Little attention has been given to how age affects the neural processing of movement within the brainstem. Since the brainstem plays a critical role in motor control throughout the whole body, having a clear understanding of deficits in brainstem function could provide important insights into movement deficits in older adults. A unique property of the startle reflex is its ability to involuntarily elicit planned movements, a phenomenon referred to as startReact. The noninvasive startReact response has previously been used to probe both brainstem utilization and motor planning. Our objective was to evaluate deficits in startReact hand extension movements in older adults. We hypothesized that startReact hand extension will be intact but delayed. Electromyography was recorded from the sternocleidomastoid (SCM) muscle to detect startle and the extensor digitorum communis (EDC) to quantify movement onset in both young (24 ± 1) and older adults (70 ± 11). Subjects were exposed to a startling loud sound when prepared to extend their hand. Trials were split into those where a startle did (SCM+) and did not (SCM-) occur. We found that startReact was intact but delayed in older adults. SCM+ onset latencies were faster than SCM- trials in both the populations, however, SCM+ onset latencies were slower in older adults compared to young (? = 8 msec). We conclude that the observed age-related delay in the startReact response most likely arises from central processing delays within the brainstem.
Human joint impedance is the dynamic relationship between the differential change in the position of a perturbed joint and the corresponding response torque; it is a fundamental property that governs how humans interact with their environments. It is critical to characterize ankle impedance during the stance phase of walking to elucidate how ankle impedance is regulated during locomotion, as well as provide the foundation for future development of natural, biomimetic powered prostheses and their control systems. In this study, ankle impedance was estimated using a model consisting of stiffness, damping and inertia. Ankle torque was well described by the model, accounting for 98 ±1.2% of the variance. When averaged across subjects, the stiffness component of impedance was found to increase linearly from 1.5 to 6.5 Nm/rad/kg between 20% and 70% of stance phase. The damping component was found to be statistically greater than zero only for the estimate at 70% of stance phase, with a value of 0.03 Nms/rad/kg. The slope of the ankle's torque-angle curve-known as the quasi-stiffness-was not statistically different from the ankle stiffness values, and showed remarkable similarity. Finally, using the estimated impedance, the specifications for a biomimetic powered ankle prosthesis were introduced that would accurately emulate human ankle impedance during locomotion.
We investigated how multi-joint changes in static upper limb posture impact the corticomotor excitability of the posterior deltoid (PD) and biceps brachii (BIC), and evaluated whether postural variations in excitability related directly to changes in target muscle length.
A startling loud acoustic stimulus can involuntarily elicit planned movements, a phenomenon referred to as startReact. Following stroke, startReact elbow flexion in stroke survivors are improved from voluntary movements. Specifically, startReact elbow flexion in unimpaired individuals is not statistically different from stroke survivors in terms of onset latency and muscle activation patterns. As hand movements are particularly impacted by stroke, our objective was to determine if startReact was intact in the hand following stroke.
The startle reflex elicits involuntary release of planned movements (startReact). Following stroke, startReact flexion movements are intact but startReact extension movements are impaired by task-inappropriate flexor activity impeding arm extension. Our objective was to quantify deficits in startReact elbow extension movements, particularly how these deficits are influenced by impairment.
Cervical spinal cord injury (SCI) paralyzes muscles of the hand and arm, making it difficult to perform activities of daily living. Restoring the ability to reach can dramatically improve quality of life for people with cervical SCI. Any reaching system requires a user interface to decode parameters of an intended reach, such as trajectory and target. A challenge in developing such decoders is that often few physiological signals related to the intended reach remain under voluntary control, especially in patients with high cervical injuries. Furthermore, the decoding problem changes when the user is controlling the motion of their limb, as opposed to an external device. The purpose of this study was to investigate the benefits of combining disparate signal sources to control reach in people with a range of impairments, and to consider the effect of two feedback approaches. Subjects with cervical SCI performed robot-assisted reaching, controlling trajectories with either shoulder electromyograms (EMGs) or EMGs combined with gaze. We then evaluated how reaching performance was influenced by task-related sensory feedback, testing the EMG-only decoder in two conditions. The first involved moving the arm with the robot, providing congruent sensory feedback through their remaining sense of proprioception. In the second, the subjects moved the robot without the arm attached, as in applications that control external devices. We found that the multimodal-decoding algorithm worked well for all subjects, enabling them to perform straight, accurate reaches. The inclusion of gaze information, used to estimate target location, was especially important for the most impaired subjects. In the absence of gaze information, congruent sensory feedback improved performance. These results highlight the importance of proprioceptive feedback, and suggest that multi-modal decoders are likely to be most beneficial for highly impaired subjects and in tasks where such feedback is unavailable.
