Muscle recruitment and coordination with an ankle exoskeleton

Exoskeletons have the potential to assist and augment human performance. Understanding how users adapt their movement and neuromuscular control in response to external assistance is important to inform the design of these devices. The aim of this research was to evaluate changes in muscle recruitment and coordination for ten unimpaired individuals walking with an ankle exoskeleton. We evaluated changes in the activity of individual muscles, cocontraction levels, and synergistic patterns of muscle coordination with increasing exoskeleton work and torque. Participants were able to selectively reduce activity of the ankle plantarflexors with increasing exoskeleton assistance. Increasing exoskeleton net work resulted in greater reductions in muscle activity than increasing exoskeleton torque. Patterns of muscle coordination were not restricted or constrained to synergistic patterns observed during unassisted walking. While three synergies could describe nearly 95% of the variance in electromyography data during unassisted walking, these same synergies could describe only 85–90% of the variance in muscle activity while walking with the exoskeleton. Synergies calculated with the exoskeleton demonstrated greater changes in synergy weights with increasing exoskeleton work versus greater changes in synergy activations with increasing exoskeleton torque. These results support the theory that unimpaired individuals do not exclusively use central pattern generators or other low-level building blocks to coordinate muscle activity, especially when learning a new task or adapting to external assistance, and demonstrate the potential for using exoskeletons to modulate muscle recruitment and coordination patterns for rehabilitation or performance.     

Modulation of shoulder muscle and joint function using a powered upper-limb exoskeleton

Robotic-assistive exoskeletons can enable frequent repetitive movements without the presence of a full-time therapist; however, human-machine interaction and the capacity of powered exoskeletons to attenuate shoulder muscle and joint loading is poorly understood. This study aimed to quantify shoulder muscle and joint force during assisted activities of daily living using a powered robotic upper limb exoskeleton (ArmeoPower, Hocoma). Six healthy male subjects performed abduction, flexion, horizontal flexion, reaching and nose touching activities. These tasks were repeated under two conditions: (i) the exoskeleton compensating only for its own weight, and (ii) the exoskeleton providing full upper limb gravity compensation (i.e., weightlessness). Muscle EMG, joint kinematics and joint torques were simultaneously recorded, and shoulder muscle and joint forces calculated using personalized musculoskeletal models of each subject’s upper limb. The exoskeleton reduced peak joint torques, muscle forces and joint loading by up to 74.8% (0.113 Nm/kg), 88.8% (5.8%BW) and 68.4% (75.6%BW), respectively, with the degree of load attenuation strongly task dependent. The peak compressive, anterior and superior glenohumeral joint force during assisted nose touching was 36.4% (24.6%BW), 72.4% (13.1%BW) and 85.0% (17.2%BW) lower than that during unassisted nose touching, respectively. The present study showed that upper limb weight compensation using an assistive exoskeleton may increase glenohumeral joint stability, since deltoid muscle force, which is the primary contributor to superior glenohumeral joint shear, is attenuated; however, prominent exoskeleton interaction moments are required to position and control the upper limb in space, even under full gravity compensation conditions. The modeling framework and results may be useful in planning targeted upper limb robotic rehabilitation tasks.     

Development and Analysis of an Electrically Actuated Lower Extremity Assistive Exoskeleton

An electrically actuated lower extremity exoskeleton is developed,in which only the knee joint is actuated actively while other joints linked by elastic elements are actuated passively.This paper describes the critical design criteria and presents the process of design and calculation of the actuation system.A flexible physical Human-Robot-Interaction (pHRI) measurement device is designed and applied to detect the human movement,which comprises two force sensors and two gasbags attached to the inner surface of the connection cuff.An online adaptive pHRI minimization control strategy is proposed and implemented to drive the robotic exoskeleton system to follow the motion trajectory of human limb.The measured pHRI information is fused by the Variance Weighted Average (VWA) method.The Mean Square Values (MSV) of pHRI and control torque are utilized to evaluate the performance of the exoskeleton.To improve the comfort level and reduce energy consumption,the gravity compensation is taken into consideration when the control law is designed.Finally,practical experiments are performed on healthy users.Experimental results show that the proposed system can assist people to walk and the outlined control strategy is valid and effective.     

Uncertainty of knee joint muscle activity during knee joint torque exertion: the significance of controlling adjacent joint torque.

