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The purpose of this study is to present a novel hybrid closed-loop control method together with its performance validation for the dexterous prosthetic hand.

The hybrid closed-loop control is composed of a high-level closed-loop control with the user in the closed loop and a low-level closed-loop control for the direct robot motion control. The authors construct the high-level control loop by using electromyography (EMG)-based human motion intent decoding and electrical stimulation (ES)-based sensory feedback. The human motion intent is decoded by a finite state machine, which can achieve both the patterned motion control and the proportional force control. The sensory feedback is in the form of transcutaneous electrical nerve stimulation (TENS) with spatial-frequency modulation. To suppress the TENS interfering noise, the authors propose biphasic TENS to concentrate the stimulation current and the variable step-size least mean square adaptive filter to cancel the noise. Eight subjects participated in the validation experiments, including pattern selection and egg grasping tasks, to investigate the feasibility of the hybrid closed-loop control in clinical use.

The proposed noise cancellation method largely reduces the ES noise artifacts in the EMG electrodes by 18.5 dB on average. Compared with the open-loop control, the proposed hybrid closed-loop control method significantly improves both the pattern selection efficiency and the egg grasping success rate, both in blind operating scenarios (improved by 1.86 s, p < 0.001, and 63.7 per cent, p < 0.001) or in common operating scenarios (improved by 0.49 s, p = 0.008, and 41.3 per cent, p < 0.001).

The proposed hybrid closed-loop control method can be implemented on a prosthetic hand to improve the operation efficiency and accuracy for fragile objects such as eggs.

The primary contribution is the proposal of the hybrid closed-loop control, the spatial-frequency modulation method for the sensory feedback and the noise cancellation method for the integrating of the myoelectric control and the ES-based sensory feedback.

This paper aims to describe the design of a multi-degree of freedom (DOF) prosthetic hand prototype implementing postural synergy mechanically, which is actuated by two motors via a transmission unit, and is controlled using surface electromyography (sEMG) signal.

First, an anthropomorphic robotic hand is designed to imitate the human hand. The robotic hand has 18 DOF, 12 of which are actively driven by Bowden cables. Next, a set of different grasp modes are performed on a “full actuation” robotic hand, and principal component analysis (PCA) method is used to extract the first two postural synergies. Then, they are used to design a differential pulley-based transmission unit using two independent inputs to drive 12 output tendons. Finally, two control signals extracted from six channels of sEMG signals are used to proportionally control the two motors for achieving hand posture synthesis.

Using a differential pulley-based mechanical transmission unit to implement the synthesis of the first two postural synergies can make the prosthetic hand achieve different grasps by two motors, such as power, precision and lateral grasps. It is also feasible to control this “two actuation” prosthetic hand by relating the two-dimensional sEMG inputs with the first two postural synergies.

Mechanical implantation of postural synergies reduces the number of independent actuators without sacrificing the prosthetic hand’s versatility and simplifies its controller. Two-dimensional control extracted from sEMG is mapped into the combination coefficients of postural synergy synthesis. It shows potential application in the practical prosthetic hand.

This paper aims to explore and review the potential of robotic rehabilitation as a treatment approach for Alzheimer’s disease (AD) and its impact on the health and quality of life of AD patients.

The present discourse endeavors to provide a comprehensive overview of extant scholarly inquiries that have examined the salience of inhibitory mechanisms vis-à-vis robotic interventions and their impact on patients with AD. Specifically, this review aims to explicate the contemporary state of affairs in this realm by furnishing a detailed explication of ongoing research endeavors. With the objective of elucidating the significance of inhibitory processes in robotic therapies for individuals with AD, this analysis offers a critical appraisal of extant literature that probes the intersection of cognitive mechanisms and assistive technologies. Through a meticulous analysis of diverse scholarly contributions, this review advances a nuanced understanding of the intricate interplay between inhibitory processes and robotic interventions in the context of AD.

According to the review papers, it appears that implementing robot-assisted rehabilitation can serve as a pragmatic and effective solution for enhancing the well-being and overall quality of life of patients and families engaged with AD. Besides, this new feature in the robotic area is anticipated to have a critical role in the success of this innovative approach.

Due to the nascent nature of this cutting-edge technology and the constrained configuration of the mechanized entity in question, further protracted analysis is imperative to ascertain the advantages and drawbacks of robotic rehabilitation vis-à-vis individuals afflicted with Alzheimer’s ailment.

The potential for robots to serve as indispensable assets in the provision of care for individuals afflicted with AD is significant; however, their efficacy and appropriateness for utilization by caregivers of AD patients must be subjected to further rigorous scrutiny.

This paper reviews the current robotic method and compares the current state of the art for the AD patient.

The EPOC neuroheadset is a commercially available device that allows game players to control a computer using their facial expressions or their thoughts. This paper aims to examine whether it has the potential to be used as an input for assistive technology (AT) devices.

Two experiments were conducted. In the first, 12 non‐impaired subjects used the neuroheadset to control a computer with their facial expressions. They also used a simple system of two head switches for comparison. In the second experiment, three non‐impaired subjects were trained to use the neuroheadset to control a computer with their thoughts.

In the first experiment, the neuroheadset was slower and less accurate than the head switches (p<0.05), and was also harder to use. It is unlikely to be preferred to existing methods of accessing AT for those that retain a small amount of head movement. In the second experiment, by the end of the week, all three subjects achieved accuracy rates greater than chance.

All subjects were non‐impaired, and the sample size in the second experiment was small. Further research should concentrate on the second experiment, using larger sample sizes and impaired subjects.

The EPOC neuroheadset is substantially cheaper than similar specialist devices, and has the potential to allow those with no voluntary muscle control to access AT with their thoughts.

