Performances of hill-type and neural network muscle models-toward a myosignal-based exoskeleton

BiLSTM-Based Joint Torque Prediction From Mechanomyogram During Isometric Contractions: A Proof of Concept Study

Quantifying muscle strength is an important measure in clinical settings; however, there is a lack of practical tools that can be deployed for routine assessment. The purpose of this study is to propose a deep learning model for ankle plantar flexion torque prediction from time-series mechanomyogram (MMG) signals recorded during isometric contractions (i.e., a similar form to manual muscle testing procedure in clinical practice) and to evaluate its performance. Four different deep learning models in terms of model architecture (based on a stacked bidirectional long short-term memory and dense layers) were designed with different combinations of the number of units (from 32 to 512) and dropout ratio (from 0.0 to 0.8), and then evaluated for prediction performance by conducting the leave-one-subject-out cross-validation method from the 10-subject dataset. As a result, the models explained more variance in the untrained test dataset as the error metrics (e.g., root-mean-square error) decreased and as the slope of the relationship between the measured and predicted joint torques became closer to 1.0. Although the slope estimates appear to be sensitive to an individual dataset, >70% of the variance in nine out of 10 datasets was explained by the optimal model. These results demonstrated the feasibility of the proposed model as a potential tool to quantify average joint torque during a sustained isometric contraction.

Exo Supportive Devices: Summary of Technical Aspects

Human societies have been trying to mitigate the suffering of individuals with physical impairments, with a special effort in the last century. In the 1950s, a new concept arose, finding similarities between animal exoskeletons, and with the goal of medically aiding human movement (for rehabilitation applications). There have been several studies on using exosuits with this purpose in mind. So, the current review offers a critical perspective and a detailed analysis of the steps and key decisions involved in the conception of an exoskeleton. Choices such as design aspects, base materials (structure), actuators (force and motion), energy sources (actuation), and control systems will be discussed, pointing out their advantages and disadvantages. Moreover, examples of exosuits (full-body, upper-body, and lower-body devices) will be presented and described, including their use cases and outcomes. The future of exoskeletons as possible assisted movement solutions will be discussed-pointing to the best options for rehabilitation.

Myoelectric Control Systems for Upper Limb Wearable Robotic Exoskeletons and Exosuits-A Systematic Review

In recent years, myoelectric control systems have emerged for upper limb wearable robotic exoskeletons to provide movement assistance and/or to restore motor functions in people with motor disabilities and to augment human performance in able-bodied individuals. In myoelectric control, electromyographic (EMG) signals from muscles are utilized to implement control strategies in exoskeletons and exosuits, improving adaptability and human-robot interactions during various motion tasks. This paper reviews the state-of-the-art myoelectric control systems designed for upper-limb wearable robotic exoskeletons and exosuits, and highlights the key focus areas for future research directions. Here, different modalities of existing myoelectric control systems were described in detail, and their advantages and disadvantages were summarized. Furthermore, key design aspects (i.e., supported degrees of freedom, portability, and intended application scenario) and the type of experiments conducted to validate the efficacy of the proposed myoelectric controllers were also discussed. Finally, the challenges and limitations of current myoelectric control systems were analyzed, and future research directions were suggested.

Sensors and Actuation Technologies in Exoskeletons: A Review

Exoskeletons are robots that closely interact with humans and that are increasingly used for different purposes, such as rehabilitation, assistance in the activities of daily living (ADLs), performance augmentation or as haptic devices. In the last few decades, the research activity on these robots has grown exponentially, and sensors and actuation technologies are two fundamental research themes for their development. In this review, an in-depth study of the works related to exoskeletons and specifically to these two main aspects is carried out. A preliminary phase investigates the temporal distribution of scientific publications to capture the interest in studying and developing novel ideas, methods or solutions for exoskeleton design, actuation and sensors. The distribution of the works is also analyzed with respect to the device purpose, body part to which the device is dedicated, operation mode and design methods. Subsequently, actuation and sensing solutions for the exoskeletons described by the studies in literature are analyzed in detail, highlighting the main trends in their development and spread. The results are presented with a schematic approach, and cross analyses among taxonomies are also proposed to emphasize emerging peculiarities.

A Human Joint Torque Estimation Method for Elbow Exoskeleton Control

Exoskeleton for motion assistance has obtained more and more attention due to its advantages in rehabilitation and assistance for daily life. This research designed an estimation method of human joint torque by the kinetic human–machine interaction between the operator’s elbow joint torque and the output of exoskeleton. The human elbow joint torque estimation was obtained by back propagation (BP) neural network with physiological and physical input elements including shoulder posture, elbow joint-related muscles activation, elbow joint position, and angular velocity. An elbow-powered exoskeleton was developed to verify the validity of the human elbow joint torque estimation. The average correlation coefficients of estimated and measured three shoulder joint angles are 97.9%, 96.2%, and 98.1%, which show that estimated joint angles are consistent with the measured joint angle. The average root-mean-square error between estimated elbow joint torque and measured values is about 0.143[Formula: see text]N[Formula: see text]m. The experiment results proved that the proposed strategy had good performance in human joint torque estimation.

