The development of advanced autonomous driving applications is hindered by the complex temporal structure of sensory data, as well as by the limited computational and energy resources of their on-board systems. Currently, neuromorphic engineering is a rapidly growing field that aims to design information processing systems similar to the human brain by leveraging novel algorithms based on spiking neural

Deep neural networks (DNNs) are known for extracting useful information from large amounts of data. However, the representations learned in DNNs are typically hard to interpret, especially in dense layers. One crucial issue of the classical DNN model such as multilayer perceptron (MLP) is that neurons in the same layer of DNNs are conditionally independent of each other, which makes co-training and

Despite decades of research, muscle-based control of assistive devices (myocontrol) is still unreliable; for instance upper-limb prostheses, each year more and more dexterous and human-like, still provide hardly enough functionality to justify their cost and the effort required to use them. In order to try and close this gap, we propose to shift the goal of myocontrol from guessing intended movements

Upper-limb protheses are subject to high rates of abandonment. Prosthesis abandonment is related to a reduced sense of embodiment, the sense of self-location, agency, and ownership that humans feel in relation to their bodies and body parts. If a prosthesis does not evoke a sense of embodiment, users are less likely to view them as useful and integrated with their bodies. Currently, visual feedback

Lower-limb exoskeletons often use torque control to achieve easy manipulation of energy flow and ensure human safety. Accuracy of applied torque greatly affects system performance and therefore it is always of interest to be improved. Feed-forward type iterative learning as a compensation term for feedback control was proved effective in torque tracking of these devices with complicated dynamics during

In order to solve the problem of data abnormalities in traditional multi-modal heterogeneous big data detection algorithms and missing data, which leads to data modal confusion, a multi-view heterogeneous big data clustering algorithm based on Kmeans clustering is established. With heterogeneous data as the supporting background of big data, data analysis with the help of multiple views, determination

The study of students behavior analysis in class plays a key role in teaching and educational reforms which can help the university to find an effective way to improve students' learning efficiency and innovation ability. It is also one of the effective ways to cultivate innovative talents. The traditional behavior recognition methods have many disadvantages such as poor robustness and low efficiency

The ability to make accurate social inferences makes humans able to navigate and act in their social environment effortlessly. Converging evidence shows that motion is one of the most informative cues in shaping the perception of social interactions. However, the scarcity of parametrized generative models for the generation of highly-controlled stimuli has slowed down both the identification of the

This paper introduces a novel exoskeleton active walking assistance control framework based on frequency adaptive dynamics movement primitives (FADMPs). The FADMPs proposed in this paper is an online learning and prediction algorithm which is able to online estimate the fundamental frequency of human joint trajectory, learn the shape of joint trajectory and predict the future joint trajectory during

Connectionist and dynamic field models consist of a set of coupled first-order differential equations describing the evolution in time of different units. We compare three numerical methods for the integration of these equations: the Euler method, and two methods we introduce here: a modified version of the fourth-order Runge Kutta method, and one semi-analytical method. We apply them to solve a well-known

The study of human balance recovery strategies is important for human balance rehabilitation and humanoid robot balance control. To date, many efforts have been made to improve balance during quiet standing and walking motions. Arm usage (arm strategy) has been proposed to control the balance during walking motion in the literature. However, limited research exists on the contributions of the arm strategy

This paper is concerned with learning transferable forward models for push manipulation that can be applying to novel contexts and how to improve the quality of prediction when critical information is available. We propose to learn a parametric internal model for push interactions that, similar for humans, enables a robot to predict the outcome of a physical interaction even in novel contexts. Given

The fault safety monitoring of hydrogen sensors is very important for their practical application. The precondition of traditional machine learning (ML) methods for sensor fault diagnosis is that enough fault data with the same distribution and feature space under the same working environment must exist. However, widely used fault diagnosis methods are not suitable for real working environments because

The deep transfer learning aims at dealing with challenges in new tasks with insufficient samples. However, when it comes to few-shot learning scenario, due to the low diversity of several known training samples, they are prone to dominated by specificity, thus misleading to one-sidedness local features instead of the reliable global feature of actual categories they belonging to. To this end, contextual

Three-dimensional scanners have been widely applied in image-guided surgery (IGS) given its potential to solve the image-to-patient registration problem. How to perform a reliable calibration between a 3D scanner and an external tracker is especially important for these applications. This study proposes a novel method for calibrating the extrinsic parameters of a 3D scanner in the coordinate system

