Search Results (96)

The application of artificial intelligence has significantly advanced interactive storytelling. However, current research has predominantly concentrated on the content generation capabilities of AI, primarily following a one-way ‘input-direct generation’ model. This has led to limited practicality in AI story writing, mainly due to the absence of investigations into user-driven creative processes. Consequently, users often perceive AI-generated suggestions as unhelpful and unsatisfactory. This study introduces a novel creative tool named Story Forge, which incorporates a card-based interactive narrative approach. By utilizing interactive story element cards, the tool facilitates the integration of narrative components with artificial intelligence-generated content to establish an interactive story writing framework. To evaluate the efficacy of Story Forge, two tests were conducted with a focus on user engagement, decision-making, narrative outcomes, the replay value of meta-narratives, and their impact on the users’ emotions and self-reflection. In the comparative assessment, the participants were randomly assigned to either the experimental group or the control group, in which they would use either a web-based AI story tool or Story Forge for story creation. Statistical analyses, including independent-sample t-tests, p-values, and effect size calculation (Cohen’s d), were employed to validate the effectiveness of the framework design. The findings suggest that Story Forge enhances users’ intuitive creativity, real-time story development, and emotional expression while empowering their creative autonomy. Full article

Motor Imagery (MI) classification plays a crucial role in enhancing the performance of brain–computer interface (BCI) systems, thereby enabling advanced neurorehabilitation and the development of intuitive brain-controlled technologies. However, MI classification using electroencephalography (EEG) is hindered by spatiotemporal variability and the limited interpretability of deep learning (DL) models. To mitigate these challenges, dropout techniques are employed as regularization strategies. Nevertheless, the removal of critical EEG channels, particularly those from the sensorimotor cortex, can result in substantial spatial information loss, especially under limited training data conditions. This issue, compounded by high EEG variability in subjects with poor performance, hinders generalization and reduces the interpretability and clinical trust in MI-based BCI systems. This study proposes a novel framework integrating channel dropout—a variant of Monte Carlo dropout (MCD)—with class activation maps (CAMs) to enhance robustness and interpretability in MI classification. This integration represents a significant step forward by offering, for the first time, a dedicated solution to concurrently mitigate spatiotemporal uncertainty and provide fine-grained neurophysiologically relevant interpretability in motor imagery classification, particularly demonstrating refined spatial attention in challenging low-performing subjects. We evaluate three DL architectures (ShallowConvNet, EEGNet, TCNet Fusion) on a 52-subject MI-EEG dataset, applying channel dropout to simulate structural variability and LayerCAM to visualize spatiotemporal patterns. Results demonstrate that among the three evaluated deep learning models for MI-EEG classification, TCNet Fusion achieved the highest peak accuracy of 74.4% using 32 EEG channels. At the same time, ShallowConvNet recorded the lowest peak at 72.7%, indicating TCNet Fusion’s robustness in moderate-density montages. Incorporating MCD notably improved model consistency and classification accuracy, especially in low-performing subjects where baseline accuracies were below 70%; EEGNet and TCNet Fusion showed accuracy improvements of up to 10% compared to their non-MCD versions. Furthermore, LayerCAM visualizations enhanced with MCD transformed diffuse spatial activation patterns into more focused and interpretable topographies, aligning more closely with known motor-related brain regions and thereby boosting both interpretability and classification reliability across varying subject performance levels. Our approach offers a unified solution for uncertainty-aware, and interpretable MI classification. Full article

Although artificial intelligence technologies have significantly enhanced autonomous robots’ capabilities in perception, decision-making, and planning, their autonomy may still fail when faced with complex, dynamic, or unpredictable environments. Therefore, it is critical to enable users to take over robot control in real-time and efficiently through teleoperation. The lightweight, wearable myoelectric armband, due to its portability and environmental robustness, provides a natural human–robot gesture interaction interface. However, current myoelectric teleoperation gesture control faces two major challenges: (1) poor intuitiveness due to visual-motor misalignment; and (2) low efficiency from discrete, single-degree-of-freedom control modes. To address these challenges, this study proposes an integrated myoelectric teleoperation interface. The interface integrates the following: (1) a novel hybrid reference frame aimed at effectively mitigating visual-motor misalignment; and (2) a finite state machine (FSM)-based control logic designed to enhance control efficiency and smoothness. Four experimental tasks were designed using different end-effectors (gripper/dexterous hand) and camera viewpoints (front/side view). Compared to benchmark methods, the proposed interface demonstrates significant advantages in task completion time, movement path efficiency, and subjective workload. This work demonstrates the potential of the proposed interface to significantly advance the practical application of wearable myoelectric sensors in human–robot interaction. Full article

