JN. Experimental results on the three datasets demonstrate that our approaches outperform the state-of-the-art methods in terms of translation accuracy and speed.

This study investigates how federated learning (FL) and human–robot collaboration (HRC) can be used to manage diverse industrial environments effectively. We aim to demonstrate how these technologies not only improve cooperation between humans and robots but also significantly enhance productivity and innovation within industrial settings. Our research proposes a new framework that integrates these advancements, paving the way for smarter and more efficient factories.

This paper looks into the difficulties of handling diverse industrial setups and explores how combining FL and HRC in the mark of Industry 5.0 paradigm could help. A literature review is conducted to explore the theoretical insights, methods and applications of these technologies that justify our proposal. Based on this, a conceptual framework is proposed that integrates these technologies to manage heterogeneous industrial environments.

The findings drawn from the literature review performed, demonstrate that personalized FL can empower robots to evolve into intelligent collaborators capable of seamlessly aligning their actions and responses with the intricacies of factory environments and the preferences of human workers. This enhanced adaptability results in more efficient, harmonious and context-sensitive collaborations, ultimately enhancing productivity and adaptability in industrial operations.

This research underscores the innovative potential of personalized FL in reshaping the HRC landscape for manage heterogeneous industrial environments, marking a transformative shift from traditional automation to intelligent collaboration. It lays the foundation for a future where human–robot interactions are not only more efficient but also more harmonious and contextually aware, offering significant value to the industrial sector.

The purpose of this study is to design a highly integrated smart glove to enable gesture acquisition and force sensory interactions, and to enhance the realism and immersion of virtual reality interaction experiences.

The smart glove is highly integrated with gesture sensing, force-haptic acquisition and virtual force feedback modules. Gesture sensing realizes the interactive display of hand posture. The force-haptic acquisition and virtual force feedback provide immersive force feedback to enhance the sense of presence and immersion of the virtual reality interaction.

The experimental results show that the average error of the finger bending sensor is only 0.176°, the error of the arm sensor is close to 0 and the maximum error of the force sensing is 2.08 g, which is able to accurately sense the hand posture and force-touch information. In the virtual reality interaction experiments, the force feedback has obvious level distinction, which can enhance the sense of presence and immersion during the interaction.

This paper innovatively proposes a highly integrated smart glove that cleverly integrates gesture acquisition, force-haptic acquisition and virtual force feedback. The glove enhances the sense of presence and immersion of virtual reality interaction through precise force feedback, which has great potential for application in virtual environment interaction in various fields.

In the tourism sector, fully unmanned and partially unmanned hotel models serving customer segments from different income groups are increasing. Analyzing examples of unmanned hotels worldwide and their practices is crucial for understanding the automation systems used, the smart technologies employed, and the opportunities and challenges these hotels present, as well as for gaining insights into their impacts on the tourism sector.

The data used in this research were obtained from secondary sources. One of the qualitative research methods, document analysis, was used for the analysis of these sources. The content analysis technique was used in the analysis of the data. A seven-stage systematic review process was used in the research. This seven-stage review process consists of the following stages: (1) determining the review objectives and formulating research questions; (2) identifying search terms and selection criteria; (3) conducting a search for unmanned hotel applications before clarifying exclusion and inclusion criteria; (4) evaluating the quality and relevance of unmanned hotel applications; (5) identifying content analysis review variables; (6) conducting content analysis; and (7) analyzing and reporting the findings.

In traditional hotel management, the innovations brought by digitalization and automation are transforming the guest experience and increasing operational efficiency. Unmanned smart hotels are equipped with various technological solutions, such as voice-controlled AI assistants, smart room control systems, AI-based concierge services, and robotic room service. These hotels are redefining roles and expectations within traditional hotel management, while simultaneously reducing costs and enhancing efficiency. Analyses indicate that unmanned smart hotels particularly appeal to specific customer segments, such as business travelers, and are becoming increasingly popular. These hotels offer advantages such as allowing guests to perform self-check-in, control their rooms, and receive necessary services via robots.

The universe of the research consists of all currently operating unmanned hotels worldwide. As a result of the research, 18 examples of unmanned smart hotels were identified. Hotels within the same chain with identical applications and processes were considered as a single example. Therefore, the research sample consists of 18 hotels.

By integrating these technological advancements, the hospitality and tourism industries can mitigate the impact of staff shortages, maintain high service standards, and improve operational efficiency. This approach allows businesses to adapt to changing workforce dynamics while continuing to deliver exceptional guest experiences. In conclusion, the significance and impact of unmanned smart hotels in the travel industry are growing. These hotels have the potential to shape the role of technology in the hospitality sector and influence future trends. Therefore, the adoption and development of unmanned smart hotels are important considerations for hotel operators and industry experts.