Prosthetic devices need to be controlled by their users, typically using physiological signals. People tend to look at objects before reaching for them and we have shown that combining eye movements with other continuous physiological signal sources enhances control. This approach suffers when subjects also look at non-targets, a problem we addressed with a probabilistic mixture over targets where subject gaze information is used to identify target candidates. However, this approach would be ineffective if a user wanted to move towards targets that have not been foveated. Here we evaluated how the accuracy of prior target information influenced decoding accuracy, as the availability of neural control signals was varied. We also considered a mixture model where we assumed that the target may be foveated or, alternatively, that the target may not be foveated. We tested the accuracy of the models at decoding natural reaching data, and also in a closed-loop robot-assisted reaching task. The mixture model worked well in the face of high target uncertainty. Furthermore, errors due to inaccurate target information were reduced by including a generic model that relied on neural signals only.
Many functional tasks require regulating appropriate forces or torques even under unpredictable disturbances. However, how this regulation can be achieved remains poorly understood. Limb impedance describes the relationship between externally imposed displacements to the limb and the changes in force or torque generated in response. Low limb impedance is preferred during torque regulation tasks. However, low-frequency impedance increases with muscle activation, which is counterproductive to torque regulation. The purpose of this study was to quantify the ability to voluntarily reduce limb impedance during torque regulation tasks, and to assess if the observed performance is near optimal given the challenges posed by activation-dependent muscle properties and time delays in the neuromuscular system. By examining elbow impedance measured in experiments and predicted by a biomechanical model with an optimal controller, our results demonstrated that individuals can reduce the low-frequency components (below 1Hz) of elbow impedance during forceful contractions, and that this performance is similar to those predicted by an optimal feedback controller. These findings suggest that neural feedback can compensate for intrinsic muscle properties in a near-optimal manner, thereby allowing torque to be regulated at frequencies below ? 1 Hz.
Functional electrical stimulation (FES) can be used to restore movement control following paralysis. For complex multijoint systems, it is becoming increasingly apparent that closed-loop controllers are needed. Designing a closed-loop control system is easiest when the open-loop system is stable. In this study we developed a computational model to assess the open-loop stability of FES-control systems. We used the model to examine the open-loop stability of the human arm throughout its reachable workspace. For each simulated position of the hand we examined the stability of the arm, assuming that a minimal pattern of muscle activation was used to support the arm against gravity. Only muscles available to an existing FES user were considered. We found that with this reduced muscle set, the stability of the arm was severely compromised. We also demonstrated that muscle co-contraction can be an effective method to improve the stability for many postures.
We present a method for controlling a neuroprosthesis for a paralyzed human arm using functional electrical stimulation (FES) and characterize the errors of the controller. The subject has surgically implanted electrodes for stimulating muscles in her shoulder and arm. Using input/output data, a model mapping muscle stimulations to isometric endpoint forces measured at the subjects hand was identified. We inverted the model of this redundant and coupled multiple-input multipleoutput system by minimizing muscle activations and used this inverse for feedforward control. The magnitude of the total RMS error over a grid in the volume of achievable isometric endpoint force targets was 11% of the total range of achievable forces. Major sources of error were random error due to trialto- trial variability and model bias due to nonstationary system properties. Because the muscles working collectively are the actuators of the skeletal system, the quantification of errors in force control guides designs of motion controllers for multi-joint, multi-muscle FES systems that can achieve arbitrary goals.
The reticulospinal tract was recently shown to have synaptic connections to the intrinsic muscles of the fingers in nonhuman primates, indicating it may contribute to hand function long thought to be controlled exclusively through corticospinal pathways. Our objective was to obtain evidence supporting the hypothesis that these same anatomical connections exist in humans. startReact, an involuntary release of a planned movement via the startle reflex, provides a noninvasive means to examine the reticulospinal tract in humans. We found that startReact was triggered during coordinated grasp but not individuated finger movements. This result suggests that the reticulospinal tract does have connections to the intrinsic muscles of the fingers in humans but its functional role is limited to coordinated movement of the whole hand. These results do not diminish the well-established role of corticospinal pathways in the control of hand movement. Indeed, they cement the significance of corticospinal pathways in individuated finger movement control. Still, these results point to an updated and expanded view of distal hand control where reticulospinal and corticospinal pathways work in parallel to generate a large repertoire of diverse, coordinated movement in the hand. Finally, the presence of reticulospinal pathways to the muscles of the hand makes this pathway an attractive therapeutic target for clinical populations where the corticospinal tract is absent or injured.
