Intuitive Communication Research Articles

Neuro-motor controlled wearable augmentations: current research and emerging trends.

Wearable augmentations (WAs) designed for movement and manipulation, such as exoskeletons and supernumerary robotic limbs, are used to enhance the physical abilities of healthy individuals and substitute or restore lost functionality for impaired individuals. Non-invasive neuro-motor (NM) technologies, including electroencephalography (EEG) and sufrace electromyography (sEMG), promise direct and intuitive communication between the brain and the WA. After presenting a historical perspective, this review proposes a conceptual model for NM-controlled WAs, analyzes key design aspects, such as hardware design, mounting methods, control paradigms, and sensory feedback, that have direct implications on the user experience, and in the long term, on the embodiment of WAs. The literature is surveyed and categorized into three main areas: hand WAs, upper body WAs, and lower body WAs. The review concludes by highlighting the primary findings, challenges, and trends in NM-controlled WAs. This review motivates researchers and practitioners to further explore and evaluate the development of WAs, ensuring a better quality of life.

Graphical methods to illustrate the nature of the relation between a continuous variable and the outcome when using restricted cubic splines with a Cox proportional hazards model.

Restricted cubic splines (RCS) allow analysts to model nonlinear relations between continuous covariates and the outcome in a regression model. When using RCS with the Cox proportional hazards model, there is no longer a single hazard ratio for the continuous variable. Instead, the hazard ratio depends on the values of the covariate for the two individuals being compared. Thus, using age as an example, when one assumes a linear relation between age and the log-hazard of the outcome there is a single hazard ratio comparing any two individuals whose age differs by 1 year. However, when allowing for a nonlinear relation between age and the log-hazard of the outcome, the hazard ratio comparing the hazard of the outcome between a 31- and a 30-year-old may differ from the hazard ratio comparing the hazard of the outcome between an 81- and an 80-year-old. We describe four methods to describe graphically the relation between a continuous variable and the outcome when using RCS with a Cox model. These graphical methods are based on plots of relative hazard ratios, cumulative incidence, hazards, and cumulative hazards against the continuous variable. Using a case study of patients presenting to hospital with heart failure and a series of mathematical derivations, we illustrate that the four methods will produce qualitatively similar conclusions about the nature of the relation between a continuous variable and the outcome. Use of these methods will allow for an intuitive communication of the nature of the relation between the variable and the outcome.

A Rapid Adaptation Approach for Dynamic Air-Writing Recognition Using Wearable Wristbands with Self-Supervised Contrastive Learning

HighlightsUtilizing self-supervised learning, the proposed wearable wristband with a four-channel sensing array and wireless transmission module is developed for tracking air-writing and dynamic gestures.The model can learn prior features from unlabeled signals of random wrist movements, significantly reducing the dependency on extensive labeled data for training.The wristband system rapidly adapts to multiple scenarios after fine-tuning using few-shot data, enhancing user interaction through natural and intuitive communication.

Zero-shot autonomous robot manipulation via natural language

Smart manufacturing revolutionizes advanced automation by leveraging deep learning and robotics technology. Nevertheless, deep learning implementation in manufacturing faces several challenges in preparing datasets, training a model, and adapting to various applications. Furthermore, programming languages are required to develop deep learning models and communicate with robots, which is not straightforward to quickly and precisely reflect human intentions in manufacturing processes. To alleviate the issue, this study proposes an autonomous zero-shot robot manipulation framework via natural language. Specifically, the Segmentation Anything Model (SAM), a promptable image segmentation model, first segments objects in images captured by an RGB-D camera, which are passed to Generative Pre-trained Transformers (GPTs), a Large Language Model (LLM), to identify and localize a designated object based on human commands given in natural language. Afterwards, the Robot Operating System (ROS) manipulates a robot to a localized position. With no additional programming or specialized skills in object localization and robot manipulation, the framework demonstrates an autonomous robot automation approach through intuitive communication between humans and machines through natural language.