CONTINUOUS MOTION AND TIME-VARYING STIFFNESS ESTIMATION OF THE HUMAN ELBOW JOINT BASED ON SEMG

The flexibility of body joints plays an important role in daily life, particularly when performing high-precision rapid pose switching. Importantly, understanding the characteristics of human joint movement is necessary for constructing robotic joints with the softness of humanoid joints. A novel method for estimating continuous motion and time-varying stiffness of the human elbow joint was proposed in the current study, which was based on surface electromyography (sEMG). We used the Hill-based muscle model (HMM) to establish a continuous motion estimation model (CMEM) of the elbow joint, and the genetic algorithm (GA) was used to optimize unknown parameters. Muscle short-range stiffness (SRS) was then used to characterize muscle stiffness, and a joint kinetic equation was used to express the relationship between skeletal muscle stiffness and elbow joint stiffness. Finally, we established a time-varying stiffness estimation model (TVSEM) of the elbow joint based on the CMEM. In addition, five subjects were tested to verify the performance of the CMEM and TVSEM. The total average root-mean-square errors (RMSEs) of the CMEM with the optimal trials were 0.19[Formula: see text]rad and 0.21[Formula: see text]rad and the repeated trials were 0.24[Formula: see text]rad and 0.25[Formula: see text]rad, with 1.25-kg and 2.5[Formula: see text]kg-loads, respectively. The values of elbow joint stiffness ranged from 0–40[Formula: see text]Nm/rad for different muscle activities, which were estimated by the TVSEM.

Assessment of the energy-related cost function over a range of walking speeds

Cost funtions are needed for calculation of muscle forces in musculoskeletal models. The behavior of the energy-related cost function, proposed by Praagman et al. (J Biomech 39(4):758-765, 2006. https://doi.org/10.1016/j.jbiomech.2004.11.034 ) (CFP), can be used as an optimization criteria in musculoskeletal models for studying gait. In particular, in this work, its performance is compared against two empirical phenomenological models at different walking speed conditions. Also, the sensitivity of the CFP function to model parameters, such as muscle mass, maximal isometric muscle force, optimal muscle fiber length and maximum muscle velocity of the contractile element, was analyzed. The obtained results showed that CFP presents different behavior (in terms of the normalized root-mean-squared deviation (NRMSD) and the coefficient of multiple correlation (CMC)) for different muscles. Also, it provided estimates with median of NRMSD between 0.176 and 0.299 and median of CMC between 0.703 and 0.865 both metrics for slow, free and fast walking speed, which could be considered as acceptable results. Furthermore, the results indicated that CFP is insensitive to changes in muscle mass and relatively sensitive to maximal isometric muscle force. However, CFP presented a noisy behavior on estimations of muscle energy rate for some muscle as compared to phenomenological models. Finally, estimations by CFP during gait are within the values obtained by the empirical phenomenological models.

A novel attention-based hybrid CNN-RNN architecture for sEMG-based gesture recognition

The surface electromyography (sEMG)-based gesture recognition with deep learning approach plays an increasingly important role in human-computer interaction. Existing deep learning architectures are mainly based on Convolutional Neural Network (CNN) architecture which captures spatial information of electromyogram signal. Motivated by the sequential nature of electromyogram signal, we propose an attention-based hybrid CNN and RNN (CNN-RNN) architecture to better capture temporal properties of electromyogram signal for gesture recognition problem. Moreover, we present a new sEMG image representation method based on a traditional feature vector which enables deep learning architectures to extract implicit correlations between different channels for sparse multi-channel electromyogram signal. Extensive experiments on five sEMG benchmark databases show that the proposed method outperforms all reported state-of-the-art methods on both sparse multi-channel and high-density sEMG databases. To compare with the existing works, we set the window length to 200ms for NinaProDB1 and NinaProDB2, and 150ms for BioPatRec sub-database, CapgMyo sub-database, and csl-hdemg databases. The recognition accuracies of the aforementioned benchmark databases are 87.0%, 82.2%, 94.1%, 99.7% and 94.5%, which are 9.2%, 3.5%, 1.2%, 0.2% and 5.2% higher than the state-of-the-art performance, respectively.

Empowering lower limbs exoskeletons: state-of-the-art

Given the advanced breakthroughs in the field of supportive robotic technologies, interest in the integration of the human body and a robot into a single system has rapidly increased. The aim of this work is to provide an overview of empowering lower limbs exoskeletons. Along with lower exoskeleton limbs, their unique design concepts, operator–exoskeleton interactions and control strategies are described. Although many problems have been solved in recent development, many challenges remain. Especially in the context of infantry soldiers, fire fighters and rescuers, the challenges of empowering exoskeletons are discussed, and improvements are outlined and described. This study is not only a summary of the current state, but also points to weaknesses of empowering lower limbs exoskeletons and outlines possible improvements.

