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This study aims to improve the muscle model control performance of a tendon-driven musculoskeletal system (TDMS) to overcome disadvantages such as multisegmentation and strong coupling. An adaptive network controller (ANC) with a disturbance observer is established to reduce the modeling error of the musculoskeletal model and improve its antidisturbance ability.

In contrast to other control technologies adopted for musculoskeletal humanoids, which use geometric relationships and antagonist inhibition control, this study develops a method comprising of three parts. (1) First, a simplified musculoskeletal model is constructed based on the Taylor expansion, mean value theorem and Lagrange–d’Alembert principle to complete the decoupling of the muscle model. (2) Next, for this simplified musculoskeletal model, an adaptive neuromuscular controller is designed to acquire the muscle-activation signal and realize stable tracking of the endpoint of the muscle-driven robot relative to the desired trajectory in the TDMS. For the ANC, an adaptive neural network controller with a disturbance observer is used to approximate dynamical uncertainties. (3) Using the Lyapunov method, uniform boundedness of the signals in the closed-loop system is proved. In addition, a tracking experiment is performed to validate the effectiveness of the adaptive neuromuscular controller.

The experimental results reveal that compared with other control technologies, the proposed design techniques can effectively improve control accuracy. Moreover, the proposed controller does not require extensive considerations of the geometric and antagonistic inhibition relationships, and it demonstrates anti-interference ability.

Musculoskeletal robots with humanoid structures have attracted considerable attention from numerous researchers owing to their potential to avoid danger for humans and the environment. The controller based on bio-muscle models has shown great performance in coordinating the redundant internal forces of TDMS. Therefore, adaptive controllers with disturbance observers are designed to improve the immunity of the system and thus directly regulate the internal forces between the bio-muscle models.

The purpose of this study is realizing human-like motions and performance through musculoskeletal robots and brain-inspired controllers. Human-inspired robotic systems, owing to their potential advantages in terms of flexibility, robustness and generality, have been widely recognized as a promising direction of next-generation robots.

In this paper, a deep forward neural network (DFNN) controller was proposed inspired by the neural mechanisms of equilibrium-point hypothesis (EPH) and musculoskeletal dynamics.

First, the neural mechanism of EPH in human was analyzed, providing the basis for the control scheme of the proposed method. Second, the effectiveness of proposed method was verified by demonstrating that equilibrium states can be reached under the constant activation signals. Finally, the performance was quantified according to the experimental results.

Based on the neural mechanism of EPH, a DFNN was crafted to simulate the process of activation signal generation in human motion control. Subsequently, a bio-inspired musculoskeletal robotic system was designed, and the high-precision target-reaching tasks were realized in human manner. The proposed methods provide a direction to realize the human-like motion in musculoskeletal robots.

The purpose of this paper is to design and develop a new robotic device for the rehabilitation of the upper limbs. The authors are focusing on a new symmetrical robot which can be used to rehabilitate the right upper limb and the left upper limb. The robotic arm can be automatically extended or reduced depending on the measurements of the patient's arm. The main idea is to integrate electrical stimulation into motor rehabilitation by robot. The goal is to provide automatic electrical stimulation based on muscle status during the rehabilitation process.

The developed robotic arm can be automatically extended or reduced depending on the measurements of the patient's arm. The system merges two rehabilitation strategies: motor rehabilitation and electrical stimulation. The goal is to take the advantages of both approaches. Electrical stimulation is often used for building muscle through endurance, resistance and strength exercises. However, in the proposed approach the electrical stimulation is used for recovery, relaxation and pain relief. In addition, the device includes an electromyography (EMG) muscle sensor that records muscle activity in real time. The control architecture provides the ability to automatically activate the appropriate stimulation mode based on the acquired EMG signal. The system software provides two modes for stimulation activation: the manual preset mode and the EMG driven mode. The program ensures traceability and provides the ability to issue a patient status monitoring report.

The developed robotic device is symmetrical and reconfigurable. The presented rehabilitation system includes a muscle stimulator associated with the robot to improve the quality of the rehabilitation process. The integration of neuromuscular electrical stimulation into the physical rehabilitation process offers effective rehabilitation sessions for neuromuscular recovery of the upper limb. A laboratory-made stimulator is developed to generate three modes of stimulation: pain relief, massage and relaxation. Through the control software interface, the physiotherapist can set the exercise movement parameters, define the stimulation mode and record the patient training in real time.

There are certain constraints when applying the proposed method, such as the sensitivity of the acquired EMG signals. This involves the use of professional equipment and mainly the implementation of sophisticated algorithms for signal extraction.

Functional electrical stimulation and robot-based motor rehabilitation are the most important technologies applied in post-stroke rehabilitation. The main objective of integrating robots into the rehabilitation process is to compensate for the functions lost in people with physical disabilities. The stimulation technique can be used for recovery, relaxation and drainage and pain relief. In this context, the idea is to integrate electrical stimulation into motor rehabilitation based on a robot to obtain the advantages of the two approaches to further improve the rehabilitation process. The introduction of this type of robot also makes it possible to develop new exciting assistance devices.