Involuntary responses to muscle stretch are often composed of a short-latency reflex (SLR) and more variable responses at longer latencies such as the medium-latency (MLR) and long-latency stretch reflex (LLR). Although longer latency reflexes are enhanced in the upper limb during stabilization of external loads, it remains unknown if they have a similar role in the lower limb. This uncertainty results in part from the inconsistency with which longer latency reflexes have been observed in the lower limb. A review of the literature suggests that studies that only observe SLRs have used perturbations with large accelerations, possibly causing a synchronization of motoneuron refractory periods or an activation of force-dependent inhibition. We therefore hypothesized that the amplitude of longer latency reflexes would vary with perturbation acceleration. We further hypothesized that if longer latency reflexes were elicited, they would increase in amplitude during control of an unstable load, as has been observed in the upper limb. These hypotheses were tested at the ankle while subjects performed a torque or position control task. SLR and MLR reflex components were elicited by ankle flexion perturbations with a fixed peak velocity and variable acceleration. Both reflex components initially scaled with acceleration, however, while the SLR continued to increase at high accelerations, the MLR weakened. At accelerations that reliably elicited MLRs, both the SLR and MLR were reduced during control of the unstable load. These findings clarify the conditions required to elicit MLRs in the ankle extensors and provide additional evidence that rapid feedback pathways are downregulated when stability is compromised in the lower limb.
The mechanical properties of human joints (i.e., impedance) are constantly modulated to precisely govern human interaction with the environment. The estimation of these properties requires the displacement of the joint from its intended motion and a subsequent analysis to determine the relationship between the imposed perturbation and the resultant joint torque. There has been much investigation into the estimation of upper-extremity joint impedance during dynamic activities, yet the estimation of ankle impedance during walking has remained a challenge. This estimation is important for understanding how the mechanical properties of the human ankle are modulated during locomotion, and how those properties can be replicated in artificial prostheses designed to restore natural movement control. Here, we introduce a mechatronic platform designed to address the challenge of estimating the stiffness component of ankle impedance during walking, where stiffness denotes the static component of impedance. The system consists of a single degree of freedom mechatronic platform that is capable of perturbing the ankle during the stance phase of walking and measuring the response torque. Additionally, we estimate the platforms intrinsic inertial impedance using parallel linear filters and present a set of methods for estimating the impedance of the ankle from walking data. The methods were validated by comparing the experimentally determined estimates for the stiffness of a prosthetic foot to those measured from an independent testing machine. The parallel filters accurately estimated the mechatronic platforms inertial impedance, accounting for 96% of the variance, when averaged across channels and trials. Furthermore, our measurement system was found to yield reliable estimates of stiffness, which had an average error of only 5.4% (standard deviation: 0.7%) when measured at three time points within the stance phase of locomotion, and compared to the independently determined stiffness values of the prosthetic foot. The mechatronic system and methods proposed in this study are capable of accurately estimating ankle stiffness during the foot-flat region of stance phase. Future work will focus on the implementation of this validated system in estimating human ankle impedance during the stance phase of walking.
Injuries of the cervical spinal cord can interrupt the neural pathways controlling the muscles of the arm, resulting in complete or partial paralysis. For individuals unable to reach due to high-level injuries, neuroprostheses can restore some of the lost function. Natural, multidimensional control of neuroprosthetic devices for reaching remains a challenge. Electromyograms (EMGs) from muscles that remain under voluntary control can be used to communicate intended reach trajectories, but when the number of available muscles is limited control can be difficult and unintuitive. We combined shoulder EMGs with target estimates obtained from gaze. Natural gaze data were integrated with EMG during closed-loop robotic control of the arm, using a probabilistic mixture model. We tested the approach with two different sets of EMGs, as might be available to subjects with C4- and C5-level spinal cord injuries. Incorporating gaze greatly improved control of reaching, particularly when there were few EMG signals. We found that subjects naturally adapted their eye-movement precision as we varied the set of available EMGs, attaining accurate performance in both tested conditions. The system performs a near-optimal combination of both physiological signals, making control more intuitive and allowing a natural trajectory that reduces the burden on the user.