Experimental evaluation of a sEMG-based control for elbow wearable assistive devices during load lifting tasks

In this work, a surface skin electromyography(sEMG)-based control solution for elbow wearable assistive devices during load lifting tasks is presented. The goal of the controller consists in limiting the user's muscle activity during the task execution, in such a way that the assistive device can partially compensate the load-related biceps muscle effort. Since sEMG-driven control strategies based on the estimation of the joint torques generally requires complex task- and subject-dependent training sessions for tuning the control algorithms, here a more direct control approach is proposed, based on a muscle activity error related proportional-integral action together with an double-threshold activation logic. The controller's parameters are easily set by means of a fast, online and automatic subject calibration procedure, ensuring a simple adjustability to different users. An experimental phase has been conducted in order to evaluate the sEMG-based control performance involving four healthy subjects, using as wearable assistive device a twisted string action module, which is particularly suitable for assistive applications because of its lightness and compactness. Results show that the control strategy is able to successfully limit the EMG activity of the subjects during the lifting tasks, providing preliminary outcomes and promising possibilities for the use of twisted string-based technologies to assist human joints and muscles.

Muscle Synergy-Driven Robust Motion Control

Humans are able to robustly maintain desired motion and posture under dynamically changing circumstances, including novel conditions. To accomplish this, the brain needs to optimize the synergistic control between muscles against external dynamic factors. However, previous related studies have usually simplified the control of multiple muscles using two opposing muscles, which are minimum actuators to simulate linear feedback control. As a result, they have been unable to analyze how muscle synergy contributes to motion control robustness in a biological system. To address this issue, we considered a new muscle synergy concept used to optimize the synergy between muscle units against external dynamic conditions, including novel conditions. We propose that two main muscle control policies synergistically control muscle units to maintain the desired motion against external dynamic conditions. Our assumption is based on biological evidence regarding the control of multiple muscles via the corticospinal tract. One of the policies is the group control policy (GCP), which is used to control muscle group units classified based on functional similarities in joint control. This policy is used to effectively resist external dynamic circumstances, such as disturbances. The individual control policy (ICP) assists the GCP in precisely controlling motion by controlling individual muscle units. To validate this hypothesis, we simulated the reinforcement of the synergistic actions of the two control policies during the reinforcement learning of feedback motion control. Using this learning paradigm, the two control policies were synergistically combined to result in robust feedback control under novel transient and sustained disturbances that did not involve learning. Further, by comparing our data to experimental data generated by human subjects under the same conditions as those of the simulation, we showed that the proposed synergy concept may be used to analyze muscle synergy-driven motion control robustness in humans.

A Control Scheme to Minimize Muscle Energy for Power Assistant Robotic Systems Under Unknown External Perturbation

This paper proposes a novel control method to minimize muscle energy for power-assistant robotic systems that support the intended motions of a user under unknown external perturbations, using surface electromyogram (sEMG) signals. Conventional control methods based on force/torque (F/T) sensors have limitations to detect human intentions and could, presumably, misunderstand or distort such intentions because of external perturbations of the interaction forces, such as those found in activities of daily living. F/T sensors measure the sum of the applied force, including unknown external forces and human intention; thus, a power-assistant robot controller cannot exactly decompose the real human force. In this paper, we describe a counterexample that cannot be supported by conventional force-sensor-based control methods. We also verify why these control methods may guide human behavior in the wrong direction, and thus, have limitations under unknown external perturbations. We then propose a new control method to minimize the muscle energy indicated by sEMG signals. The proposed control approach is fundamentally based on the concept of power-assistance, in which a robot can reduce the users expended muscle energy while performing given tasks. The proposed control approach is verified through experiments using a power-assistant robotic system for the upper limbs under external perturbations.

AN ANGLE-EMG BIOMECHANICAL MODEL OF THE HUMAN ELBOW JOINT

The biomechanical model of the human elbow joint is extensively studied. In the model, the surface electromyography (sEMG) is used as the input signal, whereas the muscle force or muscle torque is commonly considered as the output signal. The estimation of the actual muscle force or torque is important to effectively modulate the tremor suppression. However, the measurement of the muscle force or torque in vivo is difficult. In this paper, a new angle-to-EMG biomechanical model of the elbow joint was developed and evaluated by comparing the measured sEMG with the calculated sEMG. Three sources of the sEMG signal, namely, the central nervous system (CNS), the Golgi tendon and the muscle spindle were considered in this model. Furthermore, a local PID algorithm was proposed to describe the impact of the CNS on the motor neuron and the Golgi tendon model was used to transform muscle forces to stimulus signals. The model was calibrated by an improved search procedure combining the Powell search and the direct search to determine optimal model parameters. In the experiment, an sEMG signal acquisition system was established to measure the sEMG signal and the elbow joint angle. The experimental results, the predicted sEMG signal well following the measured sEMG, demonstrated that the calibrated model could be used to estimate in vivo sEMG signals and is beneficial to explore the peripheral neural system and the pathogenesis of tremor.