The results of these two experiments show that the Emotiv EPOC neuroheadset can be used as an interface for non‐impaired users to transfer information to a computer, which could in turn be used to control AT.

The purpose of this paper is to present a five-fingered, multisensory prosthetic hand integrating both intuitive myoelectric control and sensory feedback.

The artificial hand’s palm has a three-arcuate configuration and the thumb can move along a cone surface, improving the resemblance with the biological hand. By using a coupling linkage mechanism, each finger is independently actuated by a direct current motor. Both torque and position sensors are embedded in the finger to sense the hand’s status and its interaction with the outer environment. The proposed human-in-the-loop control system consists of an internal motion control scheme and an external human–machine interface. The pattern recognition-based electromyography (EMG) control scheme is adopted to control the motion of the hand, and the transcutaneous electrical nerve stimulation (TENS) is utilized to feedback the hand’s sensory information to its user.

The hand prototype shows that it has an anthropomorphic appearance (85 per cent to an average human hand), low weight (420 g), great power (10 N on the fingertip) and eligible dexterity. Clinical evaluation of the prosthetic hand on transradial amputees also approves the hand design.

From a systematic view, the paper details the design concepts of the HIT–DLR prosthetic hand IV, especially on its appearance, mechanism, myoelectric control and sensory feedback.

This paper aims to develop a signal acquisition system of surface electromyography (sEMG) and use the characteristics of (sEMG) signal to interference action pattern.

This paper proposes a fusion method based on combining the coefficient of AR model and wavelet coefficient. It improves the recognition rate of the target action. To overcome the slow convergence speed and local optimum in standard BP network, the study presents a BP algorithm which combine with LM algorithm and PSO algorithm, and it improves the convergence speed and the recognition rate of the target action.

Experiments verify the effectiveness of the system from two aspects the target motion recognition rate and the corresponding reaction speed of the robotic system.

The study developed a signal acquisition system of sEMG and used the characteristics of (sEMG) signal to interference action pattern. The myoelectricity integral values are presented to determine the starting point and end point of target movement, which is more effective than using single sample point amplitude method.

Two-handed automobile steering at low vehicle speeds may lead to reduced steering ability at large steering wheel angles and shoulder injury at high steering wheel rates (SWRs). As a first step toward solving these problems, this study aims, firstly, to design a surface electromyography (sEMG) controlled steering assistance interface that enables hands-free steering wheel rotation and, secondly, to validate the effect of this rotation on path-following accuracy.

A total of 24 drivers used biceps brachii sEMG signals to control the steering assistance interface at a maximized SWR in three driving simulator scenarios: U-turn, 90º turn and 45º turn. For comparison, the scenarios were repeated with a slower SWR and a game steering wheel in place of the steering assistance interface. The path-following accuracy of the steering assistance interface would be validated if it was at least comparable to that of the game steering wheel.

Overall, the steering assistance interface with a maximized SWR was comparable to a game steering wheel. For the U-turn, 90º turn and 45º turn, the sEMG-based human–machine interface (HMI) had median lateral errors of 0.55, 0.3 and 0.2 m, respectively, whereas the game steering wheel, respectively, had median lateral errors of 0.7, 0.4 and 0.3 m. The higher accuracy of the sEMG-based HMI was statistically significant in the case of the U-turn.

Although production automobiles do not use sEMG-based HMIs, and few studies have proposed sEMG controlled steering, the results of the current study warrant further development of a sEMG-based HMI for an actual automobile.

The application of robots to industrial problems often requires grasping and manipulation of the work piece. The robot is able to perform a task adequately only when it is assigned proper tooling and adequate methods of grasping and handling work pieces. The design of such a task requires an in‐depth knowledge of several interrelated subjects including: gripper design, force, position, stiffness and compliance control and grasp configurations. In this paper, we review the research finding on these subjects in order to present in a concise manner, which can be easily accessed by the designers of robot task, the information reported by the researchers, and identify based on the review, future research directions in these areas.

The purpose of this paper is to study the innovative design of prosthetic hands now in production from a Scottish spin‐off company.

The novel features are described, followed by the details of the mechanical construction and the available grip patterns. The benefits of the modular design are explained, and the function and construction of a skin‐like covering are explored. The clinical network supporting the prosthesis is briefly outlined.

The design allows patients to achieve many functional and natural‐looking hand configurations from simple “open” and “close” signals. Miniature motors and gearboxes allow independent movement of each digit, and an integral microprocessor translates electric signals from the forearm to control the movements of the hand. Stall detection ensures that no finger exerts excessive force or wastes power. Artificial skin can be very realistic, with imitation fingernails, hairs and pores. The lifelike prosthesis helps the patient emotionally, socially and at work.

The paper shows how advances in motors, gearboxes, batteries and electronics have enabled a breakthrough in prosthetic design.

The purpose of this two-part paper is to illustrate how sensors impart robots with perceptive capabilities. This first part considers robots that interact with humans and which seek to mimic human intentions.

Following a short introduction, this paper first discusses the sensors used in robotic prosthetics. It then considers sensor applications in recently developed service, companion and assistive robots. The final section concerns the sensors used in collaborative robots, followed by brief concluding comments.

This shows that sensors play a vital role in imparting perceptive capabilities to robots which interact with people. They can interpret human intentions, control prosthetic limbs, monitor and map a robot’s environment, assist with navigation, ensure the safety of co-workers and even detect a person’s emotional state. They are based on a diversity of principles and technologies, including microelectromechanical system (MEMS)-based sensors for physical variables, myographic electrodes and electroencephalogram (EEG) sensors, lasers, infra-red and sonar systems and sophisticated cameras and imaging systems.

This provides a timely account of how sensors confer perceptive capabilities to the growing number of robots which interact directly with people.