In recent years, it is a trend to integrate the ideas in game theory into the research of multi-robot system. In this paper, a team-competition model is proposed to solve a dynamic multi-robot task allocation problem. The allocation problem asks how to assign tasks to robots such that the most suitable robot is selected to execute the most appropriate task, which arises in many real-life applications

With the rapid development of artificial intelligence, Cybernetics and other High-tech subject technology, robots have been made and used in increasing fields, and have attracted growing research interests from different communities. The knowledge graph can act as the brain of a robot and provide intelligence, to support the interaction between the robot and the human beings. Although the large-scale

With the rapid popularity of agent technology, public opinion early warning agent is attracted wide attention. Furthermore, deep learning model can make agent more automatic and efficient. Therefore, for the agency of public opinion early warning task, the deep learning model is very suitable for completing the tasks such as popularity prediction or emergency outbreak.In this context, improving the

Neonatal infants communicate with us through cries. The infant cry signals have distinct patterns depends on the purpose of the cries. Pre-processing, Feature Extraction, and Feature Selection need expert attention and take much effort in audio signals in recent days. In Deep Learning Techniques, it automatically extracts and selects the most important features. For this, it requires an enormous amount

Wearable hand robots are becoming an attractive means in the facilitating of assistance with daily living and hand rehabilitation exercises for patients after stroke. Pattern recognition is a crucial step towards the development of wearable hand robots. Electromyography(EMG) is a commonly used biological signal for hand pattern recognition. However, the EMG based pattern recognition performance in

In a parallel development to traditional rigid rehabilitation robotic systems, cable-driven systems are becoming popular. The robowalk expander product uses passive elastic bands in the training of the lower limbs. However, a well-controlled assistance or resistance is desirable for effective walking relearning and muscle training. To achieve well-controlled force during locomotion training with the

Endowing robots with the ability to view the world the way humans do, to understand natural language and to learn novel semantic meanings when they are deployed in the physical world, is a compelling problem. Another significant aspect is linking language to action, in particular, utterances involving abstract words, in artificial agents. In this work, we propose a novel methodology, using a brain-inspired

Despite the appealing concept of central pattern generator (CPG)-based control for bipedal walking robots, there is currently no systematic methodology for the design of a CPG-based controller. To remedy this oversight, we attempted to apply the Tegotae approach, which is a Japanese concept describing how well a perceived reaction, i.e., sensory information, matches an expectation, i.e., an intended

Understanding human hand movement functionality is fundamental in neuroscience, robotics, prosthetics, and rehabilitation. People are used to investigate the movement functionality separately from qualitative or quantitative perspectives. However, it is still limited to provide an integral framework from both perspectives with a logical manner. In this paper, we provide a systematic framework to qualitatively

Brachial plexus injuries with multiple-root involvement lead to severe and long-lasting impairments in the functionality and appearance of the affected upper extremity. In cases, where biologic reconstruction of hand and arm function is not possible, bionic reconstruction may be considered as a viable clinical option. Bionic reconstruction, through a careful combination of surgical augmentation, amputation

One of the most exciting areas of robot is lower limb exoskeleton robot, which translate human’s limb movement intention by electroencephalography (EEG) or electromyography (EMG) signals into control commands that could operate robot. However, the existing detection methods of lower limb voluntary movement intention have an obstacle because its performance. In this paper, a homology characteristic

With the rapid development of social network, intelligent terminal and automatic positioning technology, location-based social network (LBSN) service has become an important and valuable application. Point of interest (POI) recommendation is an important content in LBSN, which aims to recommend new locations of interest for users. It can not only alleviate the information overload problem faced by

In this paper, a method of intelligent identification and data smooth processing of flying flexible joint pivoting center based on machine vision is proposed. The geometric center of the two markers attached to the flying body is located on a straight line at a certain angle to the center-line of the measured pivoting body, then continuous image sampling is carried out by industrial camera when the

The operation of a human-machine interface is increasingly often referred to as a two-learners problem, where both the human and the interface independently adapt their behavior based on shared information to improve joint performance over a specific task. Drawing inspiration from the field of body-machine interfaces, we take a different perspective and propose a framework for studying co-adaptation