Movement Performance of Human-Robot Cooperation Control Based on EMG-Driven Hill-Type and Proportional Models for an Ankle Power-Assist Exoskeleton Robot

Although the merits of electromyography (EMG)-based control of powered assistive systems have been certified, the factors that affect the performance of EMG-based human-robot cooperation, which are very important, have received little attention. This study investigates whether a more physiologically appropriate model could improve the performance of human-robot cooperation control for an ankle power-assist exoskeleton robot. To achieve the goal, an EMG-driven Hill-type neuromusculoskeletal model (HNM) and a linear proportional model (LPM) were developed and calibrated through maximum isometric voluntary dorsiflexion (MIVD). The two control models could estimate the real-time ankle joint torque, and HNM is more accurate and can account for the change of the joint angle and muscle dynamics. Then, eight healthy volunteers were recruited to wear the ankle exoskeleton robot and complete a series of sinusoidal tracking tasks in the vertical plane. With the various levels of assist based on the two calibrated models, the subjects were instructed to track the target displayed on the screen as accurately as possible by performing ankle dorsiflexion and plantarflexion. Two measurements, the root mean square error (RMSE) and root mean square jerk (RMSJ), were derived from the assistant torque and kinematic signals to characterize the movement performances, whereas the amplitudes of the recorded EMG signals from the tibialis anterior (TA) and the gastrocnemius (GAS) were obtained to reflect the muscular efforts. The results demonstrated that the muscular effort and smoothness of tracking movements decreased with an increase in the assistant ratio. Compared with LPM, subjects made lower physical efforts and generated smoother movements when using HNM, which implied that a more physiologically appropriate model could enable more natural and human-like human-robot cooperation and has potential value for improvement of human-exoskeleton interaction in future applications.

The role of muscle synergies in myoelectric control: trends and challenges for simultaneous multifunction control

Myoelectric control is filled with potential to significantly change human-robot interaction due to the ability to non-invasively measure human motion intent. However, current control schemes have struggled to achieve the robust performance that is necessary for use in commercial applications. As demands in myoelectric control trend toward simultaneous multifunctional control, multi-muscle coordinations, or synergies, play larger roles in the success of the control scheme. Detecting and refining patterns in muscle activations robust to the high variance and transient changes associated with surface electromyography is essential for efficient, user-friendly control. This article reviews the role of muscle synergies in myoelectric control schemes by dissecting each component of the scheme with respect to associated challenges for achieving robust simultaneous control of myoelectric interfaces. Electromyography recording details, signal feature extraction, pattern recognition and motor learning based control schemes are considered, and future directions are proposed as steps toward fulfilling the potential of myoelectric control in clinically and commercially viable applications.

Myoelectrically controlled wrist robot for stroke rehabilitation

BACKGROUND

Robot-assisted rehabilitation is an advanced new technology in stroke rehabilitation to provide intensive training. Post-stroke motor recovery depends on active rehabilitation by voluntary participation of patient's paretic motor system as early as possible in order to promote reorganization of brain. However, voluntary residual motor efforts to the affected limb have not been involved enough in most robot-assisted rehabilitation for patients after stroke. The objective of this study is to evaluate the feasibility of robot-assisted rehabilitation using myoelectric control on upper limb motor recovery.

METHODS

In the present study, an exoskeleton-type rehabilitation robotic system was designed to provide voluntarily controlled assisted torque to the affected wrist. Voluntary intention was involved by using the residual surface electromyography (EMG) from flexor carpi radialis(FCR) and extensor carpi radialis (ECR)on the affected limb to control the mechanical assistance provided by the robotic system during wrist flexion and extension in a 20-session training. The system also applied constant resistant torque to the affected wrist during the training. Sixteen subjects after stroke had been recruited for evaluating the tracking performance and therapeutical effects of myoelectrically controlled robotic system.

RESULTS

With the myoelectrically-controlled assistive torque, stroke survivors could reach a larger range of motion with a significant decrease in the EMG signal from the agonist muscles. The stroke survivors could be trained in the unreached range with their voluntary residual EMG on the paretic side. After 20-session rehabilitation training, there was a non-significant increase in the range of motion and a significant decrease in the root mean square error (RMSE) between the actual wrist angle and target angle. Significant improvements also could be found in muscle strength and clinical scales.

CONCLUSIONS

These results indicate that robot-aided therapy with voluntary participation of patient's paretic motor system using myoelectric control might have positive effect on upper limb motor recovery.

An optimized model for estimation of muscle contribution and human joint torques from sEMG information

This paper develops a Hill model based technique to estimate human elbow torque from sEMG measurements. Some new parameters are included in the optimization process in order to improve the resulting estimated torque. These parameters correspond to activation levels of muscles involved in motion generation. They have not previously been used in other works dealing with this kind of model. Results from experiments with several subjects in different movement conditions and using the new optimized parameters lead to some conclusions about the generality of the optimized models and the influence of the new parameters on the improvement of the estimation.

