A Prosthetic Hand Body Area Controller Based on Efficient Pattern Recognition Control Strategies

Poliarticulated prosthetic hands represent a powerful tool to restore functionality and improve quality of life for upper limb amputees. Such devices offer, on the same wearable node, sensing and actuation capabilities, which are not equally supported by natural interaction and control strategies. The control in state-of-the-art solutions is still performed mainly through complex encoding of gestures in bursts of contractions of the residual forearm muscles, resulting in a non-intuitive Human-Machine Interface (HMI). Recent research efforts explore the use of myoelectric gesture recognition for innovative interaction solutions, however there persists a considerable gap between research evaluation and implementation into successful complete systems. In this paper, we present the design of a wearable prosthetic hand controller, based on intuitive gesture recognition and a custom control strategy. The wearable node directly actuates a poliarticulated hand and wirelessly interacts with a personal gateway (i.e., a smartphone) for the training and personalization of the recognition algorithm. Through the whole system development, we address the challenge of integrating an efficient embedded gesture classifier with a control strategy tailored for an intuitive interaction between the user and the prosthesis. We demonstrate that this combined approach outperforms systems based on mere pattern recognition, since they target the accuracy of a classification algorithm rather than the control of a gesture. The system was fully implemented, tested on healthy and amputee subjects and compared against benchmark repositories. The proposed approach achieves an error rate of 1.6% in the end-to-end real time control of commonly used hand gestures, while complying with the power and performance budget of a low-cost microcontroller.

A Review of Classification Techniques of EMG Signals during Isotonic and Isometric Contractions

In recent years, there has been major interest in the exposure to physical therapy during rehabilitation. Several publications have demonstrated its usefulness in clinical/medical and human machine interface (HMI) applications. An automated system will guide the user to perform the training during rehabilitation independently. Advances in engineering have extended electromyography (EMG) beyond the traditional diagnostic applications to also include applications in diverse areas such as movement analysis. This paper gives an overview of the numerous methods available to recognize motion patterns of EMG signals for both isotonic and isometric contractions. Various signal analysis methods are compared by illustrating their applicability in real-time settings. This paper will be of interest to researchers who would like to select the most appropriate methodology in classifying motion patterns, especially during different types of contractions. For feature extraction, the probability density function (PDF) of EMG signals will be the main interest of this study. Following that, a brief explanation of the different methods for pre-processing, feature extraction and classifying EMG signals will be compared in terms of their performance. The crux of this paper is to review the most recent developments and research studies related to the issues mentioned above.

Effects of input frequency content and signal-to-noise ratio on the parametric estimation of surface EMG-torque dynamics

The dynamic relationship between surface EMG (sEMG) and torque can be estimated from data acquired while subjects voluntarily modulate joint torque. We have shown that for such data, the input (EMG) contains a feedback component from the output (torque) and so accurate estimates of the dynamics require the use of closed-loop identification algorithms. Moreover, this approach has several other limitations since the input is controlled indirectly and so the frequency content and signal-to-noise ratio cannot be controlled. This paper investigates how these factors influence the accuracy of estimates. This was studied using experimental sEMG recorded from healthy human subjects for tasks with different modulation rates. Box-Jenkin (BJ) method was used for identification. Results showed that input frequency content had little effect on estimates of gain and natural frequency but had strong effect on damping factor estimates. It was demonstrated that to accurately estimate the damping factor, the command signal switching rate must be less than 2s. It was also shown that random errors increased with noise level but was limited to 10% of the parameters true value for highest noise level tested. To summarize, simulation study of this work showed that voluntary modulation paradigm can accurately identify sEMG-torque dynamics.

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.

Exoskeletons for industrial application and their potential effects on physical work load

The aim of this review was to provide an overview of assistive exoskeletons that have specifically been developed for industrial purposes and to assess the potential effect of these exoskeletons on reduction of physical loading on the body. The search resulted in 40 papers describing 26 different industrial exoskeletons, of which 19 were active (actuated) and 7 were passive (non-actuated). For 13 exoskeletons, the effect on physical loading has been evaluated, mainly in terms of muscle activity. All passive exoskeletons retrieved were aimed to support the low back. Ten-forty per cent reductions in back muscle activity during dynamic lifting and static holding have been reported. Both lower body, trunk and upper body regions could benefit from active exoskeletons. Muscle activity reductions up to 80% have been reported as an effect of active exoskeletons. Exoskeletons have the potential to considerably reduce the underlying factors associated with work-related musculoskeletal injury. Practitioner Summary: Worldwide, a significant interest in industrial exoskeletons does exist, but a lack of specific safety standards and several technical issues hinder mainstay practical use of exoskeletons in industry. Specific issues include discomfort (for passive and active exoskeletons), weight of device, alignment with human anatomy and kinematics, and detection of human intention to enable smooth movement (for active exoskeletons).