The proposed design is symmetrical, reconfigurable and light, covering all the joints of the upper limbs and their movements. In addition, the developed platform is inexpensive and a portable solution based on open source hardware platforms which opens the way to more extensions and developments. Electrical stimulation is often used to improve motor function and restore loss of function. However, the main objective behind the proposed stimulation in this paper is to recover after effort. The novelty of the proposed solution is to integrate the electrical stimulation powered by EMG in robotic rehabilitation.

For the robot-assisted upper limb rehabilitation training process of the elderly with damaged neuromuscular channels and hemiplegic patients, bioelectric signals are added to transform the traditional passive training mode into the active training mode.

This paper mainly builds a steady-state visual stimulation interface, an electroencephalography (EEG) signal processing platform and an exoskeleton robot verification platform. The target flashing stimulation blocks provide visual stimulation at the specified position according to the specified frequency and stimulate EEG signals of different frequency bands. The EEG signal-processing platform constructed in this paper removes the noise by using Butterworth band-pass filtering and common average reference filtering on the obtained signals. Further, the features are extracted to identify the volunteer’s active movement intention through the canonical correlation analysis (CCA) method. The classification results are transmitted to the upper limb exoskeleton robot control system, combined with the position and posture of the exoskeleton robot to control the joint motion of robot.

Through a large number of experimental studies, the average accuracy of offline recognition of motion intention recognition can reach 86.1%. The control strategy with a three-instruction judgment method reduces the average execution error rate of the entire control system to 6.75%. Online experiments verify the feasibility of the steady-state visual evoked potentials (SSVEP)-based rehabilitation system.

An EEG signal analysis method based on SSVEP is integrated into the control of an upper limb exoskeleton robot, transforming the traditional passive training mode into the active training mode. The device used to record EEG is of very low cost, which has the potential to promote the rehabilitation system for further widely applications.

The purpose of this paper is to develop a novel wearable rehabilitation robotic hand driven by Pneumatic Muscle‐Torsion Spring (PM‐TS) for finger therapy. PM has complex nonlinear dynamics, which makes PM modelling difficult. To realize high‐accurate tracking for the robotic hand, an Echo State Network (ESN)‐based PID adaptive controller is proposed, even though the plant model is unknown.

To drive a single joint of rehabilitation robotic hand, the paper proposes a new PM‐TS actuator comprising a Pneumatic Muscle (PM) and a Torsion Spring (TS). Based on the novel actuator, a wearable robotic hand is designed. By employing the model‐free approximation capability of ESN, the RLSESN based PID adaptive controller is presented for improving the trajectory tracking performance of the rehabilitation robotic hand. An ESN together with Recursive Least Square (RLS) is called a RLSESN, where the ESN output weight matrix is updated by the online RLS learning algorithm.

Practical experiments demonstrate the validity of the PM‐TS actuator and indicate that the performance of the RLSESN based PID adaptive controller is better than that of the conventional PID controller. In addition, they also verify the effectiveness of the proposed rehabilitation robotic hand.

A new PM‐TS actuator configuration that uses a PM and a torsion spring for bi‐directional movement of joint is presented. By utilizing the new PM‐TS actuator, a novel wearable rehabilitation robotic hand for finger therapy is designed. Based on the unknown plant model, the RLSESN_PID controller is proposed to attain satisfactory performance.

The purpose of this study is developing the minimum parameter learning law for the weight updating, which reduces the updating of neural network (NN) weight only at triggering instants and makes a trade-off between the estimation accuracy and triggering frequency such that the computing complexity can be decreased. Besides that, a novel “soft” method is first constructed for the control updating at the triggered instants, to reduce the chattering effect of discontinued renewal of control. Addressing to the proposed control and updating method, a novel dead-zone condition with variable boundary about the triggered control signal is derived to ensure the positivity of adjacent execution intervals.

In this paper, to achieve the motion tracking of manipulator with uncertainty of system dynamics and the communication constraints in the control-execution channel, an adaptive event-triggered controller with NN identification is constructed to improve the transmission efficiency of control on the premise of the guaranteed performance. In the proposed method, the NN with intermittent updating is proposed to perform the uncertain approximation with the saved computation, and the triggered mechanism is constructed to regulate the transportation of the signal in the channel of controller-to-actuator.

According to the impulsive Lyapunov function, it can be proved that all the signals are semi-global uniformly ultimately bounded, and the positivity of adjacent execution intervals is also guaranteed by the proposed method. In addition, the chattering effect of control updating at the jumping instants can be relieved by the proposed “soft” mechanism, such that the control accuracy and stability can be guaranteed. Experiments on the JACO2 real manipulator are carried out to verify the effectiveness of the proposed scheme.

To the best of the author’s knowledge, this study is firstly to propose a “soft” method to reduce the chattering effect caused by discontinuous updating. Addressing to the updating method designed above, a novel dead-zone condition with variable threshold and boundary is first constructed to ensure the positivity of execution intervals.

The purpose of this paper is the research of a novel gesture-based dual-robot collaborative interaction interface, which achieves the gesture recognition when both hands overlap. This paper designs a hybrid-sensor gesture recognition platform to detect the both-hand data for dual-robot control.