A joint computational respiratory neural network-biomechanical model for breathing and airway defensive behaviors

Data-driven computational neural network models have been used to study mechanisms for generating the motor patterns for breathing and breathing related behaviors such as coughing. These models have commonly been evaluated in open loop conditions or with feedback of lung volume simply represented as a filtered version of phrenic motor output. Limitations of these approaches preclude assessment of the influence of mechanical properties of the musculoskeletal system and motivated development of a biomechanical model of the respiratory muscles, airway, and lungs using published measures from human subjects. Here we describe the model and some aspects of its behavior when linked to a computational brainstem respiratory network model for breathing and airway defensive behavior composed of discrete "integrate and fire" populations. The network incorporated multiple circuit paths and operations for tuning inspiratory drive suggested by prior work. Results from neuromechanical system simulations included generation of a eupneic-like breathing pattern and the observation that increased respiratory drive and operating volume result in higher peak flow rates during cough, even when the expiratory drive is unchanged, or when the expiratory abdominal pressure is unchanged. Sequential elimination of the model's sources of inspiratory drive during cough also suggested a role for disinhibitory regulation via tonic expiratory neurons, a result that was subsequently supported by an analysis of in vivo data. Comparisons with antecedent models, discrepancies with experimental results, and some model limitations are noted.

UPPER LIMB POWERED EXOSKELETON

An exoskeleton is a wearable robot with joints and links corresponding to those of the human body. With applications in rehabilitation medicine, virtual reality simulation, and teleoperation, exoskeletons offer benefits for both disabled and healthy populations. Analytical and experimental approaches were used to develop, integrate, and study a powered exoskeleton for the upper limb and its application as an assistive device. The kinematic and dynamic dataset of the upper limb during daily living activities was one among several factors guiding the development of an anthropomorphic, seven degree-of-freedom, powered arm exoskeleton. Additional design inputs include anatomical and physiological considerations, workspace analyses, and upper limb joint ranges of motion. Proximal placement of motors and distal placement of cable-pulley reductions were incorporated into the design, leading to low inertia, high-stiffness links, and back-drivable transmissions with zero backlash. The design enables full glenohumeral, elbow, and wrist joint functionality. Establishing the human-machine interface at the neural level was facilitated by the development of a Hill-based muscle model (myoprocessor) that enables intuitive interaction between the operator and the wearable robot. Potential applications of the exoskeleton as a wearable robot include (i) an assistive (orthotic) device for human power amplifications, (ii) a therapeutic and diagnostics device for physiotherapy, (iii) a haptic device in virtual reality simulation, and (iv) a master device for teleoperation.

Spectral properties of electromyographic and mechanomyographic signals during dynamic concentric and eccentric contractions of the human biceps brachii muscle

The purpose of this study was to describe and examine the variations in recruitment patterns of motor units (MUs) in biceps brachii (BB) through a range of joint motion during dynamic eccentric and concentric contractions. Twelve healthy participants (6 females, 6 males, age=30±8.5 years) performed concentric and eccentric contractions with constant external loading at different levels. Surface electromyography (EMG) and mechanomyography (MMG) were recorded from BB. The EMGs and MMGs were decomposed into their intensities in time-frequency space using a wavelet technique. The EMG and MMG spectra were then compared using principal component analysis. Variations in total intensity, first principal component (PCI), and the angle θ formed by first component (PCI) and second component (PCII) loading scores were explained in terms of MU recruitment patterns and elbow angles. Elbow angle had a significant effect on dynamic concentric and eccentric contractions. The EMG total intensity was greater for concentric than for eccentric contractions in the present study. MMG total intensity, however, was lower during concentric than during eccentric contractions. In addition, there was no significant difference in θ between concentric and eccentric contractions for both EMG and MMG. Selective recruitment of fast MUs from BB muscle during eccentric muscle contractions was not found in the present study.

Comparison of regression models for estimation of isometric wrist joint torques using surface electromyography

BACKGROUND

Several regression models have been proposed for estimation of isometric joint torque using surface electromyography (SEMG) signals. Common issues related to torque estimation models are degradation of model accuracy with passage of time, electrode displacement, and alteration of limb posture. This work compares the performance of the most commonly used regression models under these circumstances, in order to assist researchers with identifying the most appropriate model for a specific biomedical application.

METHODS

Eleven healthy volunteers participated in this study. A custom-built rig, equipped with a torque sensor, was used to measure isometric torque as each volunteer flexed and extended his wrist. SEMG signals from eight forearm muscles, in addition to wrist joint torque data were gathered during the experiment. Additional data were gathered one hour and twenty-four hours following the completion of the first data gathering session, for the purpose of evaluating the effects of passage of time and electrode displacement on accuracy of models. Acquired SEMG signals were filtered, rectified, normalized and then fed to models for training.

