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The purpose of this paper is to present a novel data glove which can capture the motion of the arm and hand by inertial and magnetic sensors. The proposed data glove is used to provide the information of the gestures and teleoperate the robotic arm-hand.

The data glove comprises 18 low-cost inertial and magnetic measurement units (IMMUs) which not only make up the drawbacks of traditional data glove that only captures the incomplete gesture information but also provide a novel scheme of the robotic arm-hand teleoperation. The IMMUs are compact and small enough to wear on the upper arm, forearm, palm and fingers. The calibration method is proposed to improve the accuracy of measurements of units, and the orientations of each IMMU are estimated by a two-step optimal filter. The kinematic models of the arm, hand and fingers are integrated into the entire system to capture the motion gesture. A positon algorithm is also deduced to compute the positions of fingertips. With the proposed data glove, the robotic arm-hand can be teleoperated by the human arm, palm and fingers, thus establishing a novel robotic arm-hand teleoperation scheme.

Experimental results show that the proposed data glove can accurately and fully capture the fine gesture. Using the proposed data glove as the multiple input device has also proved to be a suitable teleoperating robotic arm-hand system.

Integrated with 18 low-cost and miniature IMMUs, the proposed data glove can give more information of the gesture than existing devices. Meanwhile, the proposed algorithms for motion capture determine the superior results. Furthermore, the accurately captured gestures can efficiently facilitate a novel teleoperation scheme to teleoperate the robotic arm-hand.

Main purpose is to present methodology which allows efficient hand gesture recognition using low-budget, 5-sensor data glove. To allow widespread use of low-budget data gloves in engineering virtual reality (VR) applications, gesture dictionaries must be enhanced with more ergonomic and symbolically meaningful hand gestures, while providing high gesture recognition rates when used by different seen and unseen users.

The simple boundary-value gesture recognition methodology was replaced by a probabilistic neural network (PNN)-based gesture recognition system able to process simple and complex static gestures. In order to overcome problems inherent to PNN – primarily, slow execution with large training data sets – the proposed gesture recognition system uses clustering ensemble to reduce the training data set without significant deterioration of the quality of training. The reduction of training data set is efficiently performed using three types of clustering algorithms, yielding small number of input vectors that represent the original population very well.

The proposed methodology is capable of providing efficient recognition of simple and complex static gestures and was also successfully tested with gestures of an unseen user, i.e. person who took no part in the training phase.

The hand gesture recognition system based on the proposed methodology enables the use of affordable data gloves with a small number of sensors in VR engineering applications which require complex static gestures, including assembly and maintenance simulations.

According to literature, there are no similar solutions that allow efficient recognition of simple and complex static hand gestures, based on a 5-sensor data glove.

Strain sensors have been widely used to measure the strain of the structure. However, the ordinary sensing elements are not suitable for measuring large deformation on an irregular surface, which limits their applications. Recently, flexible sensors have attracted extensive interest because they can overcome the shortage of the ordinary sensing elements. The paper aims to discuss this issue.

In this paper, the whole measurement process of strain sensing behavior and the dimension design of fle3xible strain sensing system use the macroscopic measurement method of material tensile test to accurately measure the resistance change with strain. Afterwards, combining electrical components, the flexible strain sensors are produced for two biomedical applications: the wearable data-collecting gloves and rehabilitation training system.

The results show that the developed conductive fabric can exhibit high sensitivity, large workable strain range (>50 percent) under simple and repeated tension and good stability. Both applications demonstrate that the polypyrrole-coated fabric sensor can successfully measure the large and repeat strain, capture the motion of body and display corresponding information almost in real time.

The limitation lies in the lack of a holistic strain sensing mechanism study, and the lack of a corresponding theoretical model to explain the experimental results.

This study aims to advance a behavioural approach towards understanding how managerial perception impacts the enactment of responses to risk management during the implementation of digital technologies in industrial operations and supply chains. The purpose is to investigate the influence of (digital) technology and task uncertainty on the risk perception of managers and how this impacts risk responses adopted by managers.

Following an exploratory theory elaboration approach, the authors collected more than 80 h of interview material from 53 expert interviews. These interviews were conducted with representatives of 46 German companies that have adopted digital technologies for different industrial applications within manufacturing, assembly and logistics processes.

