This article presents a rigorous investigation of the global dissipativity (GD) and quasi-synchronization (QS) of fractional-order complex-valued neural networks (FOCVNNs) with mixed delays and external disturbances. The considered mixed delays consist of both time-varying delays (TVDs) and infinite distributed delays (IDDs), which significantly complicate the dynamical analysis of fractional-order systems. To address this challenge, the fractional comparison principle is first extended to accommodate IDD. Based on this extension, a generalized fractional Halanay inequality is established, providing an effective analytical tool for fractional-order systems with mixed delays. By employing the proposed inequality, two sufficient conditions are derived to guarantee GD and QS of FOCVNNs with mixed delays and external disturbances under designed feedback control. Finally, the effectiveness and applicability of the theoretical analysis are demonstrated through two illustrative examples, including a standard numerical simulation and a secure communication scheme constructed from a fractional-order chaotic system with mixed delays.

The integration of teleoperating robots with the Internet of Things (IoT) presents opportunities in fields like remote healthcare and semi-mechanical control. However, the incompleteness of sensory feedback can lead to operator fatigue, and latency may result in danger at the execution end when manipulating sharp objects. This study introduces a closed-loop teleoperation system leveraging multisensory fusion, visual, and haptic feedback and neuromorphic perception to mitigate this issue. At the control end, real-time control is achieved through cellular modules (delay < 71ms and position tracking RMS < 7mm) and we integrate multimodal feedback to reduce operator fatigue, including visual, vibration, temperature, and electrical stimulation. Feedback from ten volunteers grasping four objects reveals a reduction in mental fatigue (42% on average) and physical fatigue (32% on average) and higher immersion. At the remote end, we use piezoresistive films and negative temperature coefficient sensors to detect force and temperature, respectively, and RGB-D cameras to capture images and depth information. Meanwhile, a neuromorphic perception system is deployed at the remote end to rapidly process unexpected events, achieving pain reflex (haptic enhanced to 500%) and slip detection (with a response time of 1 ms). These findings demonstrate the system's potential to improve user experience and operational efficiency in remote healthcare and industry.

Memristive associative learning has gained significant attention for its ability to mimic fundamental biological learning mechanisms while maintaining system simplicity. In this work, we introduce a high-order memristive associative learning framework with a biologically realistic structure. By utilizing memristors as synaptic modules and their state information to bridge different orders of associative learning, our design effectively establishes associations between multiple stimuli and replicates the transient nature of high-order associative learning. In Pavlov’s classical conditioning experiments, our design achieves a 230% improvement in learning efficiency compared to previous works, with memristor power consumption in the synaptic modules remaining below 11 μW. In large-scale image recognition tasks, we utilize a 20×20 memristor array to represent images, enabling the system to recognize and label test images with semantic information at 100% accuracy. This scalability across different tasks highlights the framework’s potential for a wide range of applications, offering enhanced learning efficiency for current memristor-based neuromorphic systems.

Fundus diseases are prevalent causes of visual impairment and blindness worldwide, particularly in regions with limited access to ophthalmologists for timely diagnosis. Current approaches to fundus disease diagnosis heavily rely on expert-annotated data and AI-assisted image analysis, offering advantages, such as improved accuracy and accessibility. However, the dependency on annotated data poses a significant challenge, especially in regions with limited resources. To address this challenge, we propose a label-free general framework based on self-supervised machine learning. We performed feature distillation on a large number of unlabeled fundus images and employed a linear classifier for the detection of different fundus diseases. In validation experiments on the public and external validation fundus data sets, our model surpassed existing supervised approaches, achieving a remarkable increase in the area under the curve (AUC) of 15.7%, and even outperformed individual human experts. Our approach offers a promising solution to the limitations of current diagnostic methods, enhancing the potential for early and accurate detection of fundus diseases in resource-constrained settings.

Emotion recognition, a process of automatic cognition of human emotions, has great potential to improve the degree of social intelligence. Among various recognition methods, emotion recognition based on touch event’s temporal and force information receives global interests. Although previous studies have shown promise in the field of keystroke-based emotion recognition, they are limited by the need for long-term text input and the lack of high-precision force sensing technology, hindering their real-time performance and wider applicability. To address this issue, in this article, a piezoelectric-based keystroke dynamic technique is presented for quick emotion detection. The nature of piezoelectric materials enables high-resolution force detection. Meanwhile, the data collecting procedure is highly simplified because only the password entry is needed. International Affective Digitized Sounds (IADS) are applied to elicit users’ emotions, and a pleasure-arousal-dominance (PAD) emotion scale is used to evaluate and label the degree of emotion induction. A random forest (RF)-based algorithm is used in order to reduce the training dataset and improve algorithm portability. Finally, an average recognition accuracy of 79.33% of four emotions (happiness, sadness, fear, and disgust) is experimentally achieved. The proposed technique improves the reliability and practicability of emotion recognition in realistic social systems.