Exploring the effect of human-drone communication modality on safety and balance control in virtual construction environments

This study examines the impact of Human-Drone Interaction (HDI) modalities on construction workers’ safety and balance control within virtual environments. Utilising virtual reality (VR) simulations, the study explored how gesture and speech-based communications influence workers’ physical postures and balance, contrasting these modalities with a non-interactive control group. One hundred participants were recruited, and their movements and balance control were tracked using motion sensors while they interacted with virtual drones through either gesture, speech, or without communication. Results showed that interactive modalities significantly improved balance control and reduced the risk of falls, suggesting that advanced HDI can enhance safety on construction sites. However, speech-based interaction increased cognitive workload, highlighting a trade-off between physical safety and mental strain. These findings underscore the potential of integrating intuitive communication methods into construction operations, although further research is needed to optimise these interactions for long-term use and in diverse noise environments.

Integrating augmented reality, gesture recognition, and NLP for enhancing underwater human-robot interaction

This paper presents an in-depth exploration into the integration of augmented reality (AR), gesture recognition, and natural language processing (NLP) to enhance human-robot interaction (HRI) within the context of underwater robotics. It highlights the significant potential these technologies hold in addressing the unique challenges faced in underwater environments, such as limited visibility, complex navigation, and the need for precise, intuitive communication between divers and robots. By reviewing current technological advancements and applications, the study underscores the critical role of AR in providing real-time visual feedback, gesture recognition in enabling more natural control mechanisms, and NLP in facilitating voice-driven commands and interactions. The research further discusses the development of a conceptual framework for an AR-based intuitive interface that synergizes gesture recognition and NLP, aiming to revolutionize underwater HRI by making it more efficient, safe, and user-friendly. Through this investigation, the paper seeks to contribute to the advancement of underwater robotics, proposing innovative solutions that could significantly improve human-robot collaboration in challenging aquatic missions.

What Affects Human Decision Making in Human–Robot Collaboration?: A Scoping Review

The advent of Industry 4.0 has heralded advancements in Human–robot Collaboration (HRC), necessitating a deeper understanding of the factors influencing human decision making within this domain. This scoping review examines the breadth of research conducted on HRC, with a particular focus on identifying factors that affect human decision making during collaborative tasks and finding potential solutions to improve human decision making. We conducted a comprehensive search across databases including Scopus, IEEE Xplore and ACM Digital Library, employing a snowballing technique to ensure the inclusion of all pertinent studies, and adopting the PRISMA Extension for Scoping Reviews (PRISMA-ScR) for the reviewing process. Some of the important aspects were identified: (i) studies’ design and setting; (ii) types of human–robot interaction, types of cobots and types of tasks; (iii) factors related to human decision making; and (iv) types of user interfaces for human–robot interaction. Results indicate that cognitive workload and user interface are key in influencing decision making in HRC. Future research should consider social dynamics and psychological safety, use mixed methods for deeper insights and consider diverse cobots and tasks to expand decision-making studies. Emerging XR technologies offer the potential to enhance interaction and thus improve decision making, underscoring the need for intuitive communication and human-centred design.

Breaking Barriers: Hand Gesture Vocalizer for the Deaf and Mute

This study addresses communication challenges faced by deaf and mute individuals by exploring the feasibility of utilizing flex sensors and the Random Forest algorithm for hand gesture vocalization. The background underscores the significance of accessible communication tools in enhancing the lives of those with hearing and speech impairments. The study's purpose is to assess the effectiveness of flex sensors in detecting hand gestures and the Random Forest algorithm's potential to generate vocalized speech corresponding to these gestures. The methodology involves data collection from flex sensors through Arduino, Random Forest model training, and accuracy evaluation in gesture recognition. Promising results indicate the model's high accuracy in classifying diverse hand gestures. The study emphasizes the technology-driven solution's importance in bridging communication gaps for those with impairments. Combining flex sensors and the Random Forest algorithm offers an intuitive communication tool, transforming interactions for deaf and mute individuals. Consideration for real-world scenarios and user diversity during system development is highlighted, crucial for practical accuracy. Beyond individual communication, the study's implications span education, employment, and social integration for people with disabilities. Implementing this technology in education fosters inclusive environments, empowering deaf and mute students to engage actively. The integration of flex sensors and the Random Forest algorithm holds immense promise, revolutionizing communication, and life quality. As an accessible gesture-based vocalization tool, it can reshape societal perspectives, fostering inclusivity and empathy. The study advocates continuous research and development, urging widespread technology adoption to create an inclusive society valuing diversity.