A method for early detection of the initiation of sit-to-stand posture transitions

A powered lower extremity orthotic brace can potentially be used to assist frail elderly during daily activities. This paper presents a method for an early detection of the initiation of sit-to-stand (SiSt) posture transition that can be used in the control of the powered orthosis. Unlike the methods used in prosthetic devices that rely on surface electromyography (EMG), the proposed method uses only sensors embedded into the orthosis brace attached to the limb. The method was developed and validated using data from a human study with 10 individuals. Each human trial included different sets of sitting, standing and walking activities originating from various initial postures. Features from the sensor signal were extracted and aggregated in lagged epochs to incorporate the time history. Principal component analysis (PCA) was used to reduce the feature set. The principal components were then used in a leave-one-out manner to train a linear support vector machine (SVM) classifier to perform early detection of the SiSt posture transition. The proposed method achieved the sensitivity of 100% and the specificity 92.94% of trials without false positives. The average detection time (DT) of 0.1341  ±  0.3310 s following the start of transition demonstrated early recognition of the initiation of SiSt transition.

Adaptive Control of Exoskeleton Robots for Periodic Assistive Behaviours Based on EMG Feedback Minimisation

In this paper we propose an exoskeleton control method for adaptive learning of assistive joint torque profiles in periodic tasks. We use human muscle activity as feedback to adapt the assistive joint torque behaviour in a way that the muscle activity is minimised. The user can then relax while the exoskeleton takes over the task execution. If the task is altered and the existing assistive behaviour becomes inadequate, the exoskeleton gradually adapts to the new task execution so that the increased muscle activity caused by the new desired task can be reduced. The advantage of the proposed method is that it does not require biomechanical or dynamical models. Our proposed learning system uses Dynamical Movement Primitives (DMPs) as a trajectory generator and parameters of DMPs are modulated using Locally Weighted Regression. Then, the learning system is combined with adaptive oscillators that determine the phase and frequency of motion according to measured Electromyography (EMG) signals. We tested the method with real robot experiments where subjects wearing an elbow exoskeleton had to move an object of an unknown mass according to a predefined reference motion. We further evaluated the proposed approach on a whole-arm exoskeleton to show that it is able to adaptively derive assistive torques even for multiple-joint motion.

An assistive decision-and-control architecture for force-sensitive hand–arm systems driven by human–machine interfaces

Fully autonomous applications of modern robotic systems are still constrained by limitations in sensory data processing, scene interpretation, and automated reasoning. However, their use as assistive devices for people with upper-limb disabilities has become possible with recent advances in “soft robotics”, that is, interaction control, physical human–robot interaction, and reflex planning. In this context, impedance and reflex-based control has generally been understood to be a promising approach to safe interaction robotics.

To create semi-autonomous assistive devices, we propose a decision-and-control architecture for hand–arm systems with “soft robotics” capabilities, which can then be used via human–machine interfaces (HMIs). We validated the functionality of our approach within the BrainGate2 clinical trial, in which an individual with tetraplegia used our architecture to control a robotic hand–arm system under neural control via a multi-electrode array implanted in the motor cortex. The neuroscience results of this research have previously been published by Hochberg et al.

In this paper we present our assistive decision-and-control architecture and demonstrate how the semi-autonomous assistive behavior can help the user. In our framework the robot is controlled through a multi-priority Cartesian impedance controller and its behavior is extended with collision detection and reflex reaction. Furthermore, virtual workspaces are added to ensure safety. On top of this we employ a decision-and-control architecture that uses sensory information available from the robotic system to evaluate the current state of task execution. Based on a set of available assistive skills, our architecture provides support in object interaction and manipulation and thereby enhances the usability of the robotic system for use with HMIs. The goal of our development is to provide an easy-to-use robotic system for people with physical disabilities and thereby enable them to perform simple tasks of daily living. In an exemplary real-world task, the participant was able to serve herself a beverage autonomously for the first time since her brainstem stroke, which she suffered approximately 14 years prior to this research.