In the single-joint torque exertion task, which has been widely used to control muscle activity, only the relevant joint torque is specified. However, the neglect of the neighboring joint could make the procedure unreliable, considering our previous result that even monoarticular muscle activity level is indefinite without specifying the adjacent joint torque. Here we examined the amount of hip joint torque generated with knee joint torque and its influence on the activity of the knee joint muscles. Twelve healthy subjects were requested to exert various levels of isometric knee joint torque. The knee and hip joint torques were obtained by using a custom-made device. Because no information about hip joint torque was provided to the subjects, the hip joint torque measured here was a secondary one associated with the task. The amount of hip joint torque varied among subjects, indicating that they adopted various strategies to achieve the task. In some subjects, there was a considerable internal variability in the hip joint torque. Such variability was not negligible, because the knee joint muscle activity level with respect to the knee joint torque, as quantified by surface electromyography (EMG), changed significantly when the subjects were requested to change the strategy. This change occurred in a very systematic manner: in the case of the knee extension, as the hip flexion torque was larger, the activity of mono- and biarticular knee extensors decreased and increased, respectively. These results indicate that the conventional single knee joint torque exertion has the drawback that the intersubject and/or intertrial variability is inevitable in the relative contribution among mono- and biarticular muscles because of the uncertainty of the hip joint torque. We discuss that the viewpoint that both joint torques need to be considered will bring insights into various controversial problems such as the shape of the EMG-force relationship, neural factors that help determine the effect of muscle strength training, and so on.     

Iterative learning control applied to a hybrid rehabilitation exoskeleton system powered by PAM and FES

Currently upper limb exoskeleton rehabilitation robots powered by electric motors used in the hospitals are usually cumbersome, bulky and unmovable. Our developed RUPERT is a low-cost lightweight portable exoskeleton rehabilitation robot that can encourage stroke patients with high stiffness in arm flexor muscles to receive frequent intensive rehabilitation trainings in the community or home, but its joints are unidirectionally actuated by pneumatic artificial muscles (PAMs). RUPERT with one PAM of each joint is not suitable for stroke patients with weak muscles in the flaccid paralysis period. Functional electrical stimulation (FES) uses current with low frequency to activate paralyzed muscles, which can produce muscle torque and compensate the unidirectional drawbacks of RUPERT, so as to realize the two-way motion of its joints for passive reaching trainings. As both the exoskeleton robot driven by PAMs and neuromuscular skeletal system under FES possess the highly nonlinear and time-varying characteristics, which adds control difficulty to the hybrid dynamic system, iterative learning control (ILC) is chosen to control this newly designed hybrid rehabilitation system to realize repetitive task trainings.     

Improving elbow torque output of stroke patients with assistive torque controlled by EMG signals

This paper develops an assistive torque system which uses homogeneic surface electromyogram (EMG) signals to improve the elbow torque capability of stroke patients by applying an external time-varying assistive torque. In determining the magnitude of the torque to apply, the incorporated assistive torque algorithm considers the difference between the weighted biceps and triceps EMG signals such that the applied torque is proportional to the effort supplied voluntarily by the user. The overall stability of the assistive system is enhanced by the incorporation of a nonlinear damping element within the control algorithm which mimics the physiological damping of the elbow joint and the co-contraction between the biceps and triceps. Adaptive filtering of the control signal is employed to achieve a balance between the bandwidth and the system adaptability so as to ensure a smooth assistive torque output. The innovative control algorithm enables the provision of an assistive system whose operation is both natural to use and simple to learn. The effectiveness of the proposed assistive system in assisting elbow movement performance is investigated in a series of tests involving five stroke patients and five able-bodied individuals. The results confirm the ability of the system to assist all of the subjects in performing a number of reaching and tracking tasks with reduced effort and with no sacrifice in elbow movement performance.     