Investigation of virtual reality multimedia interaction technology based on wireless network

<p>In recent years, with the continuous development of wireless communication and virtual reality technology, multimedia interaction technology has received more and more attention. However, due to the limitations of bandwidth, delay, packet loss, and other problems in wireless networks, multimedia interaction technology also faces many challenges. In this paper, the virtual reality multimedia interaction technology will be studied in combination with a wireless network, in which the virtual environment is first constructed. Then, the interaction is carried out through data transmission and user interaction, and then the existing virtual reality multimedia interaction is optimized by using a data compression algorithm. In order to test the effectiveness and performance of virtual reality multimedia interaction under different network environments and determine the optimal network environment, this paper compares the multimedia interaction effect under three network environments using a wireless network, mobile network, and Bluetooth network as the research object, with download speed, loading speed, image quality, smoothness and real-time as the test variables, and 10 VR software as the constraints. The research results indicated that, under the same other conditions, taking real-time performance as an example, the delay time and feedback speed of wireless networks were between 26 ms–37 ms and 105 KB/s–115 KB/s. The delay time and feedback speed of mobile networks (3G/4G/5G, generation) were between 66 ms–75 ms and 46 KB/s–55 KB/s, while the delay time and feedback speed of Bluetooth networks were between 120 ms–130 ms and 25 KB/s–35 KB/s; this indicated that VR multimedia interaction technology in the wireless network had better performance. Virtual reality multimedia interaction technology based on wireless networks is a promising high technology that can bring users a more realistic, vivid, and intuitive communication and entertainment experience. At the same time, it will expand the application scenarios and promote the development and progress of the technology.</p>

An Innovative Guidotronic Bot with Integrated LiDAR and AI Voice Assistance

This research paper discusses the futuristic combination of LiDAR technology and Guidotronic Bot with AI Voice Assistance to improve robotic systems. AI Voice Assistance launches a new era of human-machine interaction. Today, intuitive communication and robot integration are crucial. This section discusses how AI Voice Assistance improves user experience and how people will soon be able to talk to robots in natural language. This will set the stage for Guidotronic Bot implementation discussions. This study shows the Guidotronic Bot, which can multitask without human assistance. We observe it and its effects in various situations. The integration method integrates LiDAR technology with the Guidotronic Bot's infrastructure to enable real-time mapping and vision. Accurate three-dimensional mapping from LiDAR sensors helps you avoid obstacles and navigate rough terrain. The integrated algorithm simplifies Guidotronic Bot, LiDAR sensor, and AI Voice Assistance system collaboration by combining sensors and making decisions. The bot uses AI algorithms to translate voice commands, use LiDAR for environmental data, and drive itself. This integration should improve navigation, awareness of surroundings, and voice control. This research expands robotic systems and prepares for future advances in human-robot interaction. Combining the Guidotronic Bot with LiDAR and AI Speech Recognition opens up new ways to navigate, interact with smart home devices, and work.

Enhancing Robotic Navigation in Dynamic Environments

Robotic navigation in dynamic environments represents a significant challenge and opportunity in the field of robotics, with profound implications for applications ranging from autonomous vehicles to service robots in humancentric spaces. This paper addresses the critical need for advanced navigation strategies that enable robots to operate efficiently and safely in environments characterized by continuous change and unpredictability. Traditional navigation methods, which rely heavily on static maps and predefined paths, often fall short in dynamic settings where obstacles and pathways can shift rapidly. Consequently, there is a pressing need for innovative approaches that combine robust sensing, real-time data processing, and adaptive decision-making to enhance robotic navigation capabilities. To tackle these challenges, we propose a comprehensive framework that integrates several cutting-edge technologies and methodologies. At the core of our approach is the use of advanced sensor fusion techniques, which combine data from multiple sensors, including LiDAR, cameras, and ultrasonic sensors, to create a detailed and dynamic representation of the robot's surroundings. This multi-modal sensing approach ensures that the robot can detect and respond to changes in the environment with high accuracy and reliabiality. Complementing the sensor fusion process is the implementation of realtime data processing algorithms, powered by machine learning and artificial intelligence. These algorithms enable the robot to analyze vast amounts of sensory data on-the-fly, recognizing patterns, predicting potential obstacles, and making informed navigation decisions in real-time. Machine learning models are trained using extensive datasets that include a variety of dynamic scenarios, ensuring that the robot can generalize from past experiences and adapt to new situations effectively. A key component of our framework is the development of adaptive path planning algorithms that can dynamically adjust the robot's trajectory in response to environmental changes. These algorithms leverage techniques from both classical robotics and modern AI, including probabilistic roadmaps, rapidly-exploring random trees (RRT), and deep reinforcement learning. By continuously updating the robot's path based on real-time sensory input, these adaptive planning methods ensure that the robot can navigate efficiently and avoid collisions even in highly dynamic environments. In addition to the technical advancements, our framework also emphasizes the importance of human-robot interaction (HRI) in dynamic environments. Effective HRI mechanisms are crucial for scenarios where robots operate alongside humans, such as in healthcare, hospitality, and collaborative manufacturing. We incorporate natural language processing (NLP) and gesture recognition systems to facilitate intuitive communication between humans and robots, allowing for seamless cooperation and enhanced safety. To validate our proposed framework, we conducted extensive experiments in a variety of dynamic settings, including urban environments, industrial warehouses, and public spaces. The results demonstrate significant improvements in navigation efficiency, collision avoidance, and overall operational safety compared to traditional methods. Our framework not only enhances the robot's ability to navigate dynamic