Control of robotic assistance using poststroke residual voluntary effort

Poststroke hemiparesis limits the ability to reach, in part due to involuntary muscle co-activation (synergies). Robotic approaches are being developed for both therapeutic benefit and continuous assistance during activities of daily living. Robotic assistance may enable participants to exert less effort, thereby reducing expression of the abnormal co-activation patterns, which could allow participants to reach further. This study evaluated how well participants could perform a reaching task with robotic assistance that was either provided independent of effort in the vertical direction or in the sagittal plane in proportion to voluntary effort estimated from electromyograms (EMG) on the affected side. Participants who could not reach targets without assistance were enabled to reach further with assistance. Constant anti-gravity force assistance that was independent of voluntary effort did not reduce the quality of reach and enabled participants to exert less effort while maintaining different target locations. Force assistance that was proportional to voluntary effort on the affected side enabled participants to exert less effort and could be controlled to successfully reach targets, but participants had increased difficulty maintaining a stable position. These results suggest that residual effort on the affected side can produce an effective command signal for poststroke assistive devices.

An upper-limb power-assist exoskeleton using proportional myoelectric control

We developed an upper-limb power-assist exoskeleton actuated by pneumatic muscles. The exoskeleton included two metal links: a nylon joint, four size-adjustable carbon fiber bracers, a potentiometer and two pneumatic muscles. The proportional myoelectric control method was proposed to control the exoskeleton according to the user's motion intention in real time. With the feature extraction procedure and the classification (back-propagation neural network), an electromyogram (EMG)-angle model was constructed to be used for pattern recognition. Six healthy subjects performed elbow flexion-extension movements under four experimental conditions: (1) holding a 1-kg load, wearing the exoskeleton, but with no actuation and for different periods (2-s, 4-s and 8-s periods); (2) holding a 1-kg load, without wearing the exoskeleton, for a fixed period; (3) holding a 1-kg load, wearing the exoskeleton, but with no actuation, for a fixed period; (4) holding a 1-kg load, wearing the exoskeleton under proportional myoelectric control, for a fixed period. The EMG signals of the biceps brachii, the brachioradialis, the triceps brachii and the anconeus and the angle of the elbow were collected. The control scheme's reliability and power-assist effectiveness were evaluated in the experiments. The results indicated that the exoskeleton could be controlled by the user's motion intention in real time and that it was useful for augmenting arm performance with neurological signal control, which could be applied to assist in elbow rehabilitation after neurological injury.

Recovering Planned Trajectories in Robotic Rehabilitation Therapies under the Effect of Disturbances

Robotic rehabilitation is an emerging technology in the field of Neurorehabilitation, which aims to achieve an effective patient recovery. This research focusses on the control strategy for an assistive exoskeleton aiming to reduce the effects of disturbances on planned trajectories during rehabilitation therapies. Disturbances are mostly caused by muscle synergies or by unpredictable actions produced by functional electrical stimulation. The effect of these disturbances can be either assistive or resistive forces depending on the patient's movement, which increase or decrease the speed of the affected joints by forcing the control unit to act consequently. In some therapies, like gait assistance, it is also essential to maintain synchronization between joint movements, to ensure a dynamic stability. A force control approach is used for all the joints individually, while two control methods are defined to act when disturbances are detected: Cartesian position control (Cartesian level) and Variable execution speed (joint level). The trajectory to be followed by the patient is previously recorded using an active exoskeleton, H1, worn by healthy subjects. A realistic simulation model of the exoskeleton is used for testing the effect of disturbances on the particular joints and on the planned trajectory and for evaluating the performance of the two proposed control methods. The performances of the presented methods are evaluated by comparing the resulting trajectories with respect to those planned. The evaluation of the most suitable method is performed considering the following factors: stability, minimum time delay and synchronization of the joints.

A survey on robotic devices for upper limb rehabilitation

The existing shortage of therapists and caregivers assisting physically disabled individuals at home is expected to increase and become serious problem in the near future. The patient population needing physical rehabilitation of the upper extremity is also constantly increasing. Robotic devices have the potential to address this problem as noted by the results of recent research studies. However, the availability of these devices in clinical settings is limited, leaving plenty of room for improvement. The purpose of this paper is to document a review of robotic devices for upper limb rehabilitation including those in developing phase in order to provide a comprehensive reference about existing solutions and facilitate the development of new and improved devices. In particular the following issues are discussed: application field, target group, type of assistance, mechanical design, control strategy and clinical evaluation. This paper also includes a comprehensive, tabulated comparison of technical solutions implemented in various systems.