Normalized Index of Synergy for Evaluating the Coordination of Motor Commands

Humans perform various motor tasks by coordinating the redundant motor elements in their bodies. The coordination of motor outputs is produced by motor commands, as well properties of the musculoskeletal system. The aim of this study was to dissociate the coordination of motor commands from motor outputs. First, we conducted simulation experiments where the total elbow torque was generated by a model of a simple human right and left elbow with redundant muscles. The results demonstrated that muscle tension with signal-dependent noise formed a coordinated structure of trial-to-trial variability of muscle tension. Therefore, the removal of signal-dependent noise effects was required to evaluate the coordination of motor commands. We proposed a method to evaluate the coordination of motor commands, which removed signal-dependent noise from the measured variability of muscle tension. We used uncontrolled manifold analysis to calculate a normalized index of synergy. Simulation experiments confirmed that the proposed method could appropriately represent the coordinated structure of the variability of motor commands. We also conducted experiments in which subjects performed the same task as in the simulation experiments. The normalized index of synergy revealed that the subjects coordinated their motor commands to achieve the task. Finally, the normalized index of synergy was applied to a motor learning task to determine the utility of the proposed method. We hypothesized that a large part of the change in the coordination of motor outputs through learning was because of changes in motor commands. In a motor learning task, subjects tracked a target trajectory of the total torque. The change in the coordination of muscle tension through learning was dominated by that of motor commands, which supported the hypothesis. We conclude that the normalized index of synergy can be used to evaluate the coordination of motor commands independently from the properties of the musculoskeletal system.     

Learning to walk with a robotic ankle exoskeleton

We used a lower limb robotic exoskeleton controlled by the wearer's muscle activity to study human locomotor adaptation to disrupted muscular coordination. Ten healthy subjects walked while wearing a pneumatically powered ankle exoskeleton on one limb that effectively increased plantar flexor strength of the soleus muscle. Soleus electromyography amplitude controlled plantar flexion assistance from the exoskeleton in real time. We hypothesized that subjects' gait kinematics would be initially distorted by the added exoskeleton power, but that subjects would reduce soleus muscle recruitment with practice to return to gait kinematics more similar to normal. We also examined the ability of subjects to recall their adapted motor pattern for exoskeleton walking by testing subjects on two separate sessions, 3 days apart. The mechanical power added by the exoskeleton greatly perturbed ankle joint movements at first, causing subjects to walk with significantly increased plantar flexion during stance. With practice, subjects reduced soleus recruitment by approximately 35% and learned to use the exoskeleton to perform almost exclusively positive work about the ankle. Subjects demonstrated the ability to retain the adapted locomotor pattern between testing sessions as evidenced by similar muscle activity, kinematic and kinetic patterns between the end of the first test day and the beginning of the second. These results demonstrate that robotic exoskeletons controlled by muscle activity could be useful tools for testing neural mechanisms of human locomotor adaptation.     

Joint kinetic response during unexpectedly reduced plantar flexor torque provided by a robotic ankle exoskeleton during walking

During human walking, plantar flexor activation in late stance helps to generate a stable and economical gait pattern. Because plantar flexor activation is highly mediated by proprioceptive feedback, the nervous system must modulate reflex pathways to meet the mechanical requirements of gait. The purpose of this study was to quantify ankle joint mechanical output of the plantar flexor stretch reflex response during a novel unexpected gait perturbation. We used a robotic ankle exoskeleton to mechanically amplify the ankle torque output resulting from soleus muscle activation. We recorded lower-body kinematics, ground reaction forces, and electromyography during steady-state walking and during randomly perturbed steps when the exoskeleton assistance was unexpectedly turned off. We also measured soleus Hoffmann- (H-) reflexes at late stance during the two conditions. Subjects reacted to the unexpectedly decreased exoskeleton assistance by greatly increasing soleus muscle activity about 60 ms after ankle angle deviated from the control condition (p<0.001). There were large differences in ankle kinematic and electromyography patterns for the perturbed and control steps, but the total ankle moment was almost identical for the two conditions (p=0.13). The ratio of soleus H-reflex amplitude to background electromyography was not significantly different between the two conditions (p=0.4). This is the first study to show that the nervous system chooses reflex responses during human walking such that invariant ankle joint moment patterns are maintained during perturbations. Our findings are particularly useful for the development of neuromusculoskeletal computer simulations of human walking that need to adjust reflex gains appropriately for biomechanical analyses.     

Independent control of joint stiffness in the framework of the equilibrium-point hypothesis

In the framework of the equilibrium-point hypothesis, virtual trajectories and joint stiffness patterns have been reconstructed during two motor tasks practiced against a constant bias torque. One task required a voluntary increase in joint stiffness while preserving the original joint position. The other task involved fast elbow flexions over 36°. Joint stiffness gradually subsided after the termination of fast movements. In both tasks, the external torque could slowly and unexpectedly change. The subjects were required not to change their motor commands if the torque changed, i.e. “to do the same no matter what the motor did”. In both tasks, changes in joint stiffness were accompanied by unchanged virtual trajectories that were also independent of the absolute value of the bias torque. By contrast, the intercept of the joint compliant characteristic with the angle axis,r(t)-function, has demonstrated a clear dependence upon both the level of coactivation and external load. We assume that a template virtual trajectory is generated at a certain level of the motor hierarchy and is later scaled taking into account some commonly changing dynamic factors of the movement execution, for example, external load. The scaling leads to the generation of commands to the segmental structures that can be expressed, according to the equilibrium-point hypothesis, as changes in the thresholds of the tonic stretch reflex for corresponding muscles.     