Long-latency responses elicited by postural perturbation are modulated by how a subject is instructed to respond to the perturbation, yet the neural pathways responsible for this modulation remain unclear. The goal of this study was to determine whether instruction-dependent modulation is associated with activity in brainstem pathways contributing to startle. Our hypothesis was that elbow perturbations can evoked startle, indicated by activity in the sternocleidomastoid muscle (SCM). Perturbation responses were compared to those elicited by a loud acoustic stimulus, known to elicit startle. Postural perturbations and startling acoustic stimuli both evoked SCM activity, but only when a ballistic elbow extension movement was planned. Both stimuli triggered SCM activity with the same probability. When SCM activity was present, there was an associated early onset of triceps electromyographic (EMG), as required for the planned movement. This early EMG onset occurred at a time often attributed to long-latency stretch reflexes (75-100 ms). The nature of the perturbation-triggered EMG (excitatory or inhibitory) was independent of the perturbation direction (flexion or extension) indicating that it was not a feedback response appropriate for returning the limb to its original position. The net EMG response to perturbations delivered after a movement had been planned could be explained as the sum of a stretch reflex opposing the perturbation and a startle-evoked response associated with the prepared movement. These results demonstrate that rapid perturbations can trigger early release of a planned ballistic movement, and that this release is associated with activity in the brainstem pathways contributing to startle reflexes.
This paper summarizes the discussions held during the First IEEE Life Sciences Grand Challenges Conference, held on October 4-5, 2012, at the National Academy of Sciences, Washington, DC, and the grand challenges identified by the conference participants. Despite tremendous efforts to develop the knowledge and ability that are essential in addressing biomedical and health problems using engineering methodologies, the optimization of this approach toward engineering the life sciences and healthcare remains a grand challenge. The conference was aimed at high-level discussions by participants representing various sectors, including academia, government, and industry. Grand challenges were identified by the conference participants in five areas including engineering the brain and nervous system; engineering the cardiovascular system; engineering of cancer diagnostics, therapeutics, and prevention; translation of discoveries to clinical applications; and education and training. A number of these challenges are identified and summarized in this paper.
Modulation of the long-latency reflex (LLR) is important for sensorimotor control during interaction with different mechanical loads. Transcortical pathways usually contribute to LLR modulation, but the integrity of pathways projecting to the paretic and non-paretic arms of stroke survivors is compromised. We hypothesize that disruption of transcortical reflex pathways reduces the capacity for stroke survivors to appropriately regulate the LLR bilaterally.
One theory for how humans control movement is that muscles are activated in weighted groups or synergies. Studies have shown that electromyography (EMG) from a variety of tasks can be described by a low-dimensional space thought to reflect synergies. These studies use algorithms, such as nonnegative matrix factorization, to identify synergies from EMG. Due to experimental constraints, EMG can rarely be taken from all muscles involved in a task. However, it is unclear if the choice of muscles included in the analysis impacts estimated synergies. The aim of our study was to evaluate the impact of the number and choice of muscles on synergy analyses. We used a musculoskeletal model to calculate muscle activations required to perform an isometric upper-extremity task. Synergies calculated from the activations from the musculoskeletal model were similar to a prior experimental study. To evaluate the impact of the number of muscles included in the analysis, we randomly selected subsets of between 5 and 29 muscles and compared the similarity of the synergies calculated from each subset to a master set of synergies calculated from all muscles. We determined that the structure of synergies is dependent upon the number and choice of muscles included in the analysis. When five muscles were included in the analysis, the similarity of the synergies to the master set was only 0.57 ± 0.54; however, the similarity improved to over 0.8 with more than ten muscles. We identified two methods, selecting dominant muscles from the master set or selecting muscles with the largest maximum isometric force, which significantly improved similarity to the master set and can help guide future experimental design. Analyses that included a small subset of muscles also over-estimated the variance accounted for (VAF) by the synergies compared to an analysis with all muscles. Thus, researchers should use caution using VAF to evaluate synergies when EMG is measured from a small subset of muscles.
Knee joint impedance varies substantially during physiological gait. Quantifying this modulation is critical for the design of transfemoral prostheses that aim to mimic physiological limb behavior. Conventional methods for quantifying joint impedance typically involve perturbing the joint in a controlled manner, and describing impedance as the dynamic relationship between applied perturbations and corresponding joint torques. These experimental techniques, however, are difficult to apply during locomotion without impeding natural movements. In this paper, we propose a method to estimate the elastic component of knee joint impedance that depends on muscle activation, often referred to as active knee stiffness. The method estimates stiffness using a musculoskeletal model of the leg and a model for activation-dependent short-range muscle stiffness. Muscle forces are estimated from measurements including limb kinematics, kinetics and muscle electromyograms. For isometric validation, we compare model estimates to measurements involving joint perturbations; measured stiffness is 17% lower than model estimates for extension, and 42% lower for flexion torques. We show that sensitivity of stiffness estimates to common approaches for estimating muscle force is small in isometric conditions. We also make initial estimates of how knee stiffness is modulated during gait, illustrating how this approach may be used to obtain parameters relevant to the design of transfemoral prostheses.