RESULTS

It was shown that mean adjusted coefficient of determination (Ra2) values decrease between 20%-35% for different models after one hour while altering arm posture decreased mean Ra2 values between 64% to 74% for different models.

CONCLUSIONS

Model estimation accuracy drops significantly with passage of time, electrode displacement, and alteration of limb posture. Therefore model retraining is crucial for preserving estimation accuracy. Data resampling can significantly reduce model training time without losing estimation accuracy. Among the models compared, ordinary least squares linear regression model (OLS) was shown to have high isometric torque estimation accuracy combined with very short training times.

Real-time upper limb motion estimation from surface electromyography and joint angular velocities using an artificial neural network for human-machine cooperation

A current challenge with human-machine cooperation systems is to estimate human motions to facilitate natural cooperation and safety of the human. It is a logical approach to estimate the motions from their sources (skeletal muscles); thus, we employed surface electromyography (SEMG) to estimate body motions. In this paper, we investigated a cooperative manipulation control by an upper limb motion estimation method using SEMG and joint angular velocities. The SEMG signals from five upper limb muscles and angular velocities of the limb joints were used to approximate the flexion-extension of the limb in the 2-D sagittal plane. The experimental results showed that the proposed estimation method provides acceptable performance of the motion estimation [normalized root mean square error (NRMSE) <0.15, correlation coefficient (CC) >0.9] under the noncontact condition. From the analysis of the results, we found the necessity of the angular velocity input and estimation error feedback due to physical contact. Our results suggest that the estimation method can be useful for a natural human-machine cooperation control.

Individual muscle control using an exoskeleton robot for muscle function testing

Healthy individuals modulate muscle activation patterns according to their intended movement and external environment. Persons with neurological disorders (e.g., stroke and spinal cord injury), however, have problems in movement control due primarily to their inability to modulate their muscle activation pattern in an appropriate manner. A functionality test at the level of individual muscles that investigates the activity of a muscle of interest on various motor tasks may enable muscle-level force grading. To date there is no extant work that focuses on the application of exoskeleton robots to induce specific muscle activation in a systematic manner. This paper proposes a new method, named "individual muscle-force control" using a wearable robot (an exoskeleton robot, or a power-assisting device) to obtain a wider variety of muscle activity data than standard motor tasks, e.g., pushing a handle by hand. A computational algorithm systematically computes control commands to a wearable robot so that a desired muscle activation pattern for target muscle forces is induced. It also computes an adequate amount and direction of a force that a subject needs to exert against a handle by his/her hand. This individual muscle control method enables users (e.g., therapists) to efficiently conduct neuromuscular function tests on target muscles by arbitrarily inducing muscle activation patterns. This paper presents a basic concept, mathematical formulation, and solution of the individual muscle-force control and its implementation to a muscle control system with an exoskeleton-type robot for upper extremity. Simulation and experimental results in healthy individuals justify the use of an exoskeleton robot for future muscle function testing in terms of the variety of muscle activity data.

Rehabilitation System Using Teleoperation with Force-Feedback-Based Impedance Adjustment and EMG-Moment Model for Arm Muscle Strength Assessment

In this paper, at first, a teleoperation robot systemwith haptic feedback for rehabilitation is presented. A teleoperation mechanism capable of providing force feedback by means of adjusting the system’s impedance is proposed. The stability of the teleoperation with haptic feedback via a time-delay communication environment is mathematically proved. The proposal operates theoretically with both passive and active assisted movement using teleoperation to rehabilitate of upper limb function. An EMG-moment arm model is proposed for assessing muscle strength. The performance of a two-joint link model using six muscles and a nonlinear relationship between EMG signals and muscle force is confirmed feasible in experiments with five subjects.

Implantable myoelectric sensors (IMESs) for intramuscular electromyogram recording

We have developed a multichannel electrogmyography sensor system capable of receiving and processing signals from up to 32 implanted myoelectric sensors (IMES). The appeal of implanted sensors for myoelectric control is that electromyography (EMG) signals can be measured at their source providing relatively cross-talk-free signals that can be treated as independent control sites. An external telemetry controller receives telemetry sent over a transcutaneous magnetic link by the implanted electrodes. The same link provides power and commands to the implanted electrodes. Wireless telemetry of EMG signals from sensors implanted in the residual musculature eliminates the problems associated with percutaneous wires, such as infection, breakage, and marsupialization. Each implantable sensor consists of a custom-designed application-specified integrated circuit that is packaged into a biocompatible RF BION capsule from the Alfred E. Mann Foundation. Implants are designed for permanent long-term implantation with no servicing requirements. We have a fully operational system. The system has been tested in animals. Implants have been chronically implanted in the legs of three cats and are still completely operational four months after implantation.