This paper aims to review the current state of brain-to-brain interface (B2BI) technology and its potential. B2BIs function via a brain-computer interface (BCI) to read a sender’s brain activity and a computer-brain interface (CBI) to write a pattern to a receiving brain, transmitting information. We used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) to systematically

Mental workload is a neuroergonomic human factor, which is widely used in planning system's safety and areas like brain-machine interface (BMI), neurofeedback, and assistive technologies. Robotic prosthetics methodologies are employed for assisting hemiplegic patients in performing routine activities. Assistive technologies' design and operation are required to have an easy interface with the brain

Can a socially assistive robot support patients in cardiovascular rehabilitation? To answer this question, in collaboration with medical specialists, we designed and conducted a real-world long-term study, lasting 2.5 years, at the Fundacion Cardioinfantil-Instituto de Cardiologıa clinic (Bogota, Colombia) within the context of the outpatient phase of their cardiac rehabilitation programme. The study

Relation extraction is a popular subtask in natural language processing (NLP). In the task of entity relation joint extraction, overlapping entities and multi-type relation extraction in overlapping triplets remain a challenging problem. The classification of relations by sharing the same probability space will ignore the correlation information among multiple relations. A relational-adaptive entity

During every waking moment, we must engage with our environments, the people around us, the tools we use, and even our own bodies to perform actions and achieve our intentions. There is a spectrum of control that we have over our surroundings that spans the extremes from full to negligible. When the outcomes of our actions do not align with our goals, we have a tremendous capacity to displace blame

This paper developed a cable-driven 3-degree-of-freedom (DOF) wrist rehabilitation exoskeleton actuated by the distributed active semi-active (DASA) system. Compared with the conventional cable-driven robots, the workspace of this robot is increased greatly by adding the rotating compensation mechanism and by optimizing the distribution of the cable attachment points. In the meanwhile, the efficiency

Spinal reflex is essential to the robust locomotion of quadruped animals. To investigate the reflex mechanisms, we developed a quadruped robot platform that emulates the neuromuscular dynamics of animals. The leg is designed to be highly back-drivable, and four Hill-type muscles and neuronal pathways are simulated on each leg using software. By searching for the reflex circuit that contributes to the

A number of methods have been proposed for face reconstruction from single/multiple image(s). However, it is still a challenge to do reconstruction for limited number of wild images, in which there exists complex different imaging conditions, various face appearance and limited number of high-quality images. And most current mesh model based methods cannot generate high-quality face model because of

The active visual system comprises the visual cortices, cerebral attention networks, and oculomotor system. While fascinating in its own right, it is also an important model for sensorimotor networks in general. A prominent approach to studying this system is active inference—which assumes the brain makes use of an internal (generative) model to predict proprioceptive and visual input. This approach

Shared autonomy aims at combining robotic and human control in the execution of remote, teleoperated tasks. This cooperative interaction cannot be brought about without the robot first recognizing the current human intention in a fast and reliable way so that a suitable assisting plan can be quickly instantiated and executed. Eye movements have long been known to be highly predictive of the cognitive

This study is developed to explore the role of intelligent badminton training robot (IBTR) in the prevention of badminton player injuries based on the machine learning algorithm. An IBTR is designed from the perspectives of hardware and software systems, and the movements of the athletes are recognized and analysed with the hidden Markov model (HMM) in the machine learning. After the design is completed

Semi-supervised deep learning for the biomedical graph and advanced manufacturing graph is rapidly becoming an important topic in both academia and industry. Many existing types of research focus on semi-supervised link prediction and node classification, as well as the application of these methods in sustainable development and advanced manufacturing. To date, most manufacturing graph neural networks

Finding the underlying principles of social attention in humans seems to be essential for the design of the interaction between natural and artificial agents. Here, we focus on the computational modelling of gaze dynamics as exhibited by humans when perceiving socially relevant multimodal information. The audio-visual landscape of social interactions is distilled into a number of multimodal patches

The success of a robotic pick and place task depends on the success of the entire procedure: from the grasp planning phase, to the grasp establishment phase, then the lifting and moving phase, and finally the releasing and placing phase. Being able to detect and recover from grasping failures throughout the entire process is therefore a critical requirement for both the robotic manipulator and the