Decoding Bilingual EEG Signals With Complex Semantics Using Adaptive Graph Attention Convolutional Network.

Decoding neural signals of silent reading with Brain-Computer Interface (BCI) techniques presents a fast and intuitive communication method for severely aphasia patients. Electroencephalogram (EEG) acquisition is convenient and easily wearable with high temporal resolution. However, existing EEG-based decoding units primarily concentrate on individual words due to their low signal-to-noise ratio, rendering them insufficient for facilitating daily communication. Decoding at the word level is less efficient than decoding at the phrase or sentence level. Furthermore, with the popularity of multilingualism, decoding EEG signals with complex semantics under multiple languages is highly urgent and necessary. To the best of our knowledge, there is currently no research on decoding EEG signals during silent reading of complex semantics, let alone decoding silent reading EEG signals with complex semantics for bilingualism. Moreover, the feasibility of decoding such signals remains to be investigated. In this work, we collect silent reading EEG signals of 9 English Phrases (EP), 7 English Sentences (ES), 10 Chinese Phrases (CP), and 7 Chinese Sentences (CS) from the subject within 26 days. We propose a novel Adaptive Graph Attention Convolution Network (AGACN) for classification. Experimental results demonstrate that our proposed method outperforms state-of-the-art methods, achieving the highest classification accuracy of 54.70%, 62.26%, 44.55%, and 57.14% for silent reading EEG signals of EP, ES, CP, and CS, respectively. Moreover, our results prove the feasibility of complex semantics EEG signal decoding. This work will aid aphasic patients in achieving regular communication while providing novel ideas for neural signal decoding research.

Enhancing Robotic Navigation in Dynamic Environments

Robotic navigation in dynamic environments represents a significant challenge and opportunity in the field of robotics, with profound implications for applications ranging from autonomous vehicles to service robots in humancentric spaces. This paper addresses the critical need for advanced navigation strategies that enable robots to operate efficiently and safely in environments characterized by continuous change and unpredictability. Traditional navigation methods, which rely heavily on static maps and predefined paths, often fall short in dynamic settings where obstacles and pathways can shift rapidly. Consequently, there is a pressing need for innovative approaches that combine robust sensing, real-time data processing, and adaptive decision-making to enhance robotic navigation capabilities. To tackle these challenges, we propose a comprehensive framework that integrates several cutting-edge technologies and methodologies. At the core of our approach is the use of advanced sensor fusion techniques, which combine data from multiple sensors, including LiDAR, cameras, and ultrasonic sensors, to create a detailed and dynamic representation of the robot's surroundings. This multi-modal sensing approach ensures that the robot can detect and respond to changes in the environment with high accuracy and reliabiality. Complementing the sensor fusion process is the implementation of realtime data processing algorithms, powered by machine learning and artificial intelligence. These algorithms enable the robot to analyze vast amounts of sensory data on-the-fly, recognizing patterns, predicting potential obstacles, and making informed navigation decisions in real-time. Machine learning models are trained using extensive datasets that include a variety of dynamic scenarios, ensuring that the robot can generalize from past experiences and adapt to new situations effectively. A key component of our framework is the development of adaptive path planning algorithms that can dynamically adjust the robot's trajectory in response to environmental changes. These algorithms leverage techniques from both classical robotics and modern AI, including probabilistic roadmaps, rapidly-exploring random trees (RRT), and deep reinforcement learning. By continuously updating the robot's path based on real-time sensory input, these adaptive planning methods ensure that the robot can navigate efficiently and avoid collisions even in highly dynamic environments. In addition to the technical advancements, our framework also emphasizes the importance of human-robot interaction (HRI) in dynamic environments. Effective HRI mechanisms are crucial for scenarios where robots operate alongside humans, such as in healthcare, hospitality, and collaborative manufacturing. We incorporate natural language processing (NLP) and gesture recognition systems to facilitate intuitive communication between humans and robots, allowing for seamless cooperation and enhanced safety. To validate our proposed framework, we conducted extensive experiments in a variety of dynamic settings, including urban environments, industrial warehouses, and public spaces. The results demonstrate significant improvements in navigation efficiency, collision avoidance, and overall operational safety compared to traditional m ethods. Our framework not only enhances the robot's ability to navigate dynamic environments but also provides a scalable solution that can be tailored to different applications and operational requirements.