Human–computer interaction (HCI) plays a crucial role across various fields, with eye-tracking technology emerging as a key enabler for intuitive and dynamic control in assistive systems like Assistive Robotic Arms (ARAs). By precisely tracking eye movements, this technology allows for more natural user interaction. However, current systems primarily rely on the single gaze-dependent interaction method, which leads to the “Midas Touch” problem. This highlights the need for real-time eye movement classification in dynamic interactions to ensure accurate and efficient control. This paper proposes a novel Gaussian Mixture Model–Hidden Markov Model (GMM-HMM) classification algorithm aimed at overcoming the limitations of traditional methods in dynamic human–robot interactions. By incorporating sum of squared error (SSE)-based feature extraction and hierarchical training, the proposed algorithm achieves a classification accuracy of 94.39%, significantly outperforming existing approaches. Furthermore, it is integrated with a robotic arm system, enabling gaze trajectory-based dynamic path planning, which reduces the average path planning time to 2.97 milliseconds. The experimental results demonstrate the effectiveness of this approach, offering an efficient and intuitive solution for human–robot interaction in dynamic environments. This work provides a robust framework for future assistive robotic systems, improving interaction intuitiveness and efficiency in complex real-world scenarios. Full article

This paper introduces a novel multi-objective optimization framework for sustainable portfolio rebalancing under uncertainty. The model simultaneously targets return maximization, downside risk control, and liquidity preservation, addressing the complex trade-offs faced by investors in volatile markets. Unlike traditional static approaches, the framework allows for dynamic asset reallocation and explicitly incorporates nonlinear transaction costs, offering a more realistic representation of trading frictions. Key financial parameters—including expected returns, volatility, and liquidity—are modeled using interval arithmetic, enabling a flexible, distribution-free depiction of uncertainty. Risk is measured through semi-absolute deviation, providing a more intuitive and robust assessment of downside exposure compared to classical variance. A core innovation lies in the behavioral modeling of investor preferences, operationalized through three strategic configurations, pessimistic, optimistic, and mixed, implemented via convex combinations of interval bounds. The framework is empirically validated using a diversified cryptocurrency portfolio consisting of Bitcoin, Ethereum, Solana, and Binance Coin, observed over a six-month period. The simulation results confirm the model’s adaptability to shifting market conditions and investor sentiment, consistently generating stable and diversified allocations. Beyond its technical rigor, the proposed framework aligns with sustainability principles by enhancing portfolio resilience, minimizing systemic concentration risks, and supporting long-term decision-making in uncertain financial environments. Its integrated design makes it particularly suitable for modern asset management contexts that require flexibility, robustness, and alignment with responsible investment practices. Full article

Suicide is a major public health concern, and accurate risk assessment is essential for prevention. Slider-format questions offer a quick, intuitive, and accessible method to evaluate suicide-related dimensions. This study examines the reliability of slider-based items compared to standardized psychometric instruments when delivered via a mobile app. A total of 299 university students completed a digital self-report questionnaire using the AuxiliApp mobile platform. Participants answered validated scales assessing depression, psychological pain, suicidal ideation, anger, impulsivity, loneliness, and reasons for living, each presented in both traditional Likert and novel slider formats. Pearson correlations were used to evaluate the relationship between traditional and slider-based scores. All correlations were statistically significant (p < 0.001). Moderate correlations were found in most domains, including depression, psychological pain, suicidal ideation, loneliness, and key aspects of impulsivity and anger. Lower correlations appeared in subscales related to anger control and protective beliefs against suicide. Slider-based items demonstrated acceptable psychometric equivalence and concurrent validity compared to traditional scales. Their brevity and compatibility with mobile devices support their use in telehealth and digital mental health screening. While not a replacement for clinical evaluation, they may facilitate early detection and ongoing monitoring in at-risk populations. Full article

In this paper, we introduce novel techniques for distant object manipulation, named GazeRayHand and GazeRayHand2. The two techniques translate and rotate objects by using the gaze ray as a reference line and the hand position relative to the gaze ray. The GazeRayHand2 additionally supports gaze control for quick and long-distance object translation. We evaluate these techniques by comparing them with two other recent techniques: Gaze&Pinch and modified Gaze Beam Guided. In a user study, the results showed that GazeRayHand and GazeRayHand2 not only performed similarly to Gaze&Pinch, known for high performance, but also showed notable benefits. Both GazeRayHand and GazeRayHand2 significantly reduced unnecessary translation compared to other techniques and were rated highly by users for intuitiveness and ease of use. In contrast, Gaze&Pinch had an issue of requiring two hand interactions for rotation, causing inconvenience. The modified Gaze Beam Guided was the worst among the four techniques by compulsorily requiring unnecessary interaction. Full article