Assistive control system using continuous myoelectric signal in robot-aided arm training for patients after stroke

In some stroke rehabilitation programs, robotic systems have been used to aid the patient to train. In this study, a myoelectrically controlled robotic system with 1 degree-of-freedom was developed to assist elbow training in a horizontal plane with intention involvement for people after stroke. The system could provide continuous assistance in extension torque, which was proportional to the amplitude of the subject's electromyographic (EMG) signal from the triceps, and could provide resistive torques during movement. This study investigated the system's effect on restoring the upper limb functions of eight subjects after chronic stroke in a twenty-session rehabilitation training program. In each session, there were 18 trials comprising different combinations of assistive and resistive torques and an evaluation trial. Each trial consisted of five cycles of repetitive elbow flexion and extension between 90 degrees and 0 degrees at a constant velocity of 10 degrees/s. With the assistive extension torque, subjects could reach a more extended position in the first session. After 20 sessions of training, there were statistically significant improvements in the modified Ashworth scale, Fugl-Meyer scale for shoulder and elbow, motor status scale, elbow extension range, muscle strength, and root mean square error between actual elbow angle and target angle. The results showed that the twenty-session training program improved upper limb functions.

Modeling force-velocity relation in skeletal muscle isotonic contraction using an artificial neural network

The aim of this study is to design an artificial neural network (ANN) to model force-velocity relation in skeletal muscle isotonic contraction. We obtained the data set, including physiological and morphometric parameters, by myography and morphometric measurements on frog gastrocnemius muscle. Then, we designed a multilayer perceptron ANN, the inputs of which are muscle volume, muscle optimum length, tendon length, preload, and afterload. The output of the ANN is contraction velocity. The experimental data were divided randomly into two parts. The first part was used to train the ANN. In order to validate the model, the second part of experimental data, which was not used in training, was employed to the ANN and then, its output was compared with Hill model and the experimental data. The behavior of ANN in high forces was more similar to experimental data, but in low forces the Hill model had better results. Furthermore, extrapolation of ANN performance showed that our model is more or less able to simulate eccentric contraction. Our results indicate that ANNs represent a powerful tool to capture some essential features of muscle isotonic contraction.

Real-time myoprocessors for a neural controlled powered exoskeleton arm

Exoskeleton robots are promising assistive/rehabilitative devices that can help people with force deficits or allow the recovery of patients who have suffered from pathologies such as stroke. The key component that allows the user to control the exoskeleton is the human machine interface (HMI). Setting the HMI at the neuro-muscular level may lead to seamless integration and intuitive control of the exoskeleton arm as a natural extension of the human body. At the core of the exoskeleton HMI there is a model of the human muscle, the "myoprocessor," running in real-time and in parallel to the physiological muscle, that predicts joint torques as a function of the joint kinematics and neural activation levels. This paper presents the development of myoprocessors for the upper limb based on the Hill phenomenological muscle model. Genetic algorithms are used to optimize the internal parameters of the myoprocessors utilizing an experimental database that provides inputs to the model and allows for performance assessment. The results indicate high correlation between joint moment predictions of the model and the measured data. Consequently, the myoprocessor seems an adequate model, sufficiently robust for further integration into the exoskeleton control system.

In vivo assessment of elbow flexor work and activation during stretch-shortening cycle tasks

The purpose of this study was to use an electromyography (EMG) based muscle model to investigate the performance enhancement of stretch-shortening cycle (SSC) tasks at different elbow flexion-extension velocities. A torque motor was used to oscillate the forearms of seven healthy male subjects (23-40 years) during SSC and non-SSC contractions at four frequencies of movement (.58, 1.5, 2.4 and 3.3Hz) over a range of 105 degrees -162 degrees of elbow extension. The torque was integrated as a function of joint angle to yield the work produced by the elbow flexors. The elbow flexors were transcutaneously stimulated with a voltage equivalent to 60% maximum voluntary isometric contraction torque for 4s at 50Hz. EMG of the elbow flexors and extensors was recorded from the biceps and triceps respectively. The processed EMG was used to drive a Hill based model to predict the torque of the elbow flexors. Results indicate that muscle work increases from non-SSC to SSC trials. Work decreases for SSC and non-SSC trials with increasing velocity. The simulated constant activation muscle model predicted work well for all trials and conditions, indicating muscle model accuracy. The EMG driven model predicted well for all non-SSC trials, but significantly underestimated the work for SSC tasks, suggesting that the contractile component is directly involved in optimising muscle work during SSC tasks.

