Search results

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.

Breathing resistance is the main factor that influences the wearing comfort of respirators. This paper aims to demonstrate the feasibility of using the gene expression programming (GEP) for the purpose of predicting subjective perceptions of breathing resistances of wearing respirators via surface electromyography (sEMG) and respiratory signals (RSP) sensors.

The authors developed a physiological signal monitoring system with a specific garment. The inputs included seven physical measures extracted from (RSP) and (sEMG) signals. The output was the subjective index of breathing resistances of wearing respirators derived from the category partitioning-100 scale with proven levels of reliability and validity. The prediction model was developed and validated using data collected from 30 subjects and 24 test combinations (12 respirator conditions × 2 motion conditions). The subjects evaluated 24 conditions of breathing resistances in repeated measures fashion.

The results show that the GEP model can provide good prediction performance (R² = 0.71, RMSE = 0.11). This study demonstrates that subjective perceptions of breathing resistance of wearing respirators on the human body can be predicted using the GEP via sEMG and RSP in real-time, at little cost, non-invasively and automatically.

This is the first paper suggesting that subjective perceptions of subjective breathing resistances can be predicted from sEMG and RSP sensors using a GEP model, which will remain helpful to the scientific community to start further human-centered research work and product development using wearable biosensors and evolutionary algorithms.

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.

This paper aims to present an application of design of experiments techniques to determine the optimized parameters of artificial neural networks (ANNs), which are used to estimate human force from Electromyogram (sEMG) signals for rehabilitation robotics. Physiotherapists believe, to make a precise therapeutic exercise, we need to design and perform therapeutic exercise base on patient muscle activity. Therefore, sEMG signals are the best tool for using in therapeutic robots because they are related to the muscle activity. Using sEMG signals as input for therapeutic robots need precise human force estimation from sEMG. Furthermore, the ANN estimator performance is highly dependent on the accuracy of the target date and setting parameters.

In the previous studies, the force data, which are collected from the force sensors or dynameters, has widely been used as target data in the training phase of learning ANN. However, force sensors or dynameters could measure only contact force. Therefore, the authors consider the contact force, limb’s dynamic and time in target data to increase the accuracy of target data.

There are plenty of algorithms that are used to obtain optimal ANN settings. However, to the best of our knowledge, they do not use regression analysis to model the effect of each parameter, as well as present the contribution percentage and significance level of the ANN parameters for force estimation.

In this paper, a new model to estimate the force from sEMG signals is presented. In this method, the sum of the limb’s dynamics and the contact force is used as target data in the training phase. To determine the limb’s dynamics, the patient’s body and the rehabilitation robot are modeled in OpenSim. Furthermore, in this paper, sEMG experimental data are collected and the ANN parameters based on an orthogonal array design table are regulated to train the ANN. Taguchi is used to find the optimal parameters settings. Next, analysis of variance technique is used to obtain significance level, as well as contribution percentage of each parameter, to optimize ANN’s modeling in human force estimation. The results indicate that the presented model can precisely estimate human force from sEMG signals.

This paper aims to avoid the precise modeling and controlling problems of rigid structures of hand recovery device, by proposing a hand rehabilitator based on flexible pneumatic actuator with its safety and adaptability.

The hand rehabilitator is designed based on a flexible pneumatic bending joint. The recovery training program for an injured finger is developed via forearm sEMG (surface electromyogram) sampling, analysis, classification and motion consciousness identification. Four typical movement models of the index finger and middle finger were defined and the corresponding sEMG signals were sampled. After simulation and comparative analysis, autoregressive (AR) model back propagation (BP) network was selected for sEMG analysis and hand recovery planning because of its best recognition performance. A verification test was designed and the results showed that the soft hand rehabilitator and recovery conception are feasible.

AR model BP network can identify the index finger and middle finger movement intention via an sEMG analysis. The developed flexible pneumatic hand rehabilitator is safe and suitable for finger recovering therapy.

Because of the limitation of experimental samples, the prototype rehabilitator of this work may lack generalizability for other situations. Therefore, for further study and application, systematic structure revising, experiments, data and training are necessary to improve the performance.

The paper includes implications for the development and application of a new style, safe and dexterous hand rehabilitator.

The paper tries a new approach to design a safe, flexible and easily controlled hand rehabilitator.

To provide guidelines for the development of textile electrode compression pants that collect reliable signals during surface electromyography (sEMG) measurements and maintain a comfortable level of pressure.