Electromyogram bandwidth requirements when the signal is whitened

Whitening the surface electromyogram (EMG) improves EMG amplitude (EMGσ) and EMG-torque estimation. Laboratory studies utilizing contraction levels up to maximum voluntary contraction (MVC) show that whitening is useful over a frequency band extending to 1000-2000 Hz. However, EMG electrode systems with such wide bandwidth are uncommon, particularly in real-time applications; and these contraction levels are also not common. Thus, we studied the influence of the frequency band over which whitening was performed versus the resulting performance. Low-level, torque-varying contractions (average torque level of 18.5% flexion MVC) of the elbow were contrasted with medium-level 50% MVC constant-torque contractions. For each, the maximum whitening bandwidth was varied between 30-2000 Hz. The low-level contractions (which incorporate the contraction range of most daily tasks) showed that performance utilizing frequencies out to 400-500 Hz was not statistically different than results out to the full available frequency (2000 Hz). For the medium-level (50% MVC) contractions, frequencies out to 800-900 Hz were statistically equivalent to the full bandwidth. These results suggest that conventional electrodes with a typical passband of ∼ 500 Hz are appropriate when whitening data from contraction levels typically experienced in many applications. Wider bandwidths may be advantageous for strenuous activities.

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.

A modified multi-channel EMG feature for upper limb motion pattern recognition

The EMG signal is a well-known and useful biomedical signal. Much information related to muscles and human motions is included in EMG signals. Many approaches have proposed various methods that tried to recognize human motion via EMG signals. However, one of the critical problems of motion pattern recognition is that the performance of recognition is easily affected by the normalization procedure and may not work well on different days. In this paper, a modified feature of the multi-channel EMG signal is proposed and the normalization procedure is also simplified by using this modified feature. To recognize motion pattern, we applied the support vector machine (SVM) to build the motion pattern recognition model. In training and validation procedures, we used the 2-DoF exoskeleton robot arm system to do the designed pose, and the multi-channel EMG signals were obtained while the user resisted the robot. Experiment results indicate that the performance of applying the proposed feature (94.9%) is better than that of conventional features. Moreover, the performances of the recognition model, which applies the modified feature to recognize the motions on different days, are more stable than other conventional features.

Effects of robotic knee exoskeleton on human energy expenditure

A number of studies discuss the design and control of various exoskeleton mechanisms, yet relatively few address the effect on the energy expenditure of the user. In this paper, we discuss the effect of a performance augmenting exoskeleton on the metabolic cost of an able-bodied user/pilot during periodic squatting. We investigated whether an exoskeleton device will significantly reduce the metabolic cost and what is the influence of the chosen device control strategy. By measuring oxygen consumption, minute ventilation, heart rate, blood oxygenation, and muscle EMG during 5-min squatting series, at one squat every 2 s, we show the effects of using a prototype robotic knee exoskeleton under three different noninvasive control approaches: gravity compensation approach, position-based approach, and a novel oscillator-based approach. The latter proposes a novel control that ensures synchronization of the device and the user. Statistically significant decrease in physiological responses can be observed when using the robotic knee exoskeleton under gravity compensation and oscillator-based control. On the other hand, the effects of position-based control were not significant in all parameters although all approaches significantly reduced the energy expenditure during squatting.

Exoskeleton Technology in Rehabilitation: Towards an EMG-Based Orthosis System for Upper Limb Neuromotor Rehabilitation

The rehabilitation of patients should not only be limited to the first phases during intense hospital care but also support and therapy should be guaranteed in later stages, especially during daily life activities if the patient’s state requires this. However, aid should only be given to the patient if needed and as much as it is required. To allow this, automatic self-initiated movement support and patient-cooperative control strategies have to be developed and integrated into assistive systems. In this work, we first give an overview of different kinds of neuromuscular diseases, review different forms of therapy, and explain possible fields of rehabilitation and benefits of robotic aided rehabilitation. Next, the mechanical design and control scheme of an upper limb orthosis for rehabilitation are presented. Two control models for the orthosis are explained which compute the triggering function and the level of assistance provided by the device. As input to the model fused sensor data from the orthosis and physiology data in terms of electromyography (EMG) signals are used.

A Power Assist Device Based on Joint Equilibrium Point Estimation from EMG Signals

In some researches about power assist devices, surface ElectroMyoGraphy (EMG) signals are used to estimate user intentions to move their limbs. These conventional methods mainly focus on estimation of joint torque. However, the devices based on torque estimation are inclined to cause the vibration of users’ posture originating from the waviness of the EMG signals. Focusing on estimation of states related to the joint angle may improve the performance of the power assist devices. This paper proposes a new method that estimates user joint equilibrium point and stiffness separately from the EMG and that amplifies the stiffness while tuning the device joints according to user equilibrium points. To evaluate the method, we constructed a power assist system for the wrist and compared the method with a method based on simple torque estimation during posture maintenance tasks. Our results showed that the proposed method offers a more stable operation at the same assist ratio and proved the effectiveness of the method.