A Multi‐Module Sensing and Bi‐Directional HMI Integrating Interaction, Recognition, and Feedback for Intelligent Robots

AbstractRapid advances in robotics have placed urgent demands on more intelligent human‐machine interaction technologies. Specifically, the way of establishing dual‐way intuitive communication with a consistent sensory system can greatly enhance efficiency and reliability. Here, a bi‐directional human‐machine interface (HMI) is designed by applying starch‐based hydrogel sensors. The whole system consists of a multi‐modal wearable sensory exoskeleton with a haptic feedback module and sensory robotic hand. The sensory exoskeleton with strain‐sensing glove and rotation‐sensing arm can capture and project the motion of the entire upper limb. The system offers object recognition functions by utilizing a sensing array on the robotic hands and machine learning algorithms, which can identify the shape and hardness information. The recognized results can be delivered back to the operator via vision and vibrational haptic feedback, respectively. This dual‐way intelligent sensory system shows potential application in many key fields such as the Internet of Things, teleoperation, and medical robotics.

SDViT: Stacking of Distilled Vision Transformers for Hand Gesture Recognition

Hand gesture recognition (HGR) is a rapidly evolving field with the potential to revolutionize human–computer interactions by enabling machines to interpret and understand human gestures for intuitive communication and control. However, HGR faces challenges such as the high similarity of hand gestures, real-time performance, and model generalization. To address these challenges, this paper proposes the stacking of distilled vision transformers, referred to as SDViT, for hand gesture recognition. An initially pretrained vision transformer (ViT) featuring a self-attention mechanism is introduced to effectively capture intricate connections among image patches, thereby enhancing its capability to handle the challenge of high similarity between hand gestures. Subsequently, knowledge distillation is proposed to compress the ViT model and improve model generalization. Multiple distilled ViTs are then stacked to achieve higher predictive performance and reduce overfitting. The proposed SDViT model achieves a promising performance on three benchmark datasets for hand gesture recognition: the American Sign Language (ASL) dataset, the ASL with digits dataset, and the National University of Singapore (NUS) hand gesture dataset. The accuracies achieved on these datasets are 100.00%, 99.60%, and 100.00%, respectively.

ANALISIS PENGGUNAAN OMOIYARI HYOUGEN DALAM FILM COQUELICOT-ZAKA KARA KARYA SUTRADARA GOROU MIYAZAKI

Omoiyari hyougen is an expression of caution in speaking and paying attention to the interlocutor. However, there are still many people, especially Japanese language learners in Indonesia who do not really understand the importance of the purpose of omoiyari hyougen when talking with other speakers, especially with Japanese citizens. This study aims to describe the purpose of using omoiyari hyougen in the Coquelicot-Zaka Kara anime. This type of research is qualitative research with descriptive analysis method and data collection using listening technique, followed by free engagement speaking technique and note taking technique. The data of this research is utterances containing omoiyari hyougen and the data source used is the film Coquelicot-Zaka Kara. From the results of the study, it was found that there were 20 data of omoiyari hyougen which were divided into 5 objectives, namely: the goal of maintaining consensus was 1 data, optimizing the comfort of others as much as 6 data, social reciprocity as much as 11 data, intuitive communication as much as 1 data, and feelings of guilt as much as 1 data.