The ease with which persons with upper-limb amputations can control their powered prostheses is largely determined by the efficacy of the user command interface. One needs to understand the abilities of the human operator regarding the different available options. Electromyography (EMG) is widely used to control powered upper-limb prostheses. It is an indirect estimator of muscle force and may be expected to limit the control capabilities of the prosthesis user. This study compared EMG control with force control, an interface that is used in everyday interactions with the environment. We used both methods to perform a position-tracking task. Direct-position control of the wrist provided an upper bound for human-operator capabilities. The results demonstrated that an EMG control interface is as effective as force control for the position-tracking task. We also examined the effects of gain and tracking frequency on EMG control to explore the limits of this control interface. We found that information transmission rates for myoelectric control were best at higher tracking frequencies than at the frequencies previously reported for position control. The results may be useful for the design of prostheses and prosthetic controllers.
Trajectory-based models that incorporate target position information have been shown to accurately decode reaching movements from bio-control signals, such as muscle (EMG) and cortical activity (neural spikes). One major hurdle in implementing such models for neuroprosthetic control is that they are inherently designed to decode single reaches from a position of origin to a specific target. Gaze direction can be used to identify appropriate targets, however information regarding movement intent is needed to determine when a reach is meant to begin and when it has been completed. We used linear discriminant analysis to classify limb states into movement classes based on recorded EMG from a sparse set of shoulder muscles. We then used the detected state transitions to update target information in a mixture of Kalman filters that incorporated target position explicitly in the state, and used EMG activity to decode arm movements. Updating the target position initiated movement along new trajectories, allowing a sequence of appropriately timed single reaches to be decoded in series and enabling highly accurate continuous control.
We tend to look at targets prior to moving our hand towards them. This means that our eye movements contain information about the movements we are planning to make. This information has been shown to be useful in the context of decoding of movement intent from neural signals. However, this is complicated by the fact that occasionally, subjects may want to move towards targets that have not been foveated, or may be distracted and temporarily look away from the intended target. We have previously accounted for this uncertainty using a probabilistic mixture over targets, where the gaze information is used to identify target candidates. Here we evaluate how the accuracy of prior target information influences decoding accuracy. We also consider a mixture model where we assume that the target may be foveated or, alternatively, that the target may not be foveated. We found that errors due to inaccurate target information were reduced by including a generic model representing movements to all targets into the mixture.
Dynamic joint stiffness defines the dynamic relationship between the position of the joint and the torque acting about it; hence it is important in the control of movement and posture. Joint stiffness consists of two components: intrinsic stiffness and reflex stiffness. Measuring intrinsic and reflex torques directly is not possible, thus estimating intrinsic and reflex stiffness is challenging. A further complication is that both intrinsic and reflex stiffness vary with joint position and torque. Thus, the measurement of dynamic joint stiffness during movement requires a time-varying algorithm. Recently we described an algorithm to estimate time-varying intrinsic and reflex stiffness and demonstrated its application. This paper describes modifications to that algorithm that significantly improves the accuracy of the estimates it generates while increasing its computational efficiency by a factor of seven.
Joint impedance is an important property of the human muscular system and plays a role in the control of movement and posture. Previous studies showed that joint impedance varies with the position of the joint and activation level of the surrounding muscles; however, it remains unknown how it varies during movement. Non-parametric algorithms that estimate time-varying impedance do exist; however these algorithms require hundreds of realizations of the same time-varying behavior. In this paper we develop a non-parametric algorithm that can estimate slowly time-varying impedance using multiple short data segments. Using simulated data we evaluate the desired data segment length and the number of realizations needed to yield accurate estimates.
Machine learning methods for interfacing humans with machines is an emerging area. Here we propose a novel algorithm for interfacing humans with powered lower limb prostheses for restoring control of naturalistic gait following amputation. Unlike most previous neural machine interfaces, our approach fuses control information from the user with sensor information from the prosthesis to approximate the closed loop behavior of the unimpaired sensorimotor system. We present a Bayesian framework to control an artificial knee by probabilistically mixing of process state estimates from different Kalman filters, each addressing separate regimes of locomotion such as level ground walking, walking up a ramp, and walking down a ramp. We show its utility as a mode classifier that is tolerant to temporary sensor faults which are frequently experienced in practical applications.