An EMG-Based Control for an Upper-Limb Power-Assist Exoskeleton Robot

Many kinds of power-assist robots have been developed in order to assist self-rehabilitation and/or daily life motions of physically weak persons. Several kinds of control methods have been proposed to control the power-assist robots according to user's motion intention. In this paper, an electromyogram (EMG)-based impedance control method for an upper-limb power-assist exoskeleton robot is proposed to control the robot in accordance with the user's motion intention. The proposed method is simple, easy to design, humanlike, and adaptable to any user. A neurofuzzy matrix modifier is applied to make the controller adaptable to any users. Not only the characteristics of EMG signals but also the characteristics of human body are taken into account in the proposed method. The effectiveness of the proposed method was evaluated by the experiments.

Assistance using adaptive oscillators: robustness to errors in the identification of the limb parameters

This paper provides a robustness analysis of the method we recently developed for rhythmic movement assistance using adaptive oscillators. An adaptive oscillator is a mathematical tool capable of extracting high-level features (i.e. amplitude, frequency, offset) of a quasi-sinusoidal measured movement, a rhythmic flexion-extension of the elbow in this case. By the use of a simple inverse dynamical model, the system can predict the torque produced by a human participant, such that a fraction of this estimated torque is fed back through a series elastic actuator to provide movement assistance. This paper objectives are twofold. First, we introduce a new 1 DOF assistive device developed in our lab. Second, we derive model-based predictions and conduct experimental validations to measure the variations in movement frequency as a function of the open parameters of the inverse dynamical model. As such, the paper provides an estimation of the robustness of our method due to model approximations. As main result, the paper reveals that the movement frequency is particularly robust to errors in the estimation of the damping coefficient. This is of high interest for the applicability of our approach, this parameter being in general the most difficult to identify.

A lower-limb power-assist robot with perception-assist

In order to assist the motion in the daily lives of physically weak persons such as elderly persons, many kinds of power-assist robots have been developed. In the case of some physically weak persons, the ability to perceive the environment is sometimes deteriorated also. A method of perception-assist has been proposed to assist not only the user's motion but also the user's interaction with an environment, by applying the modification force to the user's motion if it is necessary. In this paper, the perception-assist for a lower-limb power-assist exoskeleton robot is proposed. In the daily life, the walking is very important for persons to achieve desired tasks. Basically, the robot assists the user's muscle force according to the user's motion intention which is estimated based on EMG signals. If the robot has found problems which might lead the user to dangerous situation such as the falling down, the robot tries to modify the user's motion in addition to the ordinal power-assists to make the user walk properly. Since the user might fall down by the effect of the additional modification force of the perception-assist, the robot automatically prevents the user from falling down by considering ZMP (Zero Moment Point). The effectiveness of the proposed method has been evaluated by performing experiments.

ACTIVE EXOSKELETONS FOR UPPER-LIMB MOTION ASSIST

This paper presents the current state of research into power-assist exoskeletons for the upper limb. The assist of the upper limb is important for physically weak persons in daily activities, since upper-limb motion is involved in many important motions in daily living. The most important criterion is that power-assist exoskeletons assist the user's motion automatically in accordance with the user's motion intentions. Electromyogram (EMG) signals in which the user's motion intention is reflected could provide vital real-time information to facilitate accurate control of the power-assist exoskeleton in accordance with the user's motion intentions. A four degree-of-freedom active exoskeleton that assists human upper-limb motion (shoulder vertical flexion/extension, shoulder horizontal flexion/extension, elbow flexion/extension, and forearm supination/pronation) is also proposed.

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.

Identification of constant-posture EMG-torque relationship about the elbow using nonlinear dynamic models

The surface electromyogram (EMG) from biceps and triceps muscles of 33 subjects was related to elbow torque, contrasting EMG amplitude (EMGσ) estimation processors, linear/nonlinear model structures, and system identification techniques. Torque estimation was improved by 1) advanced EMGσ processors (i.e., whitened, multiple-channel signals); 2) longer duration training sets (52 s versus 26 s); and 3) determination of model parameters via pseudoinverse and ridge regression methods. Dynamic, nonlinear parametric models that included second- or third-degree polynomial functions of EMGσ outperformed linear models and Hammerstein/Weiner models. A minimum error of 4.65 ± 3.6% maximum voluntary contraction (MVC) flexion was attained using a third-degree polynomial, 28th-order dynamic model, with model parameters determined using the pseudoinverse method with tolerance 5.6 × 10 (-3) on 52 s of four-channel whitened EMG data. Similar performance (4.67 ± 3.7% MVC flexion error) was realized using a second-degree, 18th-order ridge regression model with ridge parameter 50.1.