An Alternative Application to CHATGPT that Uses Reliable Sources to Enhance the Learning Process

Abstract ChatGPT is a type of language model that uses machine learning algorithms to generate responses to natural language input. Its advanced technology can revolutionize the way humans interact with machines by creating more natural and intuitive communication. However, the accuracy of ChatGPT’s responses may be compromised by biased or inaccurate data, which highlights the importance of carefully evaluating its output. Furthermore, early versions of ChatGPT were found to produce academic papers with missing references, which may compromise the credibility of research in the academic publishing industry. This underscores the need to establish regulations and guidelines that ensure the ethical and transparent use of these technologies, and to avoid relying solely on automated tools like ChatGPT without thoroughly reviewing the literature. In order to minimise the potential for inaccurate information in the classroom, an intuitive application based on the AI language (OpenAI - also used by ChatGPT) has been implemented. The application has been designed in such a way that it does not use internet databases as a search source and the answers to the questions are based on pre-established documentation that has been checked/certified by the educational institution/professors. The developed application can be installed either locally on a personal computer or on a file server/online library etc. and does not require access to a reliable Internet connection as in the case of ChatGPT.

Analysis of implicit robot control methods for joint task execution

Body language is an essential component of communication. The amount of unspoken information it transmits during interpersonal interactions is an invaluable complement to simple speech and makes the process smoother and more sustainable. On the contrary, existing approaches to human–machine collaboration and communication are not as intuitive. This is an issue that needs to be addressed if we aim to continue using artificial intelligence and machines to increase our cognitive or even physical capabilities. In this study, we analyse the potential of an intuitive communication method between biological and artificial agents, based on machines understanding and learning the subtle unspoken and involuntary cues found in human motion during the interaction process. Our work was divided into two stages: the first, analysing whether a machine using these implicit cues would produce the same positive effect as when they are manifested in interpersonal communication; the second, evaluating whether a machine could identify the cues manifested in human motion and learn (through the use of Long-Short Term Memory Networks) to associate them with the appropriate command intended from its user. Promising results were gathered, showing an improved work performance and reduced cognitive load on the user side when relying on the proposed method, hinting to the potential of more intuitive, human to human inspired, communication methods in human–machine interaction.

Evolving radiological protection guidelines for exploration-class missions

International Space Station partner nations have yet to agree on career radiation dose constraints. This is of increasing concern for collaborative mission planning beyond low-Earth orbit, since it is likely that one or two long-duration missions will expose crew to a cumulative dose that approaches or exceeds their current respective limits. As with radiological effects, the cumulative health impact of the numerous other injuries and illnesses documented during spaceflight is inherently heightened with longer and farther missions, say to the Moon and Mars. This paper summarizes the origin of existing radiological constraints employed by the Canadian Space Agency and explores how to build upon these protection practices to address the challenges associated with beyond low-Earth orbit missions. The discussion then leads into a review of conventional risk metrics currently under evaluation by space-faring nations to quantify risk of radiation-induced cancer mortality. This paper concludes with a proposal for the application of an existing burden of disease model termed the Disability Adjusted Life Year, to space exploration. This model can accommodate the many health hazards of spaceflight, including ionizing radiation, on a common scale. It has the potential to serve as an intuitive communication tool for informing on the impact of spaceflight on crew health.

Tell Me a Story! Blending Digital Storytelling Into Marketing Higher Education for Student Engagement

Multimodal learning via the use of smart devices, online social interactions, and intuitive communication platforms are fundamentally changing teaching and learning settings. Consequently, educators face unique challenges around student engagement as learners increasingly look to the use of technology-enabled activities for meaningful collaboration. Within this context, this research explores the role of digital storytelling in promoting perceived individual student and group engagement, and how these interact with group functioning, in a postgraduate marketing subject. We evaluate the implementation of this assessment as an avenue to optimize the benefits of the blended learning setting. It contributes to understanding by incorporating the concept of collective, or collaborative engagement where there is a scarcity of research despite the widespread application of group assessment in business education, and marketing education in particular. We employed canonical correlation analysis as an exploratory technique to gain initial insight into its efficacy for student engagement. We contend that digital storytelling shows promise as an inherently social and interactive learning task to provide an authentic assessment for a range of marketing problems. In turn, these attributes provide a stimulating vehicle for student engagement that can promote learning and satisfaction.