Current Issue : July-September Volume : 2026 Issue Number : 3 Articles : 5 Articles
This paper proposes a human–system coupling framework for understanding interactive environments in which embodied human activity is continuously translated into perceptual feedback through computational systems. Rather than conceptualizing interaction as a sequence of discrete commands, the framework interprets interactive systems as perceptual mediation environments linking bodily action, computational interpretation, and perceptual response. The framework is illustrated through the EchoCycle installation, which converts mechanical energy generated by cycling into real-time audiovisual feedback. Observations from the installation suggest that participants initially engage in exploratory behavior and gradually develop more stable activity patterns as they adapt to the feedback provided by the system. In shared interaction contexts, the perceptual environment reflects collective activity, creating conditions under which behavioral alignment among participants may emerge. By framing interactive systems as continuous perception–action loops, this study highlights how computational mediation can shape both individual adaptation and collective interaction dynamics. The proposed framework contributes to human– computer interaction and interactive system design by offering an integrated perspective on embodied action, perceptual feedback, and responsive environments....
Operational robots have demonstrated significant potential in complex scenarios such as live-line maintenance and medical surgery. Existing research on Mixed Reality (MR) and Digital Twin (DT) systems has primarily focused on unidirectional data visualization and passive state monitoring. Existing research on Mixed Reality (MR) and Digital Twin (DT) systems has primarily focused on unidirectional data visualization and passive state monitoring, acting as “open-loop” observation tools that fail to address low operational precision and inefficient human-robot synergy in dynamic, high-risk environments. For the first time, we integrate an MR-based closed-loop digital twin operating system for human-robot collaborative operation into the task execution of live-line operation equipment to address the above challenges. Moving beyond simple visualization, the proposed framework establishes an integrated operational paradigm that bridges the gap between immersive perception and real-time interventional control. This framework comprises three integral components: (1) the construction of a high-fidelity virtual digital twin; (2) the development of a human-computer interaction paradigm based on MR technology; and (3) the establishment of an MR-based human-machine collaborative operation mode. Building upon this framework, a system was implemented for live-line working robots. Experimental results indicate that, compared with traditional control methods, the proposed system reduces the task completion time of live-line equipment tasks by 14.3% on average, verifying the feasibility and effectiveness of the pioneering application of the closed-loop digital twin operating system in live-line operation equipment....
Digital twins are becoming essential tools in smart, human-centric manufacturing, yet validated approaches that integrate real human behavior into digital twin models remain limited. This study develops and experimentally validates a digital twin as a tool for evaluating human performance in balancing human–machine interaction. A physical system comprising a conveyor belt, sensors, and operator-controlled elements was constructed, and a functionally equivalent digital model was created using Arduino IDE and MATLAB/ Simulink. The digital twin records and synchronizes key human–machine interaction variables, including response time, assembly time, and execution consistency. Validation was conducted through simulation testing and an experimental study with 18 participants performing repeated assembly cycles. The results show that the developed digital twin accurately replicates the temporal dynamics of the physical process and reliably captures individual human performance patterns. Overall, the study provides a validated methodological framework for human–machine-integrated digital twins and demonstrates their potential for analyzing human–machine interaction, supporting operator training, and adaptive workplace design in line with Industry 5.0 principles....
We present GANimate, a lightweight method for animating talking faces that leverages recent advances in latent-space manipulation of Generative Adversarial Networks (GANs). Unlike existing approaches based on computationally intensive diffusion models, transformers, or complex 3DMM representations, which are impractical for mobile and other low-resource edge devices due to high memory and compute demands, GANimate is designed for efficient operation on low-memory, low-compute edge devices. The model operates on 2D lip landmarks extracted from standard mobile vision-sensor inputs and requires no pre-training, making it easily integrable with any lip-landmark generator. Through an optimization process in the GAN feature latent space, these landmarks act as geometric constraints to animate a static portrait, producing realistic and expressive lip movements. To maintain stability and visual coherence across frames, we employ a Kalman filter to detect and track lip landmarks during video synthesis, enabling adaptive refinement and improved temporal consistency. The result is a compact and modular framework that bridges the gap between performance and accessibility in talking face synthesis, delivering high-quality and stable animations with minimal computational overhead. GANimate represents an important step toward lifelike, real-time avatars suitable for sensor-enabled and mobile human–computer interaction....
Emotion recognition in human-robot interaction (HRI) is important in order to develop socially aware and responsive robotic systems. In this work, a near real-time emotion recognition architecture is introduced which combines deep AI models such as CNN, and recurrent architectures (LSTM/ GRU) for audio and visual-based emotion detection. The system is tested on the benchmark datasets like FER-2013 and RAVDESS. The goal is to provide a strong and scalable approach that can serve as a step toward the full integration of emotionally intelligent robots into everyday life, to encourage empathic, adaptive, and rewarding human-robot interaction. The proposed deep AI model was tested on a dataset comprising 5,000 multimodal samples of human emotional expressions collected in controlled and real-world human-robot interaction scenarios. The dataset included five emotional categories: Happy, Sad, Angry, Neutral, and Surprise. Experimental results show the effectiveness of the proposed system based on the public datasets, and its practical use in the simulated human-robot interaction (HRI) scenario. Also, the proposed approach provides high accuracy and low inference delay, which can support robotic agents to have effective emotion-adaptive behaviors in live-interaction environments....
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