Current Issue : October-December Volume : 2026 Issue Number : 4 Articles : 5 Articles
As soft interfaces become central to robotics, wearables, and human–machine interaction, a persistent challenge is to sense touch with high fidelity while keeping devices simple, robust, and negligible power requirement at the sensitive element. Herein, we report a soft mechanoluminescent (ML) tactile sensor converting force directly into light for imaging-based readout, integrating a thin, three-layer ML-skin with a CMOS module. Under mechanical stimulation, BaTiO3 inclusions intensify local piezoelectric fields to excite ZnS:Cu emitters, producing light without electrical bias, pixel wiring, or external illumination. This optical transduction provides intrinsic electrical isolation while enabling scalable, high-density spatial mapping, where resolution is defined by optics rather than electrode routing. Coupled to a 640 × 480, 30 Hz CMOS array, the ML-sensor achieves a sensitivity of 27.5 N− 1 , a 30 ms response time, ∼ 80 μm spatial resolution, and stable operation for over 8000 cycles. Furthermore, MLsensor enables real-time handwriting recognition and human–machine interaction, demonstrating its potential as a natural tactile interface. By merging force-to-light conversion with a minimal device stack and vision-native readout, this work outlines a pathway to energy-efficient, conformal touch interfaces scalable across next-generation soft electronics and interactive systems....
Mobile auditing has been increasingly recognized as a critical direction in the digital transformation of auditing practices. However, field auditing scenarios are constrained by limited device resources, sensitive data privacy requirements, unstable network conditions, and elevated cognitive load among auditors, all of which substantially hinder human–computer interaction efficiency and audit quality. To address these challenges, a multi-module collaborative optimization framework for human–computer interaction was proposed. Four core technologies were integrated into the framework: non-intrusive cognitive load quantification, lightweight on-device cognitive inference, dynamic user interface information density reconfiguration, and adaptive computation offloading under weak network conditions. Through this integration, end-to-end coordination was achieved, encompassing cognitive state awareness, adaptive interface adjustment, and computational task optimization. To validate the effectiveness of the proposed framework, controlled dual-task experiments were conducted, simulating both static and dynamic interference conditions commonly encountered in realworld field auditing. Performance comparisons between the optimized system and a baseline system demonstrated that cognitive load was significantly reduced, while interaction efficiency and task accuracy were markedly improved. Furthermore, stable system performance was maintained under dynamic interference conditions, alongside lightweight deployment and enhanced privacy preservation capabilities. The proposed approach provides a practical technical pathway for optimizing human–computer interaction in mobile professional productivity tools, enriches interdisciplinary research at the intersection of mobile computing and cognitive engineering, and offers substantial academic and engineering value....
Industry 5.0 places humans at the center of production systems, requiring technologies that integrate operators as active components while adapting dynamically to their physical and cognitive needs. Within this context, facilitating the learning of complex concepts becomes essential, particularly through intuitive and accessible approaches. The objective of this work is to develop a hands-on educational platform for the introduction to human–robot interaction, aligned with Sustainable Development Goal 4 (SDG4). The platform is designed to support the experiential learning of key aspects of collaboration between human and robots while simultaneously familiarizing students with practical elements, including programming, hardware implementation with microcontrollers and sensors, and the use of the Robot Operating System (ROS). The developed system is based on the use of inertial measurement units (IMUs) to capture kinematic signals, enabling real-time interaction with a collaborative robot. The platform supports both translational and orientation control, with a maximum latency of 0.3 s, ensuring responsive and effective human–robot interaction. The hands-on approach will allow students to interact directly with the test bench, putting previously learned theoretical concepts into practice, according to the principle of learn-by-doing....
With the development of human–computer interaction technology, non-contact interaction based on gaze tracking and facial movements has become a research hotspot. Traditional mouse-and-keyboard methods pose challenges for people with disabilities or limited hand movements, while existing gaze-tracking systems often rely on expensive hardware or lack sufficient accuracy. This paper designs and implements a real-time system using ordinary cameras, achieving natural, efficient interaction via multimodal input combination. The system uses an improved MobileNetV2 backbone to construct GazeTrackNet for gaze estimation. It adopts MediaPipe Face Mesh to detect facial landmarks. Meanwhile, it applies geometric feature analysis, including eye aspect ratio and mouth aspect ratio, to identify actions such as blinking and mouth opening. It adopts a hybrid control strategy that combines gaze jumping and head fine-tuning, using mouth state as the main control switch. Key contributions include a lightweight gaze-tracking algorithm that enables stable and efficient gaze detection on consumer-grade hardware, a multimodal interaction strategy based on facial movement that improves system stability and ease of use, and a complete prototype system that achieves real-time performance on standard laptops. Experimental results show an average gaze average angle error of 3.0◦, 97% eye state recognition accuracy, and end-to-end latency below 70 ms. The system can satisfy the requirements of daily desktop interaction under normal indoor lighting, and shows potential for future barrierfree interaction applications after further validation with target users. Existing gazetracking methods either suffer from low precision on lightweight devices or bring heavy computational overhead. Common facial recognition approaches also face frequent false trigger interference. Compared with them, our scheme achieves balanced accuracy and real-time performance via an attention-enhanced structure, and the designed dual antishake mechanism effectively suppresses misjudgment, delivering a more stable hands-free interaction experience....
Head-mounted eye-tracking systems play a critical role in virtual reality, human–computer interaction, and clinical applications, yet achieving both high angular accuracy and precise 3D gaze position estimation with low-cost hardware remains challenging. This paper proposes a lightweight, training-free geometric 3D gaze tracking framework for binocular 3D gaze tracking using consumer-grade hardware, which leverages stereo geometric triangulation and a simplified physiological eye model to achieve robust 3D gaze estimation, requiring only standard infrared cameras and dichroic mirrors without additional specialized hardware. The method was evaluated in controlled indoor conditions with 30 participants, where it achieved an angular error ranging from 1.1◦ to 2.82◦ and a 3D gaze position error below 13.24 mm. Compared to two state-of-the-art academic nondeep- learning methods, the proposed framework delivers competitive angular accuracy while significantly reducing 3D position error, outperforming the baselines by 34% to 56% in depth estimation precision. These results demonstrates that the proposed geometric framework is a practical and effective solution for high-precision 3D gaze tracking on low-cost hardware, suitable for both research and consumer applications....
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