Subject-specific myoelectric pattern classification of functional hand movements for stroke survivors

In this study, we developed a robust subject-specific electromyography (EMG) pattern classification technique to discriminate intended manual tasks from muscle activation patterns of stroke survivors. These classifications will enable volitional control of assistive devices, thereby improving their functionality. Twenty subjects with chronic hemiparesis participated in the study. Subjects were instructed to perform six functional tasks while their muscle activation patterns were recorded by ten surface electrodes placed on the forearm and hand of the impaired limb. In order to identify intended functional tasks, a pattern classifier using linear discriminant analysis was applied to the EMG feature vectors. The classification accuracy was mainly affected by the impairment level of the subject. Mean classification accuracy was 71.3% for moderately impaired subjects (Chedoke Stage of Hand 4 and 5), and 37.9% for severely impaired subjects (Chedoke Stage of Hand 2 and 3). Most misclassification occurred between grip tasks of similar nature, for example, among pinch, key, and three-fingered grips, or between cylindrical and spherical grips. EMG signals from the intrinsic hand muscles significantly contributed to the inter-task variability of the feature vectors, as assessed by the inter-task squared Euclidean distance, thereby indicating the importance of intrinsic hand muscles in functional manual tasks. This study demonstrated the feasibility of the EMG pattern classification technique to discern the intent of stroke survivors. Future work should concentrate on the construction of a subject-specific EMG classification paradigm that carefully considers both functional and physiological impairment characteristics of each subject in the target task selection and electrode placement procedures.

A fuzzy-genetic model for estimating forces from electromyographical activity of antagonistic muscles due to planar lower arm movements: the effect of nonlinear muscle properties

The aim of this paper is to create a model for mapping the surface electromyogram (EMG) signals to the force that generated by human arm muscles. Because the parameters of each person's muscle are individual, the model of the muscle must have two characteristics: (1) The model must be adjustable for each subject. (2) The relationship between the input and output of model must be affected by the force-length and the force-velocity behaviors are proven through Hill's experiments. Hill's model is a kinematic mechanistic model with three elements, i.e. one contractile component and two nonlinear spring elements. In this research, fuzzy systems are applied to improve the muscle model. The advantages of using fuzzy system are as follows: they are robust to noise, they prove an adjustable nonlinear mapping, and are able to model the uncertainties of the muscle. Three fuzzy coefficients have been added to the relationships of force-length (active and passive) and force-velocity existing in Hill's model. Then, a genetic algorithm (GA) has been used as a biological search method that can adjust the parameters of the model in order to achieve the optimal possible fit. Finally, the accuracy of the fuzzy genetic implementation Hill-based muscle model (FGIHM) is invested as following: the FGIHM results have 12.4% RMS error (in worse case) in comparison to the experimental data recorded from three healthy male subjects. Moreover, the FGIHM active force-length relationship which is the key characteristics of muscles has been compared to virtual muscle (VM) and Zajac muscle model. The sensitivity of the FGIHM has been evaluated by adding a white noise with zero mean to the input and FGIHM has proved to have lower sensitivity to input noise than the traditional Hill's muscle model.

Oscillator-based assistance of cyclical movements: model-based and model-free approaches

In this article, we propose a new method for providing assistance during cyclical movements. This method is trajectory-free, in the sense that it provides user assistance irrespective of the performed movement, and requires no other sensing than the assisting robot's own encoders. The approach is based on adaptive oscillators, i.e., mathematical tools that are capable of learning the high level features (frequency, envelope, etc.) of a periodic input signal. Here we present two experiments that we recently conducted to validate our approach: a simple sinusoidal movement of the elbow, that we designed as a proof-of-concept, and a walking experiment. In both cases, we collected evidence illustrating that our approach indeed assisted healthy subjects during movement execution. Owing to the intrinsic periodicity of daily life movements involving the lower-limbs, we postulate that our approach holds promise for the design of innovative rehabilitation and assistance protocols for the lower-limb, requiring little to no user-specific calibration.