To increase skin adhesion, 12 textile electrode bands for biceps brachii were prepared according to a combination of variables, namely, the type of the textile electrode, the pressure level and the presence or absence of an electrolyte. The dry textile electrode adopted herein was developed in terms of the size and material of the contact area, and a new electrode design was proposed. After examining the optimal design conditions by measuring the sEMGs during isometric exercise of the biceps brachii, prototype pants were designed based on the design variables that gave the most promising evaluation results. The completed prototype pants were verified through isometric thigh muscle exercises.

It was confirmed that the textile electrode was capable of EMG measurement with an excellent signal quality. Upon considering the comfort of wearing the device and the cost efficiency of dry electrodes, prototype pants that adopted a fit relative to a light clothing pressure (i.e. thigh: 1.3–1.9 kPa), and combined both silicon and silver thread embroidery with a wide contact area for stability, were designed and their sEMG measurements were confirmed.

In this study, wearable clothing based on textile electrodes was developed to ensure a comfortable fit from the wearer's perspective, and a design method was proposed for the development of low-cost SmartWear electrodes and circuits.

Work-related low back disorders (LBDs) are prevalent among rebar workers although their causes remain uncertain. The purpose of this study is to examine the self-reported discomfort and spinal biomechanics (muscle activity and spinal kinematics) experienced by rebar workers.

In all, 20 healthy male participants performed simulated repetitive rebar lifting tasks with three different lifting weights, using either a stoop (n = 10) or a squat (n = 10) lifting posture, until subjective fatigue was reached. During these tasks, trunk muscle activity and spinal kinematics were recorded using surface electromyography and motion sensors, respectively.

A mixed-model, repeated measures analysis of variance revealed that an increase in lifting weight significantly increased lower back muscle activity at L3 level but decreased fatigue and time to fatigue (endurance time) (p < 0.05). Lifting postures had no significant effect on spinal biomechanics (p < 0.05). Test results revealed that lifting different weights causes disproportional loading upon muscles, which shortens the time to reach working endurance and increases the risk of developing LBDs among rebar workers.

Future research is required to: broaden the research scope to include other trades; investigate the effects of using assistive lifting devices to reduce manual handling risks posed; and develop automated human condition-based solutions to monitor trunk muscle activity and spinal kinematics.

This study fulfils an identified need to study laboratory-based simulated task conducted to investigate the risk of developing LBDs among rebar workers primarily caused by repetitive rebar lifting.

This paper aims to propose an innovative adaptive control method for lower-limb rehabilitation robots.

Despite carrying out various studies on the subject of rehabilitation robots, the flexibility and stability of the closed-loop control system is still a challenging problem. In the proposed method, surface electromyography (sEMG) and human force-based dual closed-loop control strategy is designed to adaptively control the rehabilitation robots. A motion analysis of human lower limbs is performed by using a wavelet neural network (WNN) to obtain the desired trajectory of patients. In the outer loop, the reference trajectory of the robot is modified by a variable impedance controller (VIC) on the basis of the sEMG and human force. Thenceforward, in the inner loop, a model reference adaptive controller with parameter updating laws based on the Lyapunov stability theory forces the rehabilitation robot to track the reference trajectory.

The experiment results confirm that the trajectory tracking error is efficiently decreased by the VIC and adaptively correct the reference trajectory synchronizing with the patients’ motion intention; the model reference controller is able to outstandingly force the rehabilitation robot to track the reference trajectory. The method proposed in this paper can better the functioning of the rehabilitation robot system and is expandable to other applications of the rehabilitation field.

The proposed approach is interesting for the design of an intelligent control of rehabilitation robots. The main contributions of this paper are: using a WNN to obtain the desired trajectory of patients based on sEMG signal, modifying the reference trajectory by the VIC and using model reference control to force rehabilitation robot to track the reference trajectory.

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.

The purpose of the paper is to present a three-dimensional model and analyze the internal link between surface potential distribution and the electrical activity of lumbar muscles with finite element method.

Finite element method.

The simulated results have shown that there is a significant difference of surface potential topography patterns between low back pain (LBP) patients and normal healthy control. The normal shows symmetrical in contrast with the asymmetrical LBP pattern.

It provides a new view to analyze lumbar muscle activity with finite element method, which has a potential clinical application on lumbar muscle function analysis and LBP rehabilitation assessment.