The findings provide nuanced insights on how individual and combined sources of uncertainty (technology and task uncertainty) impact the perception of decision makers and the resulting managerial responses adopted. The authors uncover the important role played by the interaction between digital technology and human being in the context of industrial operations. The exploratory study shows that the joint collaboration between humans and technologies has negative implications for managerial risk responses regardless of positive or negative perception, and therefore, requires significant attention in future studies.

The empirical base for this study is limited to German companies (mainly small and medium size). Moreover, German culture can be characterised by a high uncertainty avoidance and this may also limit the generalizability of the findings.

Managers should critically revise their perception of different types of digital technologies and be aware of the impact of human-machine interaction. Thereby, they should investigate more systematic approaches of risk identification and assessment.

This paper focuses on the managerial risk responses in the context of digitalisation projects with practical insights of 53 expert interviews.

Cables are widely used, and they play a key role in complex electromechanical products such as vehicles, ships, aircraft and satellites. Cable design and assembly significantly impact the development cycle and assembly quality, which is be-coming a key element affecting the function of a product. However, there are various kinds of cables, with complex geo-metric configurations and a narrow assembly space, which can easily result in improper or missed assembly, an unreasonable layout or interference. Traditional serial design methods are inefficient and costly, and they cannot predict problems in installation and use. Based on physical modeling, computer-aided cable design and assembly can effectively solve these problems. This paper aims to address virtual assembly (VA) of flexible cables based on physical modeling.

Much research has focused recently on virtual design and assembly-process planning for cables. This paper systematically reviews the research progress and the current state of mechanical models, virtual design, assembly-process planning, collision detection and geometric configuration and proposes areas for further research.

In the first instance, the main research groups and typical systems are investigated, followed by extensive exploration of the major research issues. The latter can be reviewed from five perspectives: the current state of mechanical models, virtual design, assembly-process planning, collision detection and geometric configuration. Finally, the barriers that prevent successful application of VA are also discussed, and the future research directions are summarized.

This paper presents a comprehensive survey of the topics of VA of flexible cables based on physical modeling and investigates some new ideas and recent advances in the area.

Two of the emerging issues for the health‐care sector in the 1990s are occupational health and safety, and iatrogenic issues. Both of these issues are implicated in the use of pre‐powdered latex gloves. Hospital healthcare workers are exposed to latex in many ways: gloves, intravenous sets, ventilator circuits, dental products, resuscitation equipment, anaesthetic equipment. Post‐operative complications, delayed wound healing, scar formation, and the potential for misdiagnosis, in the presence of starch powder, have been well documented in the literature with the need for thorough glove rinsing prior to surgery. Another route for glove powder to enter wounds is through a barrier breach. For an institution to ensure it provides the most durable and effective barrier for health‐care worker protection and patient safety, knowledge is needed regarding the various factors which lead to glove barrier failure. The primary aim of the study was to evaluate the in‐use durability of the surgical gloves in current use against powder‐free gloves. Descriptive statistics were used to analyse the data, in addition a cost analysis was calculated. The results of this study demonstrated clinically important differences between existing glove products in terms of barrier quality.

The construction industry remains one of the most hazardous industries worldwide, with a higher number of fatalities and injuries each year. The safety and well-being of workers at a job site are paramount as they face both immediate and long-term risks such as falls and musculoskeletal disorders. To mitigate these dangers, sensor-based technologies have emerged as a crucial tool to promote the safety and well-being of workers on site. The implementation of real-time sensor data-driven monitoring tools can greatly benefit the construction industry by enabling the early identification and prevention of potential construction accidents. This study aims to explore the innovative method of prototype development regarding a safety monitoring system in the form of smart personal protective equipment (PPE) by taking advantage of the recent advances in wearable technology and cloud computing.

The proposed smart construction safety system has been meticulously crafted to seamlessly integrate with conventional safety gear, such as gloves and vests, to continuously monitor construction sites for potential hazards. This state-of-the-art system is primarily geared towards mitigating musculoskeletal disorders and preventing workers from inadvertently entering high-risk zones where falls or exposure to extreme temperatures could occur. The wearables were introduced through the proposed system in a non-intrusive manner where the safety vest and gloves were chosen as the base for the PPE as almost every construction worker would be required to wear them on site. Sensors were integrated into the PPE, and a smartphone application which is called SOTER was developed to view and interact with collected data. This study discusses the method and process of smart PPE system design and development process in software and hardware aspects.