Surface EMG pattern recognition for real-time control of a wrist exoskeleton

Background  

Surface electromyography (sEMG) signals have been used in numerous studies for the classification of hand gestures and movements and successfully implemented in the position control of different prosthetic hands for amputees. sEMG could also potentially be used for controlling wearable devices which could assist persons with reduced muscle mass, such as those suffering from sarcopenia. While using sEMG for position control, estimation of the intended torque of the user could also provide sufficient information for an effective force control of the hand prosthesis or assistive device. This paper presents the use of pattern recognition to estimate the torque applied by a human wrist and its real-time implementation to control a novel two degree of freedom wrist exoskeleton prototype (WEP), which was specifically developed for this work.     

Isokinetic elbow joint torques estimation from surface EMG and joint kinematic data: using an artificial neural network model.

Because the relations between electromyographic signal (EMG) and anisometric joint torque remain unpredictable, the aim of this study was to determine the relations between the EMG activity and the isokinetic elbow joint torque via an artificial neural network (ANN) model. This 3-layer feed-forward network was constructed using an error back-propagation algorithm with an adaptive learning rate. The experimental validation was achieved by rectified, low-pass filtered EMG signals from the representative muscles, joint angle and joint angular velocity and measured torque. Learning with a limited set of examples allowed accurate prediction of isokinetic joint torque from novel EMG activities, joint position, joint angular velocity. Sensitivity analysis of the hidden node numbers during the learning and testing phases demonstrated that the choice of numbers of hidden node was not critical except at extreme values of those parameters. Model predictions were well correlated with the experimental data (the mean root-mean-square-difference and correlation coefficient gamma in learning were 0.0290 and 0.998, respectively, and in three different speed testings were 0.1413 and 0.900, respectively). These results suggested that an ANN model can represent the relations between EMG and joint torque/moment in human isokinetic movements. The effect of different adjacent electrode sites was also evaluated and showed the location of electrodes was very important to produce errors in the ANN model.     

An analytical model for the ergometer rowing: inverse multibody dynamics analysis

A model for the ergometer rowing exercise is presented in this paper. From the quantitative observations of a particular trajectory (motion), the model is used to determine the moment of the forces produced by the muscles about each joint. These forces are evaluated according to the continuous system of equations of motion. An inverse dynamics analysis is performed in order to predict the joint torques developed by the muscles during the execution of the task. An elementary multibody mechanical system is used as an example to discuss the assumptions and procedures adopted.     

Adaptive Control for Passive Kinesiotherapy ELLTIO

Exoskeletons are mechatronic devices used to increase human muscle strength and resistance. In the last decade these devices have become a very useful tool to assist active kinesiotherapy. This paper presents the design of exoskeleton focused on the rehabilitation of ankle and knee for the right leg. The construction of prototype like Exoskeleton for Lower Limb Training with Instrumented Orthoses （ELLTIO） using Series Elastic Actuator （SEA） to reduce the effort in the human joints, and a control law to perform a rehabilitation routine using an adaptive control scheme were first implemented in simulation to verify the control strategy and make a real rehabilitation test. The adaptive control law is proposed with the intention that the exoskeleton can adapt to user parameters at the time when performing the exercise. The results show the parameters estimation and tracking trajectory for the exoskeleton were proposed, and this trajectory could be a routine rehabilitation proposed by the therapist.     

OpenSim Moco: Musculoskeletal optimal control

Musculoskeletal simulations are used in many different applications, ranging from the design of wearable robots that interact with humans to the analysis of patients with impaired movement. Here, we introduce OpenSim Moco, a software toolkit for optimizing the motion and control of musculoskeletal models built in the OpenSim modeling and simulation package. OpenSim Moco uses the direct collocation method, which is often faster and can handle more diverse problems than other methods for musculoskeletal simulation. Moco frees researchers from implementing direct collocation themselves—which typically requires extensive technical expertise—and allows them to focus on their scientific questions. The software can handle a wide range of problems that interest biomechanists, including motion tracking, motion prediction, parameter optimization, model fitting, electromyography-driven simulation, and device design. Moco is the first musculoskeletal direct collocation tool to handle kinematic constraints, which enable modeling of kinematic loops (e.g., cycling models) and complex anatomy (e.g., patellar motion). To show the abilities of Moco, we first solved for muscle activity that produced an observed walking motion while minimizing squared muscle excitations and knee joint loading. Next, we predicted how muscle weakness may cause deviations from a normal walking motion. Lastly, we predicted a squat-to-stand motion and optimized the stiffness of an assistive device placed at the knee. We designed Moco to be easy to use, customizable, and extensible, thereby accelerating the use of simulations to understand the movement of humans and other animals.     