A comparison of models of the isometric force of locust skeletal muscle in response to pulse train inputs

Muscle models are an important tool in the development of new rehabilitation and diagnostic techniques. Many models have been proposed in the past, but little work has been done on comparing the performance of models. In this paper, seven models that describe the isometric force response to pulse train inputs are investigated. Five of the models are from the literature while two new models are also presented. Models are compared in terms of their ability to fit to isometric force data, using Akaike's and Bayesian information criteria and by examining the ability of each model to describe the underlying behaviour in response to individual pulses. Experimental data were collected by stimulating the locust extensor tibia muscle and measuring the force generated at the tibia. Parameters in each model were estimated by minimising the error between the modelled and actual force response for a set of training data. A separate set of test data, which included physiological kick-type data, was used to assess the models. It was found that a linear model performed the worst whereas a new model was found to perform the best. The parameter sensitivity of this new model was investigated using a one-at-a-time approach, and it found that the force response is not particularly sensitive to changes in any parameter.

Mechanical Performance of Actuators in an Active Orthosis for the Upper Extremities

The aim of the project OrthoJacket is to develop a lightweight, portable, and active orthosis for the upper limps. The system consists of two special designed fluidic actuators which are used for supporting the elbow function and the internal rotation of the shoulder. A new design of flexible fluid actuator (FFA) is presented that enables more design options of attaching parts, as it is allowed by conventional actuators with a stationary centre of rotation. This advantage and the inherent flexibility and the low weight of this kind of actuator predestined them for the use in exoskeletons, orthoses, and prostheses. The actuator for the elbow generates a maximum torque of 32 Nm; the internal rotation is supported with 7 Nm. Both actuators support the movement with up to 100% of the necessary power. The shells for the arm and forearm are made of carbon reinforced structures in combination with inflatable cushions.

Human-robot synchrony: flexible assistance using adaptive oscillators

We propose a novel method for movement assistance that is based on adaptive oscillators, i.e., mathematical tools that are capable of extracting the high-level features (amplitude, frequency, and offset) of a periodic signal. Such an oscillator acts like a filter on these features, but keeps its output in phase with respect to the input signal. Using a simple inverse model, we predicted the torque produced by human participants during rhythmic flexion-extension of the elbow. Feeding back a fraction of this estimated torque to the participant through an elbow exoskeleton, we were able to prove the assistance efficiency through a marked decrease of the biceps and triceps electromyography. Importantly, since the oscillator adapted to the movement imposed by the user, the method flexibly allowed us to change the movement pattern and was still efficient during the nonstationary epochs. This method holds promise for the development of new robot-assisted rehabilitation protocols because it does not require prespecifying a reference trajectory and does not require complex signal sensing or single-user calibration: the only signal that is measured is the position of the augmented joint. In this paper, we further demonstrate that this assistance was very intuitive for the participants who adapted almost instantaneously.

A methodology to quantify alterations in human upper limb movement during co-manipulation with an exoskeleton

While a large number of robotic exoskeletons have been designed by research teams for rehabilitation, it remains rather difficult to analyse their ability to finely interact with a human limb: no performance indicators or general methodology to characterize this capacity really exist. This is particularly regretful at a time when robotics are becoming a recognized rehabilitation method and when complex problems such as 3-D movement rehabilitation and joint rotation coordination are being addressed. The aim of this paper is to propose a general methodology to evaluate, through a reduced set of simple indicators, the ability of an exoskeleton to interact finely and in a controlled way with a human. The method involves measurement and recording of positions and forces during 3-D point to point tasks. It is applied to a 4 degrees-of-freedom limb exoskeleton by way of example.

Study of stability of time-domain features for electromyographic pattern recognition

Background

Significant progress has been made towards the clinical application of human-machine interfaces (HMIs) based on electromyographic (EMG) pattern recognition for various rehabilitation purposes. Making this technology practical and available to patients with motor deficits requires overcoming real-world challenges, such as physical and physiological changes, that result in variations in EMG signals and systems that are unreliable for long-term use. In this study, we aimed to address these challenges by (1) investigating the stability of time-domain EMG features during changes in the EMG signals and (2) identifying the feature sets that would provide the most robust EMG pattern recognition.

Methods

Variations in EMG signals were introduced during physical experiments. We identified three disturbances that commonly affect EMG signals: EMG electrode location shift, variation in muscle contraction effort, and muscle fatigue. The impact of these disturbances on individual features and combined feature sets was quantified by changes in classification performance. The robustness of feature sets was evaluated by a stability index developed in this study.