Cognitively challenging training sessions during robot-assisted gait training after stroke were shown to be key requirements for the success of rehabilitation. Despite a broad variability of cognitive impairments amongst the stroke population, current rehabilitation environments do not adapt to the cognitive capabilities of the patient, as cognitive load cannot be objectively assessed in real-time. We provided healthy subjects and stroke patients with a virtual task during robot-assisted gait training, which allowed modulating cognitive load by adapting the difficulty level of the task. We quantified the cognitive load of stroke patients by using psychophysiological measurements and performance data. In open-loop experiments with healthy subjects and stroke patients, we obtained training data for a linear, adaptive classifier that estimated the current cognitive load of patients in real-time. We verified our classification results via questionnaires and obtained 88% correct classification in healthy subjects and 75% in patients. Using the pre-trained, adaptive classifier, we closed the cognitive control loop around healthy subjects and stroke patients by automatically adapting the difficulty level of the virtual task in real-time such that patients were neither cognitively overloaded nor under-challenged.
The nervous system can regulate the mechanical properties of the human ankle through feed-forward mechanisms such as co-contraction and rapid feedback mechanisms such as stretch reflexes. Though each of these strategies may contribute to joint stability, it is unclear how their relative contribution varies when ankle stability is threatened. We addressed this question by characterizing co-contraction and stretch reflexes during balance of an inverted pendulum simulated by a rotary motor configured as an admittance servo. The stability of this haptic environment was manipulated by varying the stiffness of a virtual spring supporting the pendulum. We hypothesized that co-contraction and stretch reflex amplitude would increase as the stability of the haptic load attached to the ankle was reduced. Electromyographic activity in soleus, medial and lateral gastrocnemius, and tibialis anterior was used to characterize co-contraction patterns and stretch reflex amplitude as subjects stabilized the haptic load. Our results revealed that co-contraction was heightened as stability was reduced, but that the resulting joint stiffness was not sufficient to fully counteract the imposed instability. Reflex amplitude, in comparison, was attenuated as load stability was reduced, contrary to results from upper limb studies using similar paradigms. Together these findings suggest that the nervous system utilizes feed-forward co-contraction rather than rapid involuntary feedback to increase ankle stability during simple balance tasks. Furthermore, since the stiffness generated through co-contraction was not sufficient to fully balance the haptic load, our results suggest an important role for slower, volitional feedback in the control of ankle stability during balancing tasks.
The mechanical properties of the human arm are regulated to maintain stability across many tasks. The static mechanics of the arm can be characterized by estimates of endpoint stiffness, considered especially relevant for the maintenance of posture. At a fixed posture, endpoint stiffness can be regulated by changes in muscle activation, but which activation-dependent muscle properties contribute to this global measure of limb mechanics remains unclear. We evaluated the role of muscle properties in the regulation of endpoint stiffness by incorporating scalable models of muscle stiffness into a three-dimensional musculoskeletal model of the human arm. Two classes of muscle models were tested: one characterizing short-range stiffness and two estimating stiffness from the slope of the force-length curve. All models were compared with previously collected experimental data describing how endpoint stiffness varies with changes in voluntary force. Importantly, muscle properties were not fit to the experimental data but scaled only by the geometry of individual muscles in the model. We found that force-dependent variations in endpoint stiffness were accurately described by the short-range stiffness of active arm muscles. Over the wide range of evaluated arm postures and voluntary forces, the musculoskeletal model incorporating short-range stiffness accounted for 98 ± 2, 91 ± 4, and 82 ± 12% of the variance in stiffness orientation, shape, and area, respectively, across all simulated subjects. In contrast, estimates based on muscle force-length curves were less accurate in all measures, especially stiffness area. These results suggest that muscle short-range stiffness is a major contributor to endpoint stiffness of the human arm. Furthermore, the developed model provides an important tool for assessing how the nervous system may regulate endpoint stiffness via changes in muscle activation.
The force and position data used to construct models of limb impedance are often obtained from closed-loop experiments. If the system is tested in a stiff environment, it is possible to treat the data as if they were obtained in open loop. However, when limb impedance is studied in a compliant environment, the presence of feedback cannot be ignored. While unbiased estimates of a system can be obtained directly using the prediction error method, the same cannot be said when linear regression or correlation analysis is used to fit nonparametric time- or frequency-domain models. We develop a prediction error minimization-based identification method for a nonparametric time-domain model augmented with a parametric noise model. The identification algorithm is tested on a dynamic mass-spring-damper system and returns consistent estimates of the systems properties under both stiff and compliant feedback control. The algorithm is then used to estimate the impedance of a human elbow joint in both stiff and compliant environments.