Sensor-based rehabilitation physical training assessment methods have attracted significant attention in refined evaluation scenarios. A refined rehabilitation evaluation method combines the expertise of clinicians with advanced sensor-based technology to capture and analyze subtle movement variations often unobserved by traditional subjective methods. Current approaches center on either body postures or muscle strength, which lack more sophisticated analysis features of muscle activation and coordination, thereby hindering analysis efficacy in deep rehabilitation feature exploration. To address this issue, we present a multimodal network algorithm that integrates surface electromyography (sEMG) and stress distribution signals. The algorithm considers the physical knowledge a priori to interpret the current rehabilitation stage and efficiently handles temporal dynamics arising from diverse user profiles in an online setting. Besides, we verified the performance of this model using a learned-nonuse phenomenon assessment task in 24 subjects, achieving an accuracy of 94.7%. Our results surpass those of conventional feature-based, distance-based, and ensemble baseline models, highlighting the advantages of incorporating multimodal information rather than relying solely on unimodal data. Moreover, the proposed model presents a network design solution for rehabilitation physical training that requires deep bioinformatic features and can potentially assist real-time and home-based physical training work.

Human activity recognition (HAR) is now a powerful in-home fitness assistive technology. This article presents a wearables-based multisensor system (WMS), which not only supports conventional functionalities, such as motion evaluation based on multidimensional information about the user’s body (movement speed, angle, muscle states, etc.), but also provides advanced services, including assessing training fatigue and providing real time, elastic, and professional training advice. The proposed WMS is experimentally validated by yielding 90.11% accuracy of motion evaluation with 36% and 23% improvement of fitness effect on bicep girth and muscular endurance, indicating its feasibility to prompt the development of HAR in the in-home fitness training domain.

In recent years, human activity recognition (HAR) technologies in e-health have triggered broad interest. In literature, mainstream works focus on the body's spatial information (i.e. postures) which lacks the interpretation of key bioinformatics associated with movements, limiting the use in applications requiring comprehensively evaluating motion tasks' correctness. To address the issue, in this article, a Wearables-based Multi-column Neural Network (WMNN) for HAR based on multi-sensor fusion and deep learning is presented. Here, the Tai Chi Eight Methods were utilized as an example as in which both postures and muscle activity strengths are significant. The research work was validated by recruiting 14 subjects in total, and we experimentally show 96.9% and 92.5% accuracy for training and testing, for a total of 144 postures and corresponding muscle activities. The method is then provided with a human-machine interface (HMI), which returns users with motion suggestions (i.e. postures and muscle strength). The report demonstrates that the proposed HAR technique can enhance users' self-training efficiency, potentially promoting the development of the HAR area.

In recent years, assistive wearables technologies based on Internet of Things (IoT) platforms for amyotrophic lateral sclerosis (ALS) patients trigger broad interests. Nevertheless, the user privacy leakage issue, owing to the scene camera installed on wearables to analyze environmental information, hinders further success use for ALS patients. To address this issue, in this article, a smart human-environment interactive (HEI) environment, including eye motion detection, radio-frequency identification (RFID), and speech feedback techniques, under the IoT framework is presented. Here, the users’ intentions are first interpreted by the eye motion classification, and then the target smart devices are reported and desired operations are confirmed by the RFID and speech feedback system in a hand-shaking manner. A high average accuracy of 93.2% is experimentally achieved, demonstrating the feasibility of the proposed method in obtaining satisfying performance while avoiding potential privacy leakage.

The combination of teleoperating robots and the Internet of Things (IoT) could be employed in many areas, including remote nursing and semi-mechanical control. However, it is known that subjects can quickly encounter physical and mental fatigue, which can potentially lower accuracy in teleoperation. To address this issue, this article presents a closed-loop teleoperation system based on multisensory fusion, visual, and haptic feedback within the IoT framework. Various sensors for electromyography, inertial measurement unit, and mechanical hand control system are deployed to obtain body signals from participants, subsequently processed by artificial intelligence methods. Resistive sensors are installed at the robotic hand side to learn the contact force between the robot hand and the grasped object. To allow the user to understand the contact force levels, a haptic interface is equipped to provide three-level mechanical vibrations. To help users identify and trace different objects, a region convolution neural network for detecting 100 different categories of things is constructed. In addition, the real-time control (delay$\mathbf {< } 110$ms) is offered by a tri-layered IoT architecture. The feasibility of the proposed technique is validated by a grasping task carried out by 20 volunteers. During experiments, it is observed that merely 10.8 s is averagely needed for participants in finishing the task, with a high average success rate of 97%. Besides, NASA-TLX questionnaire and maximum voluntary contraction test report that users suffer light mental and physical fatigue when the proposed technique is used.

Visual display of information is an obvious requirement in today,s highly digital world, and constitutes a powerful means of conveying complex information. This stems from the ability of the human eye and brain to perceive and process vast quantities of data in parallel. The history of visualizing information can be traced to the ancient era, when our ancestors carved images on cave walls and monuments. Mosaic art form emerged in the 3rd millennium BC, using small pieces of glass, stone, or other materials in combination to display information. These pieces are similar to pixels in the modern electronic display. The electronic display has become the primary human-machine interface in most applications, ranging from mobile phones, tablets, laptops, and desktops to TVs, signage, and domestic electrical appliances, not to mention industrial and analytical equipment. In the meantime, user interaction with the display has progressed significantly. Through sophisticated hand gestures, the display has evolved to become a highly efficient information exchange device. While interactive displays are currently very popular in mobile electronic devices such as smartphones and tablets, the development of large-area, flexible electronics, offers great opportunities for interactive technologies on an even larger scale. Indeed technologies that were once considered science fiction are now becoming a reality; the transparent display and associated smart surface being a case in point. Examines the market for interactive displays as the next omnipresent technology.