Search

It is appreciated that digital health is increasing in interest as an important area for efficiently standardizing and developing health services in Ireland, and worldwide. However, digital health is still considered to be in its infancy and there is a need to understand important factors that will support the development and uniform uptake of these technologies, which embrace their utility and ensure data trustworthiness. This constituted the first study to identify themes believed to be relevant by respiratory care and digital health experts in the Republic of Ireland to help inform future decision-making among respiratory patients that may potentially facilitate engagement with and appropriate use of digital health innovation (DHI). The study explored and identified expert participant perceptions, beliefs, barriers, and cues to action that would inform content and future deployment of living labs in respiratory care for remote patient monitoring of people with respiratory diseases using DHI. The objective of this case study was to generate and evaluate appropriate data sets to inform the selection and future deployment of an ICT-enabling technology that will empower patients to manage their respiratory systems in real-time in a safe effective manner through remote consultation with health service providers. The co-creation of effective DHI for respiratory care will be informed by multi-actor stakeholder participation, such as through a Quintuple Helix Hub framework combining university-industry-government-healthcare-society engagements. Studies, such as this, will help bridge the interface between top-down digital health policies and bottom-up end-user engagements to ensure safe and effective use of health technology. In addition, it will address the need to reach a consensus on appropriate key performance indicators (KPIs) for effective uptake, implementation, standardization, and regulation of DHI.

Virtual reality (VR) has recently emerged as a platform that can be employed in the context of e-health applications. Even though the majority of VR-based applications focus on visual stimuli as the main content, audio also plays a very important role. If someone has an issue with auditory processing, the comprehension of auditory information is compromised. This condition reflects negatively on one’s quality of life, given the impact of audio perception on one’s ability to communicate effectively or to differentiate and ignore “noise”. This work aims to design a VR application that can be used to: (a) optimize multimodal VR experiences based on user trials (b) extract user data continuously throughout the experiment (c) evaluate a user’s auditory processing ability. To accomplish this goal, participants are required to localise a sound source in space in the presence of multiple listening conditions with simple and complex sound stimuli configurations. Data collected from users consists of physiological and objective metrics. The results of this study highlight the relationship between user behaviour (head movement, fixation points) and performance in the sound localisation task. This information can be used to design future applications with the purpose of training one’s auditory localisation ability. In addition, the evaluation compared the impact of using two interaction methods to perform this task: using a pointer or eye gaze to indicate the location of the target source. The findings from this study show statistically significant differences in terms of physiological response when subjects are exposed to different interaction methods, with greater immersion and performance for the pointer group.

Ever since its discovery, the applications of Shape Memory Alloys (SMA) can be found across a range of application domains, from structural design to medical technology. This is based upon the unique and inherent characteristics such as thermal Shape Memory Effect (SME) and Superelasticity (or Pseudoelasticity). While thermal SME is used for shape morphing applications wherein temperature change can govern the shape and dimension of the SMA, Superelasticity allows the alloy to withstand a comparatively very high magnitude of loads without undergoing plastic deformation at higher temperatures. These unique properties in wearables have revolutionized the field, and from fabrics to exoskeletons, SMA has found its place in robotics and cobotics. This review article focuses on the most recent research work in the field of SMA-based smart wearables paired with robotic applications for human-robot interaction. The literature is categorized based on SMA property incorporated and on actuator or sensor-based concept. Further, use-cases or conceptual frameworks for SMA fiber in fabric for ‘Smart Jacket’ and SMA springs in the shoe soles for ‘Smart Shoes’ are proposed. The conceptual frameworks are built upon existing technologies; however, their utility in a smart factory concept is emphasized, and algorithms to achieve the same are proposed. The integration of the two concepts with the Industrial Internet of Things (IIoT) is discussed, specifically regarding minimizing hazards for the worker/user in Industry 5.0. The article aims to propel a discussion regarding the multi-faceted applications of SMAs in human-robot interaction and Industry 5.0. Furthermore, the challenges and the limitations of the smart alloy and the technological barriers restricting the growth of SMA applications in the field of smart wearables are observed and elaborated.

One of the most promising technologies that is driving digitalization in several industries is Digital Twin (DT). DT refers to the digital replica or model of any physical object (physical twin). What differentiates DT from simulation and other digital or CAD models is the automatic bidirectional exchange of data between digital and physical twins in real-time. The benefits of implementing DT in any sector include reduced operational costs and time, increased productivity, better decision making, improved predictive/preventive maintenance, etc. As a result, its implementation is expected to grow exponentially in the coming decades as, with the advent of Industry 4.0, products and systems have become more intelligent, relaying on collection and storing incremental amounts of data. Connecting that data effectively to DTs can open up many new opportunities and this paper explores different industrial sectors where the implementation of DT is taking advantage of these opportunities and how these opportunities are taking the industry forward. The paper covers the applications of DT in 13 different industries including the manufacturing, agriculture, education, construction, medicine, and retail, along with the industrial use case in these industries.