This paper aims to concentrate on recent innovations in flexible wearable sensor technology tailored for monitoring vital signals within the contexts of wearable sensors and systems developed specifically for monitoring health and fitness metrics.

In recent decades, wearable sensors for monitoring vital signals in sports and health have advanced greatly. Vital signals include electrocardiogram, electroencephalogram, electromyography, inertial data, body motions, cardiac rate and bodily fluids like blood and sweating, making them a good choice for sensing devices.

This report reviewed reputable journal articles on wearable sensors for vital signal monitoring, focusing on multimode and integrated multi-dimensional capabilities like structure, accuracy and nature of the devices, which may offer a more versatile and comprehensive solution.

The paper provides essential information on the present obstacles and challenges in this domain and provide a glimpse into the future directions of wearable sensors for the detection of these crucial signals. Importantly, it is evident that the integration of modern fabricating techniques, stretchable electronic devices, the Internet of Things and the application of artificial intelligence algorithms has significantly improved the capacity to efficiently monitor and leverage these signals for human health monitoring, including disease prediction.

Recent emerging information technologies like digital twin (DT) provide new concepts and transform information management processes in the architecture, engineering and construction (AEC) industry. Although numerous articles are pertinent to DT applications, existing research areas and potential future directions related to the state-of-the-art DT in project operation and maintenance (O&M) are yet to be studied. Therefore, this paper aims to review the state-of-the-art research on DT applications in project O&M.

The current review adopted four methodological steps, including literature search, literature selection, science mapping analysis and qualitative discussion to gain a deeper understanding of DT in project O&M. The impact and contribution of keywords and documents were examined from a total of 444 journal articles retrieved from the Scopus database.

Five mainstream research topics were identified, including (1) DT-based artificial intelligence technology for project O&M, (2) DT-enabled smart city and sustainability, (3) DT applications for project asset management, (4) Blockchain-integrated DT for project O&M and (5) DT for advanced project management. Subsequently, research gaps and future research directions were proposed.

This study intends to raise awareness of future research by summarizing the current DT development phases and their impact on DT implementation in project O&M among researchers and practitioners.

This study aims to offer a comprehensive exploration of the potential and challenges associated with sensor fusion-based virtual reality (VR) applications in the context of enhanced physical training. The main objective is to identify key advancements in sensor fusion technology, evaluate its application in VR systems and understand its impact on physical training.

The research initiates by providing context to the physical training environment in today’s technology-driven world, followed by an in-depth overview of VR. This overview includes a concise discussion on the advancements in sensor fusion technology and its application in VR systems for physical training. A systematic review of literature then follows, examining VR’s application in various facets of physical training: from exercise, skill development and technique enhancement to injury prevention, rehabilitation and psychological preparation.

Sensor fusion-based VR presents tangible advantages in the sphere of physical training, offering immersive experiences that could redefine traditional training methodologies. While the advantages are evident in domains such as exercise optimization, skill acquisition and mental preparation, challenges persist. The current research suggests there is a need for further studies to address these limitations to fully harness VR’s potential in physical training.

The integration of sensor fusion technology with VR in the domain of physical training remains a rapidly evolving field. Highlighting the advancements and challenges, this review makes a significant contribution by addressing gaps in knowledge and offering directions for future research.

This study explores how customers' individual characteristics and perceptions affect acceptance of service robots as a hotel workforce. The Interactive Technology Acceptance Model (iTAM) has inspired us to investigate effects of customers' technological self-efficacy, perceived interactivity, sense of utility, and enjoyment-level of acceptance related to hotel-service robots as staff.

Data were collected from 224 customers via an online questionnaire conducted in the period April–June 2022 by convenience sampling, and then analyzed by using partial least squares – structural equation modeling (PLS-SEM).

The findings show that customers' technological self-efficacy and perceived interactivity with service robots enhances perceived usefulness and perceived enjoyment, serving as functional and emotional value components of service robots. They also demonstrate that robot's interactivity outweighs other robot's value components, such as perceived usefulness and perceived enjoyment for acceptance of service robots as employees in hotels.

While empirically validating the iTAM, this study emphasizes service robot interactivity as the most important aspect for customers' acceptance, and it adds a new perspective regarding the underexplored role of the customer-robot interface. Combining specific dimensions from different technology acceptance models (functional/socio-emotional/relational; utilitarian/hedonic) the study contributes to the service robot literature currently missing a more holistic understanding of consumers' experience and adoption drivers, and it provides managerial guidance on how to successfully implement service robots in hotel environments.