Appropriate regulation of human arm mechanics is essential for completing the diverse range of tasks we accomplish each day. The steady state mechanical properties of the arm most relevant for postural tasks can be characterized by endpoint stiffness, the static forces generated by a limb in response to external perturbations of posture. Endpoint stiffness is directional, resisting perturbations in certain directions more than others. It has been shown that humans can voluntarily control the orientation of the maximum stiffness to meet specific task requirements, although the limits on this control are poorly understood. Both neural and biomechanical factors may limit endpoint stiffness control. The purpose of this work was to quantify the biomechanical constraints limiting the control of stiffness orientation. A realistic musculoskeletal model of the human arm coupled with a model of muscle stiffness was used to explore the range of endpoint stiffness orientations that could be achieved with changes in the feedforward control of muscle activation. We found that this range is constrained by the biomechanics of the neuromuscular system, and by the requirements of the specific task being performed by the subject. These constraints and the sensitivity to experimental conditions may account for some of the discrepancies in the literature regarding the ability to control endpoint stiffness orientation.
Patterns of stereotyped muscle coactivation, clinically referred to as synergies, emerge following stroke and impair arm function. Although researchers have focused on cortical contributions, there is growing evidence that altered stretch reflex pathways may also contribute to impairment. However, most previous reflex studies have focused on passive, single-joint movements without regard to their coordination during volitional actions. The purpose of this study was to examine the effects of stroke on coordinated activity of stretch reflexes elicited in multiple arm muscles following multijoint perturbations. We hypothesized that cortical injury results in increased stretch reflexes of muscles characteristic of the abnormal flexor synergy during active arm conditions. To test this hypothesis, we used a robot to apply position perturbations to impaired arms of 10 stroke survivors and dominant arms of 8 healthy age-matched controls. Corresponding reflexes were assessed during volitional contractions simulating different levels of gravitational support, as well as during voluntary flexion and extension of the elbow and shoulder. Reflexes were quantified by average rectified surface electromyogram, recorded from eight muscles spanning the elbow and shoulder. Reflex coordination was quantified using an independent components analysis. We found stretch reflexes elicited in the stroke group were significantly less sensitive to changes in background muscle activation compared with those in the control group (P < 0.05). We also observed significantly increased reflex coupling between elbow flexor and shoulder abductor-extensor muscles in stroke subjects relative to that in control subjects. This increased coupling was present only during volitional tasks that required elbow flexion (P < 0.001), shoulder extension (P < 0.01), and gravity opposition (P < 0.01), but not during the "no load" condition. During volitional contractions, reflex amplitudes scaled with the level of impairment, as assessed by Fugl-Meyer scores (r(2) = 0.63; P < 0.05). We conclude that altered reflex coordination is indicative of motor impairment level and may contribute to impaired arm function following stroke.
Simultaneous contraction of agonist and antagonist muscles acting about a joint influences joint stiffness and stability. Although several studies have shown that reflexes in the muscle lengthened by a joint perturbation are modulated during co-contraction, little attention has been given to reflex regulation in the antagonist (shortened) muscle. The goal of the present study was to determine whether co-contraction gives rise to altered reflex regulation across the joint by examining reflexes in the muscle shortened by a joint perturbation. Reflexes were recorded from electromyographic activity in elbow flexors and extensors while positional perturbations to the elbow joint were applied. Perturbations were delivered during isolated activation of the flexor or extensor muscles as well as during flexor and extensor co-contraction. Across the group, the shortening reflex in the elbow extensor switched from suppression during isolated extensor muscle activation to facilitation during co-contraction. The shortening reflex in the elbow flexor remained suppressive during co-contraction but was significantly smaller compared to the response obtained during isolated elbow flexor activation. This response in the shortened muscle was graded by the level of activation in the lengthened muscle. The lengthening reflex did not change during co-contraction. These results support the idea that reflexes are regulated across multiple muscles around a joint. We speculate that the facilitatory response in the shortened muscle arises through a fast-conducting oligosynaptic pathway involving Ib interneurons.