Using recurrent artificial neural network model to estimate voluntary elbow torque in dynamic situations

Muscle modelling is an important component of body segmental motion analysis. Although many studies had focused on static conditions, the relationship between electromyographic (EMG) signals and joint torque under voluntary dynamic situations has not been well investigated. The aim of this study was to investigate the performance of a recurrent artificial neural network (RANN) under voluntary dynamic situations for torque estimation of the elbow complex. EMG signals together with kinematic data, which included angle and angular velocity, were used as the inputs to estimate the expected torque during movement. Moreover, the roles of angle and angular velocity in the accuracy of prediction were investigated, and two models were compared. One model used EMG and joint kinematic inputs and the other model used only EMG inputs without kinematic data. Six healthy subjects were recruited, and two average angular velocities (60 degrees s(-1) and 90 degrees s(-1)) with three different loads (0 kg, 1 kg, 2 kg) in the hand position were selected to train and test the RANN between 90 degrees elbow flexion and full elbow extension (0 degrees). After training, the root mean squared error (RMSE) between expected torque and predicted torque of the model, with EMG and joint kinematic inputs in the training data set and the test data set, were 0.17 +/- 0.03 Nm and 0.35 +/- 0.06 Nm, respectively. The RMSE values between expected torque and predicted torque of the model, with only EMG inputs in the training data set and the test set, were 0.57 +/- 0.07 Nm and 0.73 +/- 0.11 Nm, respectively. The results showed that EMG signals together with kinematic data gave significantly better performance in the joint torque prediction; joint angle and angular velocity provided important information in the estimation of joint torque in voluntary dynamic movement.

A numeric study of the noise-induced tremor in a mathematical model of the stretch reflex

A mathematical model of the stretch reflex for the cat soleus muscle is presented. The time-delay differential equations of the model are solved using the fourth-order Runge-Kutta algorithm, introducing a Gaussian-noise term to simulate the environmental noise. The muscle response dynamics are then studied under various levels of average muscle activation. Finally, the feasibility of explaining the so-called physiological tremor from the properties of the stretch reflex mechanisms is discussed by comparing our results with reported experimental evidence.

Hierarchical genetic algorithm versus static optimization-investigation of elbow flexion and extension movements

The applicability of static optimization (and, respectively, frequently used objective functions) for prediction of individual muscle forces for dynamic conditions has often been discussed. Some of the problems are whether time-independent objective functions are suitable, and how to incorporate muscle physiology in models. The present paper deals with a twofold task: (1) implementation of hierarchical genetic algorithm (HGA) based on the properties of the motor units (MUs) twitches, and using multi-objective, time-dependent optimization functions; and (2) comparison of the results of the HGA application with those obtained through static optimization with a criterion "minimum of a weighted sum of the muscle forces raised to the power of n". HGA and its software implementation are presented. The moments of neural stimulation of all MUs are design variables coding the problem in the terms of HGA. The main idea is in using genetic operations to find these moments, so that the sum of MUs twitches satisfies the imposed goals (required joint moments, minimal sum of muscle forces, etc.). Elbow flexion and extension movements with different velocities are considered as proper illustration. It is supposed that they are performed by two extensor muscles and three flexor muscles. The results show that HGA is a suitable means for precise investigation of motor control. Many experimentally observed phenomena (such as antagonistic co-contraction, three-phasic behavior of the muscles during fast movements) can find their explanation by the properties of the MUs twitches. Static optimization is also able to predict three-phasic behavior and could be used as practicable and computationally inexpensive method for total estimation of the muscle forces.

Biomechanical analysis of fatigue-related foot injury mechanisms in athletes and recruits during intensive marching

An integrative analysis, comprising radiographic imaging of the foot, plantar pressure measurements, surface electromyography (EMG) and finite element (FE) modelling of the three-dimensional (3D) foot structure, was used to determine the effects of muscular fatigue induced by intensive athletic or military marching on the structural stability of the foot and on its internal stress state during the stance phase. The medial/lateral (M/L) tendency towards instability of the foot structure during marching in fatigue conditions was experimentally characterised by measuring the M/L deviations of the foot-ground centre of pressure (COP) and correlating these data with fatigue of specific lower-limb muscles, as demonstrated by the EMG spectra. The results demonstrated accelerated fatigue of the peroneus longus muscle in marching conditions (treadmill march of 2 km completed by four subjects at an approximately constant velocity of 8 km h-1). Severe fatigue of the peroneus longus is apparently the dominant cause of lack of foot stability, which was manifested by abnormal lateral deviations of the COP during the stance phase. Under these conditions, ankle sprain injuries are likely to occur. The EMG analysis further revealed substantial fatigue of the pre-tibial and triceps surae muscles during intensive marching (averaged decreases of 36% and 40% in the median frequency of their EMG signal spectra, respectively). Incorporation of this information into the 3D FE model of the foot resulted in a substantial rise in the levels of calcaneal and metatarsal stress concentrations, by 50% and 36%, respectively. This may point to the mechanism by which stress fractures develop and provide the biomechanical tools for future clinical investigations.

Analysis of muscular fatigue and foot stability during high-heeled gait

Plantar pressure measurements and surface electromyography (EMG) were used to determine the effects of muscular fatigue induced by high-heeled gait. The medio-lateral (M/L) stability of the foot was characterized by measuring the M/L deviations of the center of pressure (COP) and correlating these data with fatigue of lower-limb muscles seen on EMG. EMG measurements from habitual high-heeled shoe wearers demonstrated an imbalance of gastrocnemius lateralis versus gastrocnemius medialis activity in fatigue conditions, which correlated with abnormal lateral shifts in the foot-ground or shoe-ground COP of these women.