This research study posits a smart system for PPE that utilises real-time sensor data collection to improve worksite safety and promote worker well-being. The study outlines the development process of a prototype that records crucial real-time data such as worker location, altitude, temperature and hand pressure while handling various construction objects. The collected data are automatically uploaded to a cloud service, allowing supervisors to monitor it through a user-friendly smartphone application. The worker tracking ability with the smart PPE can help to alleviate the identified issues by functioning as an active warning system to the construction safety management team. It is steadily evident that the proposed smart PPE system can be utilised by the respective industry practitioners to ensure the workers' safety and well-being at construction sites through monitoring of the workers with real-time sensor data.

The proposed smart PPE system assists in reducing the safety risks posed by hazardous environments as well as preventing a certain degree of musculoskeletal problems for workers. Ultimately, the current study unveils that the construction industry can utilise cloud computing services in conjunction with smart PPE to take advantage of the recent advances in novel technological avenues and bring construction safety management to a new level. The study significantly contributes to the prevailing knowledge of construction safety management in terms of applying sensor-based technologies in upskilling construction workers' safety in terms of real-time safety monitoring and safety knowledge sharing.

Gesture recognition plays an important role in many fields such as human–computer interaction, medical rehabilitation, virtual and augmented reality. Gesture recognition using wearable devices is a common and effective recognition method. This study aims to combine the inverse magnetostrictive effect and tunneling magnetoresistance effect and proposes a novel wearable sensing glove applied in the field of gesture recognition.

A magnetostrictive sensing glove with function of gesture recognition is proposed based on Fe-Ni alloy, tunneling magnetoresistive elements, Agilus30 base and square permanent magnets. The sensing glove consists of five sensing units to measure the bending angle of each finger joint. The optimal structure of the sensing units is determined through experimentation and simulation. The output voltage model of the sensing units is established, and the output characteristics of the sensing units are tested by the experimental platform. Fifteen gestures are selected for recognition, and the corresponding output voltages are collected to construct the data set and the data is processed using Back Propagation Neural Network.

The sensing units can detect the change in the bending angle of finger joints from 0 to 105 degrees and a maximum error of 4.69% between the experimental and theoretical values. The average recognition accuracy of Back Propagation Neural Network is 97.53% for 15 gestures.

The sensing glove can only recognize static gestures at present, and further research is still needed to recognize dynamic gestures.

A new approach to gesture recognition using wearable devices.

This study has a broad application prospect in the field of human–computer interaction.

The sensing glove can collect voltage signals under different gestures to realize the recognition of different gestures with good repeatability, which has a broad application prospect in the field of human–computer interaction.

States that at present wind tunnels are the most commonly used methods for obtaining data on aerofoils and investigating boundary layer phenomena in order to improve laminar flows. Looks at an alternative to wind tunnels – an unmanned aerial vehicle developed by the University of Glasgow – the GUAV‐1. Discusses the work undertaken by the GUAV‐1 as well as its intended uses and aspirations.

In this era of globalisation, as competition intensifies, providing quality products and services has become a competitive advantage and a need to ensure survival. The Six Sigma's problem-solving methodology DMAIC has been one of the several techniques used by organisations to improve the quality of their products and services. This paper aims to demonstrate the empirical application of Six Sigma and DMAIC to reduce product defects within a rubber gloves manufacturing organisation.

The paper follows the DMAIC methodology to systematically investigate the root cause of defects and provide a solution to reduce/eliminate them. In particular, the design of experiments, hypothesis testing and two-way analysis of variance techniques were combined to statistically determine whether two key process variables, oven's temperature and conveyor's speed, had an impact on the number of defects produced, as well as to define their optimum values needed to reduce/eliminate the defects.

The analysis from employing Six Sigma and DMAIC indicated that the oven's temperature and conveyor's speed influenced the amount of defective gloves produced. After optimising these two process variables, a reduction of about 50 per cent in the “leaking” gloves defect was achieved, which helped the organisation studied to reduce its defects per million opportunities from 195,095 to 83,750 and thus improve its sigma level from 2.4 to 2.9.

This paper can be used as a guiding reference for managers and engineers to undertake specific process improvement projects, in their organisations, similar to the one presented in this paper.

This study presents an industrial case which demonstrates how the application of Six Sigma and DMAIC can help manufacturing organisations to achieve quality improvements in their processes and thus contribute to their search for process excellence.