This paper uses a combination of Leap Motion and PrimeSense in the vertical direction, which detects both-hand data in real time. When there is occlusion between hands, each hand is detected by one of the sensors, and a quaternion-based algorithm is used to realize the conversion of two sensors corresponding to different coordinate systems. When there is no occlusion, the data are fused by a self-adaptive weight fusion algorithm. Then the collision detection algorithm is used to detect the collision between robots to ensure safety. Finally, the data are transmitted to the dual robots.

This interface is implemented on a dual-robot system consisting of two 6-DOF robots. The dual-robot cooperative experiment indicates that the proposed interface is feasible and effective, and it takes less time to operate and has higher interaction efficiency.

A novel gesture-based dual-robot collaborative interface is proposed. It overcomes the problem of gesture occlusion in two-hand interaction with low computational complexity and low equipment cost. The proposed interface can perform a long-term stable tracking of the two-hand gestures even if there is occlusion between the hands. Meanwhile, it reduces the number of hand reset to reduce the operation time. The proposed interface achieves a natural and safe interaction between the human and the dual robot.

Snake-inspired robots are of great significance in many fields because of their great adaptability to the environment. This paper aims to systematically illustrate the research progress of snake-inspired robots according to their application environments. It classifies snake-inspired robots according to the numbers of degrees of freedom in each joint and briefly describes the modeling and control of snake-inspired robots. Finally, the application fields and future development trends of snake-inspired robots are analyzed and discussed.

This paper summarizes the research progress of snake-inspired robots and clarifies the requirements of snake-inspired robots for self-adaptive environments and multi-functional tasks. By equipping various sensors and tool modules, snake-inspired robots are developed from fixed-point operation in a single environment to autonomous operation in an amphibious environment. Finally, it is pointed out that snake-inspired robots will be developed in terms of rigid and flexible deformable structure, long endurance and multi-function and intelligent autonomous control.

Inspired by the modular and reconfigurable concepts of biological snakes, snake-inspired robots are well adapted to unknown and changing environments. Therefore, snake-inspired robots will be widely used in industrial, military, medical, post-disaster search and rescue applications. Snake-inspired robots have become a hot research topic in the field of bionic robots.

This paper summarizes the research status of snake-inspired robots, which facilitates the reader to be a comprehensive and systematic understanding of the research progress of snake-inspired robots. This helps the reader to gain inspiration from biological perspectives.

This study aims to offer a comprehensive exploration of the potential and challenges associated with sensor fusion-based virtual reality (VR) applications in the context of enhanced physical training. The main objective is to identify key advancements in sensor fusion technology, evaluate its application in VR systems and understand its impact on physical training.

The research initiates by providing context to the physical training environment in today’s technology-driven world, followed by an in-depth overview of VR. This overview includes a concise discussion on the advancements in sensor fusion technology and its application in VR systems for physical training. A systematic review of literature then follows, examining VR’s application in various facets of physical training: from exercise, skill development and technique enhancement to injury prevention, rehabilitation and psychological preparation.

Sensor fusion-based VR presents tangible advantages in the sphere of physical training, offering immersive experiences that could redefine traditional training methodologies. While the advantages are evident in domains such as exercise optimization, skill acquisition and mental preparation, challenges persist. The current research suggests there is a need for further studies to address these limitations to fully harness VR’s potential in physical training.

The integration of sensor fusion technology with VR in the domain of physical training remains a rapidly evolving field. Highlighting the advancements and challenges, this review makes a significant contribution by addressing gaps in knowledge and offering directions for future research.

Dysarthria is a neuromotor speech disorder caused by neuromuscular disturbances that affect one or more articulators resulting in unintelligible speech. Though inter-phoneme articulatory variations are well captured by formant frequency-based acoustic features, these variations are expected to be much higher for dysarthric speakers than normal. These substantial variations can be well captured by placing sensors in appropriate articulatory position. This study focuses to determine a set of articulatory sensors and parameters in order to assess articulatory dysfunctions in dysarthric speech.

The current work aims to determine significant sensors and parameters associated using motion path and correlation analyzes on the TORGO database of dysarthric speech. Among eight informative sensor channels and six parameters per channel in positional data, the sensors such as tongue middle, back and tip, lower and upper lips and parameters (y, z, φ) are found to contribute significantly toward capturing the articulatory information. Acoustic and positional data analyzes are performed to validate these identified significant sensors. Furthermore, a convolutional neural network-based classifier is developed for both phone-and word-level classification of dysarthric speech using acoustic and positional data.

The average phone error rate is observed to be lower, up to 15.54% for positional data when compared with acoustic-only data. Further, word-level classification using a combination of both acoustic and positional information is performed to study that the positional data acquired using significant sensors will boost the performance of classification even for severe dysarthric speakers.

The proposed work shows that the significant sensors and parameters can be used to assess dysfunctions in dysarthric speech effectively. The articulatory sensor data helps in better assessment than the acoustic data even for severe dysarthric speakers.