While Local Differential Privacy (LDP) offers strong privacy guarantees for IoT data collection, users often struggle to understand its implications and control their privacy settings. This paper presents a user-centric approach to implementing LDP in smart home environments, focusing on voice command privacy. We analyze privacy control patterns across major smart home platforms and propose a novel interface that translates complex LDP parameters into four intuitive privacy levels. The interface combines visual controls with concrete examples showing how privacy transformations affect voice commands. By mapping mathematical privacy parameters to user-friendly settings while maintaining theoretical guarantees, our approach explores making differential privacy more accessible in IoT environments. We validated our design through a usability study to understand its strengths in accessibility and key areas for refinement. Full article

Social robots in Ambient Assisted Living (AAL) environments offer a promising alternative for enhancing senior care by providing companionship and functional support. These robots can serve as intuitive interfaces to complex smart home systems, allowing seniors and caregivers to easily control their environment and access various assistance services through natural interactions. By combining the emotional engagement capabilities of social robots with the comprehensive monitoring and support features of AAL, this integrated approach can potentially improve the quality of life and independence of elderly individuals while alleviating the burden on human caregivers. This paper explores the integration of social robotics with ambient assisted living (AAL) technologies to enhance elderly care. We propose a novel framework where a social robot is the central orchestrator of an AAL environment, coordinating various smart devices and systems to provide comprehensive support for seniors. Our approach leverages the social robot’s ability to engage in natural interactions while managing the complex network of environmental and wearable sensors and actuators. In this paper, we focus on the technical aspects of our framework. A computational P2P notebook is used to customize the environment and run reactive services. Machine learning models can be included for real-time recognition of gestures, poses, and moods to support non-verbal communication. We describe scenarios to illustrate the utility and functionality of the framework and how the robot is used to orchestrate the AAL environment to contribute to the well-being and independence of elderly individuals. We also address the technical challenges and future directions for this integrated approach to elderly care. Full article

The Controller Area Network (CAN) facilitates efficient communication among vehicle components. While it ensures fast and reliable data transmission, its lightweight design makes it susceptible to data manipulation in the absence of security layers. To address these vulnerabilities, machine learning (ML)-based intrusion detection systems (IDS) have been developed and shown to be effective in identifying anomalous CAN traffic. However, these models often function as black boxes, offering limited transparency into their decision-making processes, which hinders trust in safety-critical environments. To overcome these limitations, this paper proposes a novel method that combines Local Interpretable Model-agnostic Explanations (LIME) with a vision-language model (VLM) to generate detailed textual interpretations of an ML-based CAN IDS. This integration mitigates the challenges of visual-only explanations in traditional XAI and enhances the intuitiveness of IDS outputs. By leveraging the multimodal reasoning capabilities of VLMs, the proposed method bridges the gap between visual and textual interpretability. The method supports both global and local explanations by analyzing feature importance with LIME and translating results into human-readable narratives via VLM. Experiments using a publicly available CAN intrusion detection dataset demonstrate that the proposed method provides coherent, text-based explanations, thereby improving interpretability and end-user trust. Full article

Clinical Decision Support Systems (CDSSs) have become indispensable in medical decision-making. The heterogeneity and vast volume of medical data require firm attention to data management and integration strategies. On the other hand, CDSS functionality must be enhanced through improved human–computer interaction (HCI) principles. This study investigates the bidirectional relationship between data management practices (specifically data entry management, data transformation, and data integration) and HCI principles within CDSSs. Through a novel framework and practical case studies, we demonstrate how high-quality data entry, driven by controlled workflows and automated technologies, is crucial for system usability and reliability. We explore the transformative positive impact of robust data management techniques, including standardization, normalization, and advanced integration solutions, on the HCI elements and overall system performance. Conversely, we illustrate how effective HCI design improves data quality by reducing cognitive load, minimizing errors, and fostering user engagement. The findings reveal a synergistic relationship between HCI and data science, providing actionable insights for designing intuitive and efficient CDSSs. This research bridges the gap between technical and human-centric approaches, advancing CDSS usability, decision accuracy, and clinician trust for better patient outcomes. Full article