Upper extremity kinematic and kinetic adaptations during a fatiguing repetitive task

Repetitive low-force contractions are common in the workplace and yet can lead to muscle fatigue and work-related musculoskeletal disorders. The current study aimed to investigate potential motion adaptations during a simulated repetitive light assembly work task designed to fatigue the shoulder region, focusing on changes over time and age-related group differences. Ten younger and ten older participants performed four 20-min task sessions separated by short breaks. Mean and variability of joint angles and scapular elevation, joint net moments for the shoulder, elbow, and wrist were calculated from upper extremity kinematics recorded by a motion tracking system. Results showed that joint angle and joint torque decreased across sessions and across multiple joints and segments. Increased kinematic variability over time was observed in the shoulder joint; however, decreased kinematic variability over time was seen in the more distal part of the upper limb. The changes of motion adaptations were sensitive to the task-break schedule. The results suggested that kinematic and kinetic adaptations occurred to reduce the biomechanical loading on the fatigued shoulder region. In addition, the kinematic and kinetic responses at the elbow and wrist joints also changed, possibly to compensate for the increased variability caused by the shoulder joint while still maintaining task requirements. These motion strategies in responses to muscle fatigue were similar between two age groups although the older group showed more effort in adaptation than the younger in terms of magnitude and affected body parts.     

A pilot study investigating motor adaptations when learning to walk with a whole-body powered exoskeleton

Evidence is emerging on how whole-body powered exoskeleton (EXO) use impacts users in basic occupational work scenarios, yet our understanding of how users learn to use this complex technology is limited. We explored how novice users adapted to using an EXO during gait. Six novices and five experienced users completed the study. Novices completed an initial training/familiarization gait session, followed by three subsequent gait sessions using the EXO, while experienced users completed one gait session with the EXO. Spatiotemporal gait measures, pelvis and lower limb joint kinematics, muscle activities, EXO torques, and human-EXO interaction forces were measured. Adaptations among novices were most pronounced in spatiotemporal gait measures, followed by joint kinematics, with smaller changes evident in muscle activity and EXO joint torques. Compared to the experienced users, novices exhibited a shorter step length and walked with significantly greater anterior pelvic tilt and less hip extension. Novices also used lower joint torques from the EXO at the hip and knee, and they had greater biceps femoris activity. Overall, our results may suggest that novices exhibited clear progress in learning, but they had not yet adopted motor strategies similar to those of experienced users after the three sessions. We suggest potential future directions to enhance motor adaptations to powered EXO in terms of both training protocols and human-EXO interfaces.     

Invariant hip moment pattern while walking with a robotic hip exoskeleton

Robotic lower limb exoskeletons hold significant potential for gait assistance and rehabilitation; however, we have a limited understanding of how people adapt to walking with robotic devices. The purpose of this study was to test the hypothesis that people reduce net muscle moments about their joints when robotic assistance is provided. This reduction in muscle moment results in a total joint moment (muscle plus exoskeleton) that is the same as the moment without the robotic assistance despite potential differences in joint angles. To test this hypothesis, eight healthy subjects trained with the robotic hip exoskeleton while walking on a force-measuring treadmill. The exoskeleton provided hip flexion assistance from approximately 33% to 53% of the gait cycle. We calculated the root mean squared difference (RMSD) between the average of data from the last 15 min of the powered condition and the unpowered condition. After completing three 30-min training sessions, the hip exoskeleton provided 27% of the total peak hip flexion moment during gait. Despite this substantial contribution from the exoskeleton, subjects walked with a total hip moment pattern (muscle plus exoskeleton) that was almost identical and more similar to the unpowered condition than the hip angle pattern (hip moment RMSD 0.027, angle RMSD 0.134, p<0.001). The angle and moment RMSD were not different for the knee and ankle joints. These findings support the concept that people adopt walking patterns with similar joint moment patterns despite differences in hip joint angles for a given walking speed.