Results

Muscle fatigue had the smallest effect on the studied EMG features, while electrode location shift and varying effort level significantly reduced the classification accuracy for most of the features. Under these disturbances, the most stable EMG feature set with combination of four features produced at least 16.0% higher classification accuracy than the least stable set. EMG autoregression coefficients and cepstrum coefficients showed the most robust classification performance of all studied time-domain features.

Conclusions

Selecting appropriate EMG feature combinations can overcome the impact of the studied disturbances on EMG pattern classification to a certain extent; however, this simple solution is still inadequate. Stabilizing electrode contact locations and developing effective classifier training strategies are suggested to further improve the robustness of HMIs based on EMG pattern recognition.

Development of a myoelectric control scheme based on a time delayed neural network

Presented in this work is a possible myoelectric control scheme for a rehabilitation robotic application. The control input is from a time delayed neural network (TDNN). The input to the TDNN is four electromyographic (EMG) signals associated with the movement of the elbow and shoulder joints. The output of the TDNN is the joint position of the elbow and the joint position of the shoulder in the sagittal plane. The results presented here show the possibility of controlling multiple degrees of freedom at once. Prior work has shown that the optimal delay for accurate position prediction from a TDNN was 875ms with a 125ms interval, but this work shows that a delay of 300ms and a 100ms interval achieves similar results. This points to the feasibility of a TDNN based control scheme.

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.

Real-time calculation of knee extension moment and its evaluation

This paper proposes a real-time processing algorithm which estimates a subject knee moments using some joint angle data and EMGs of involved muscles. This algorithm will be essential part for the control system design of exoskeletal robotic devices. In order for this algorithm to accurately predict joint moments, it is necessary to know the one's musculo-skeletal properties, which is virtually impossible. An optimization process is used to determine one's musculo-tendon characteristics. The proposed method is evaluated through the comparison with experimental results.

Perception-Assist with an Active Stereo Camera for an Upper-Limb Power-Assist Exoskeleton

This paper presents perception assistance with an active camera for an upper-limb power-assist exoskeleton that assists user perception as well as user motion when the user interacts with the environment using sensors of the exoskeleton. The active stereo camera monitors user interaction with the environment, so the exoskeleton identifies objects that can be touched or grabbed by the user. Stereo camera positioning is controlled to continuously track the exoskeleton end-effector, ensuring that the user's hand always lies within the camera viewfield. If any obstacle might block the camera viewfield, the camera is controlled to avoid the obstacle. The effectiveness of the proposed concept is evaluated in experiments.

Use of MMG signals for the control of powered orthotic devices: development of a rectus femoris measurement protocol

A test protocol is defined for the purpose of measuring rectus femoris mechanomyographic (MMG) signals. The protocol is specified in terms of the following: measurement equipment, signal processing requirements, human postural requirements, test rig, sensor placement, sensor dermal fixation, and test procedure. Preliminary tests of the statistical nature of rectus femoris MMG signals were performed, and Gaussianity was evaluated by means of a two-sided Kolmogorov-Smirnov test. For all 100 MMG data sets obtained from the testing of two volunteers, the null hypothesis of Gaussianity was rejected at the 1%, 5%, and 10% significance levels. Most skewness values were found to be greater than 0.0, while all kurtosis values were found to be greater than 3.0. A statistical convergence analysis also performed on the same 100 MMG data sets suggested that 25 MMG acquisitions should prove sufficient to statistically characterize rectus femoris MMG. This conclusion is supported by the qualitative characteristics of the mean rectus femoris MMG power spectral densities obtained using 25 averages.

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.

EMG-based and gaze-tracking-based man-machine interfaces

A great demand for brain-machine and, more generally, man-machine interfaces is arising nowadays, pushed by several promising scientific and technological results, which are encouraging the concentration of efforts in this field. The possibility of measuring, processing and decoding brain activity, so as to interpret neural signals, is often looked at as a possibility to bypass lost or damaged neural and/or motor structures. Beyond that, such interfaces currently show a potential for applications in other fields, space science being certainly one of them. At present, the concept of "reading" the brain to detect intended actions and use these to control external devices is being studied with several technical and methodological approaches; among these, interfaces based on electroencephalographic signals play today a prominent role. Within such a context, the aim of this section is to present a brief survey on two types of noninvasive man-machine interfaces based on a different approach. In particular, they rely on the extraction of control signals from the user with techniques that adopt electromyography and gaze tracking. Working principles, implementations, typical features, and applications of these two types of interfaces are reported.