The microservices architectural style has gained widespread adoption in recent years thanks to its ability to deliver high scalability and maintainability. However, the development process for microservices-based applications can be complex and challenging. Indeed, it often requires developers to manage a large number of distributed components with the burden of handling low-level, recurring needs, such as inter-service communication, brokering, event management, and data replication. In this article, we present Microhooks: a novel framework designed to streamline the development of microservices by allowing developers to focus on their business logic while declaratively expressing the so-called low-level needs. Based on the inversion of control and the materialized view patterns, among others, our framework automatically generates and injects the corresponding artifacts, leveraging 100% build time code introspection and instrumentation, as well as context building, for optimized runtime performance. We provide the first implementation for the Java world, supporting the most popular containers and brokers, and adhering to the standard Java/Jakarta Persistence API. From the user perspective, Microhooks exposes an intuitive, container-agnostic, broker-neutral, and ORM framework-independent API. Microhooks evaluation against state-of-the-art practices has demonstrated its effectiveness in drastically reducing code size and complexity, without incurring any considerable cost on performance. Based on such promising results, we believe that Microhooks has the potential to become an essential component of the microservices development ecosystem. Full article

Soft robotic exoskeletons have emerged as a transformative solution for rehabilitation and assistance, offering greater adaptability and comfort than rigid designs. Myoelectric control, based on electromyography (EMG) signals, plays a key role in enabling intuitive and adaptive interaction between the user and the exoskeleton. This review analyzes recent advancements in myoelectric control strategies, emphasizing their integration into soft robotic exoskeletons. Unlike previous studies, this work highlights the unique challenges posed by the deformability and compliance of soft structures, requiring novel approaches to motion intention estimation and control. Key contributions include critically evaluating machine learning-based motion prediction, model-free adaptive control methods, and real-time validation strategies to enhance rehabilitation outcomes. Additionally, we identify persistent challenges such as EMG signal variability, computational complexity, and the real-time adaptability of control algorithms, which limit clinical implementation. By interpreting recent trends, this review highlights the need for improved EMG acquisition techniques, robust adaptive control frameworks, and enhanced real-time learning to optimize human-exoskeleton interaction. Beyond summarizing the state of the art, this work provides an in-depth discussion of how myoelectric control can advance rehabilitation by ensuring more responsive and personalized exoskeleton assistance. Future research should focus on refining control schemes tailored to soft robotic architectures, ensuring seamless integration into rehabilitation protocols. This review is a foundation for developing intelligent soft exoskeletons that effectively support motor recovery and assistive applications. Full article

Background/Objectives: Individuals with transradial limb loss or absence often retain the ability to pro-supinate their forearm, but the traditional design of the prosthesis precludes this motion from being used for direct prosthesis control. Methods: A prosthetic arm was created for a single user that employed a novel split inner socket to allow pro-supination of the residuum to control a powered prosthetic wrist rotator. A total of 14 subjects (13 able-bodied subjects and one prosthesis user) performed the Refined Clothespin Relocation Test. The user performed the test with their own and a novel research prosthesis, which allowed independent hand and wrist function. Movements of limb segments were recorded using a motion capture system and an analysis of limb segment angles and compensatory motion was made. Results: The research prosthesis reduced compensation in the trunk and head and reduced pain in some joints, while the time to complete the test increased. Conclusions: This method has the potential to create additional intuitive control channels for transradial prostheses. Full article

The “Infinity” foot controller for controlling prosthetic arms has been improved in this paper in several ways, including a foot sleeve that enables barefoot use, an improved sensor-controller unit design, and a more intuitive control scheme that allows gradual control of finger actuation. Furthermore, the “Persistence Arm”, a novel transradial prosthetic arm prototype, is introduced. This below-the-elbow arm has a direct-drive wrist actuation system, a thumb design with two degrees of freedom, and carbon fiber tendons for actuating the four forefingers. The manufactured prototype arm and foot controller underwent various tests to verify their efficacy. Wireless transmission speed tests showed that the maximum time delay is less than 165 ms, giving almost instantaneous response from the arm to any user’s foot control signal. Gripping tests quantified the grip and pulling forces of the arm prototype as 2.8 and 12.7 kg, respectively. The arm successfully gripped various household items of different shapes, weights, and sizes. These results highlight the potential of foot control as an alternative prosthetic arm control method and the possibility of new 3D-printed prosthetic arm designs to replace costly prostheses in the market, which could potentially reduce the high rejection rates of upper limb prostheses. Full article