Exoskeletons are increasingly used in rehabilitation and daily life in patients with motor disorders after neurological injuries. In this paper, a realistic human knee exoskeleton model based on a physical system was generated, a human-machine system was created in a musculoskeletal modelling software, and human-machine interactions based on different assistive strategies were simulated. The developed

Occlusions, restricted field of view and limited resolution all constrain a robot's ability to sense its environment from a single observation. In these cases, the robot first needs to actively query multiple observations and accumulate information before it can complete a task. In this paper, we cast this problem of active vision as active inference, which states that an intelligent agent maintains

Gaze and language are major pillars in multimodal communication. Gaze is a non-verbal mechanism that conveys crucial social signals in face-to-face conversation. However, compared to language, gaze has been less studied as a communication modality. The purpose of the present study is twofold: (i) To investigate gaze direction (i.e., aversion and face gaze) and its relation to speech in a face-to-face

Numerical cognition is a fundamental component of human intelligence that has not been fully understood yet. Indeed, it is a subject of research in many disciplines, e.g. neuroscience, education, cognitive and developmental psychology, philosophy of mathematics, linguistics. In Artificial Intelligence, aspects of numerical cognition have been modelled through neural networks to replicate and analytically

In this paper, a semi-active exoskeleton used to reduce the muscle fatigue of the lower limb was designed and evaluated. Background: The advantages and disadvantages of assistive exoskeletons developed for industrial purposes were introduced. Simulation: The process of squatting was simulated in the AnyBody.7.1 software, the result showed that muscle activity of the gluteus maximus, rectus femoris

Aiming at the problems of slow convergence speed, poor global search ability, and unknown time-varying dynamic obstacles in path planning of ant colony algorithm in a dynamic environment, an improved ant colony optimization algorithm using time taboo strategy, namely time taboo ant colony algorithm (TTACO),is proposed. Use adaptive initial pheromone distribution, rollback strategy, pheromone preferential

Stroke patients often have difficulty completing motor tasks even after substantive rehabilitation. Poor recovery of motor function can often be linked to stroke-induced damage to motor pathways. However, stroke damage in pathways that impact effective integration of sensory feedback with motor control may represent an unappreciated obstacle to smooth motor coordination. In this study we investigated

Position sense refers to an aspect of proprioception crucial for motor control and learning. The onset of neurological diseases can damage such sensory afference, with consequent motor disorders dramatically reducing the associated recovery process. In regular clinical practice, assessment of proprioceptive deficits is run by means of clinical scales which do not provide quantitative measurements.

Background The Ommaya reservoir implantation technique allows for bypass of the blood-brain barrier. It can be continuously administered locally and be used to repeatedly flush the intracranial cavity to achieve the purpose of treatment. Accurate, fast, and minimally invasive placement of the drainage tube is essential during the Ommaya reservoir implantation technique, which can be achieved with the

In the 5G environment, the errors of industrial robots in recognizing, detecting, and positioning objects are serious; in addition, the recognition speed and accuracy of industrial robots are unsatisfactory. Therefore, in order to solve these problems, the image layers are convolved and pooled through the deep learning model of artificial intelligence, and the visual recognition system of industrial

Humans initially learn about objects through the sense of touch, in a process called haptic exploration. In this paper, we present a neural network model of this learning process. The model implements two key assumptions. The first is that haptic exploration can be thought of as a type of navigation, where the exploring hand plays the role of an autonomous agent, and the explored object is this agent's

Modelling is widely used in biomedical research to gain insights into pathophysiology and treatment of neurological disorders but existing models, such as animal models and computational models, are limited in generalisability to humans and are restricted in the scope of possible experiments. Robotics offers a potential complementary modelling platform, with advantages such as embodiment and physical

COVID-19 pandemic has affected the population worldwide, evidencing new challenges and opportunities for several kinds of emergent and existing technologies. Social Assistive Robotics could be a potential tool to support clinical care areas, promoting physical distancing, and reducing the contagion rate. In this context, this paper presents a long-term evaluation of a social robotic platform for gait

The trajectory tracking and control of incomplete mobile robots are explored to improve the accuracy of the trajectory tracking of the robot controller. First, the mathematical kinematics model of the nonholonomic mobile robot is studied. Then, the improved Backpropagation Neural Network (BPNN) is applied to the robot controller. On this basis, a mobile robot trajectory tracking controller combining