Gait analysis is a technique that is used to understand movement patterns and, in some cases, to inform the development of rehabilitation protocols. Traditional rehabilitation approaches have relied on expert guided feedback in clinical settings. Such efforts require the presence of an expert to inform the re-training (to evaluate any improvement) and the patient to travel to the clinic. Nowadays, potential opportunities exist to employ the use of digitized "feedback" modalities to help a user to "understand" improved gait technique. This is important as clear and concise feedback can enhance the quality of rehabilitation and recovery. A critical requirement emerges to consider the quality of feedback from the user perspective i.e. how they process, understand and react to the feedback. In this context, this paper reports the results of a Quality of Experience (QoE) evaluation of two feedback modalities: Augmented Reality (AR) and Haptic, employed as part of an overall gait analysis system. The aim of the feedback is to reduce varus/valgus misalignments, which can cause serious orthopedics problems. The QoE analysis considers objective (improvement in knee alignment) and subjective (questionnaire responses) user metrics in 26 participants, as part of a within subject design. Participants answered 12 questions on QoE aspects such as utility, usability, interaction and immersion of the feedback modalities via post-test reporting. In addition, objective metrics of participant performance (angles and alignment) were also considered as indicators of the utility of each feedback modality. The findings show statistically significant higher QoE ratings for AR feedback. Also, the number of knee misalignments was reduced after users experienced AR feedback (35% improvement with AR feedback relative to baseline when compared to haptic). Gender analysis showed significant differences in performance for number of misalignments and time to correct valgus misalignment (for males when they experienced AR feedback). The female group self-reported higher utility and QoE ratings for AR when compared to male group.

Virtual reality (VR) technology is emerging as a tool for simulating different eating environments to better understand consumer sensory response to food. This research explored the impact of different environmental contexts on participants’ hedonic ratings of two different food products: beef steaks, and milk chocolate, using VR as the context-enhancing technology. Two separate studies were conducted. For beef, two different contextual conditions were compared: traditional sensory booths and a VR restaurant. For chocolate, data were generated under three different contextual conditions: traditional sensory booths, VR Irish countryside; VR busy city (Dublin, Ireland). All VR experiences were 360-degree video based. Consumer level of engagement in the different contextual settings was also investigated. The results showed that VR had a significant effect on participants’ hedonic responses to the food products. Beef was rated significantly higher in terms of liking for all sensory attributes when consumed in the VR restaurant. While for chocolate, the VR countryside context generated significantly higher hedonic scores for flavour and overall liking in comparison to the sensory booth. Taken together, both studies demonstrate how specific contextual settings can impact participants’ sensory response to food products, when compared to a traditional sensory laboratory condition.

Digital Twin (DT) refers to the virtual copy or model of any physical entity (physical twin) both of which are interconnected via exchange of data in real time. Conceptually, a DT mimics the state of its physical twin in real time and vice versa. Application of DT includes real-time monitoring, designing/planning, optimization, maintenance, remote access, etc. Its implementation is expected to grow exponentially in the coming decades. The advent of Industry 4.0 has brought complex industrial systems that are more autonomous, smart, and highly interconnected. These systems generate considerable amounts of data useful for several applications such as improving performance, predictive maintenance, training, etc. A sudden influx in the number of publications related to ‘Digital Twin’ has led to confusion between different terminologies related to the digitalization of industries. Another problem that has arisen due to the growing popularity of DT is a lack of consensus on the description of DT as well as so many different types of DT, which adds to the confusion. This paper intends to consolidate the different types of DT and different definitions of DT throughout the literature for easy identification of DT from the rest of the complimentary terms such as ‘product avatar’, ‘digital thread’, ‘digital model’, and ‘digital shadow’. The paper looks at the concept of DT since its inception to its predicted future to realize the value it can bring to certain sectors. Understanding the characteristics and types of DT while weighing its pros and cons is essential for any researcher, business, or sector before investing in the technology.

Whilst investigating student performance in design and arithmetic tasks, as well as during exams, electrodermal activity (EDA)-based sensors have been used in attempts to understand cognitive function and cognitive load. Limitations in the employed approaches include lack of capacity to mark events in the data, and to explain other variables relating to performance outcomes. This paper aims to address these limitations, and to support the utility of wearable EDA sensor technology in educational research settings. These aims are achieved through use of a bespoke time mapping software which identifies key events during task performance and by taking a novel approach to synthesizing EDA data from a qualitative behavioral perspective. A convergent mixed method design is presented whereby the associated implementation follows a two-phase approach. The first phase involves the collection of the required EDA and behavioral data. Phase two outlines a mixed method analysis with two approaches of synthesizing the EDA data with behavioral analyses. There is an optional third phase, which would involve the sequential collection of any additional data to support contextualizing or interpreting the EDA and behavioral data. The inclusion of this phase would turn the method into a complex sequential mixed method design. Through application of the convergent or complex sequential mixed method, valuable insight can be gained into the complexities of individual learning experiences and support clearer inferences being made on the factors relating to performance. These inferences can be used to inform task design and contribute to the improvement of the teaching and learning experience.