The often studied stretch reflex is fundamental to the involuntary control of posture and movement. Nevertheless, there remains controversy regarding its functional role. Many studies have demonstrated that stretch reflexes can be modulated in a task appropriate manner. This review focuses on modulation of the long-latency stretch reflex, thought to be mediated, at least in part, by supraspinal pathways. For example, this component of the stretch reflex increases in magnitude during interactions with compliant environments, relative to its sensitivity during interactions with rigid environments. This suggests that reflex sensitivity increases to augment limb stability when that stability is not provided by the environment. However, not all results support the stabilizing role of stretch reflexes. Some studies have demonstrated that involuntary responses within the time period corresponding to the long-latency reflex can destabilize limb posture. We propose that this debate stems from the fact that multiple perturbation-sensitive pathways can contribute to the long-latency stretch reflex and that these pathways have separate functional roles. The presented studies suggest that neural activity occurring within the period normally ascribed to the long-latency stretch reflex is highly adaptable to current task demands and possibly should be considered more intelligent than "reflexive".
The dynamics of sway during quiet stance have often been approximated by the movement of an unstable inverted pendulum. Controlling this unstable load requires the nervous system to balance the reliance on feed-forward volitional activation and feedback mechanisms such as stretch reflexes. It has been demonstrated that reflex excitability is heightened when postural stability is threatened by destabilizing forces in the environment. However, the relationship between postural stability, volitional activation, and stretch reflex excitability remains unclear. We addressed this question by characterizing feed-forward and feedback activation strategies during balance of a simulated inverted pendulum. We hypothesized that feed-forward co-contraction and stretch reflex amplitude would scale together as the external support provided by the environment was reduced. Electromyographic (EMG) responses in 5 muscles of the lower limb were used to characterize co-contraction patterns and stretch reflex amplitude as subjects stabilized the simulated loads. Our results revealed that co-contraction magnitude did indeed scale with increasingly destabilizing torques; however reflex amplitude was attenuated as stability was reduced. These findings suggest that the contribution of feedback mechanisms to postural stability depends on both the level of stability provided by the environment and how the environment influences the pattern of volitional activation.
Using the upper limb to manipulate objects or tools requires maintenance of stable arm posture. The ability to maintain stable postures is dependent on the mechanical properties of the arm, which can be characterized by estimates of endpoint stiffness. In this study we quantified the endpoint stiffness of the human arm during postural interactions with mechanically imposed unstable loads. The purpose was to determine the extent to which arm stiffness is adapted according to the mechanical properties of the environment during postural tasks. We estimated the endpoint stiffness of the right arms of eight subjects as they interacted with four haptic environments: rigid, unstable along the direction of maximal endpoint stiffness and orthogonal to this direction, and a high-strength unstable environment also aligned to the orientation of maximal endpoint stiffness. The size and orientation of endpoint stiffness were quantified for each haptic condition. Stiffness size was increased along the directions of the destabilizing environments (p<0.003). However, the environments had no significant effect on stiffness orientation (p>0.26). These findings suggest that at a fixed posture interactions with unstable environments can induce moderate, task-appropriate changes in limb mechanics that are tuned to the environment. However, these changes are small relative to those that can be obtained by changing limb posture.
Stretch reflexes have been considered one of the simplest circuits in the human nervous system. Yet, their role is controversial given that they assist or resist an imposed perturbation depending on the task instruction. Evidence shows that a loud acoustic stimulus applied prior to an impending movement elicits a movement-direction dependent muscle activity. In our study, we found that a perturbation can also trigger this early onset of movement, if applied during movement preparation. These responses were also perturbation direction dependent. This suggests an interaction of between the limb-stabilizing stretch reflexes and the voluntary activity.
Stretch reflexes contribute to arm impedance and longer-latency stretch reflexes exhibit increased sensitivity during interactions with compliant or unstable environments. This increased sensitivity is consistent with a regulation of arm impedance to compensate for decreased stability of the environment, but the specificity of this modulation has yet to be investigated. Many tasks, such as tool use, compromise arm stability along specific directions, and stretch reflexes tuned to those directions could present an efficient mechanism for regulating arm impedance in a task-appropriate manner. To be effective, such tuning should adapt not only to the mechanical properties of the environment but to those properties in relation to the arm, which also has directionally specific mechanical properties. The purpose of this study was to investigate the specificity of stretch reflex modulation during interactions with mechanical environments that challenge arm stability. The tested environments were unstable, having the characteristics of a negative stiffness spring. These were either aligned or orthogonal to the direction of maximal endpoint stiffness for each subject. Our results demonstrate preferential increases in reflexes, elicited within 50-100 ms of perturbation onset, to perturbations applied specifically along the direction of the destabilizing environments. This increase occurred only when the magnitude of the environmental instability exceeded endpoint stiffness along the same direction. These results are consistent with task-specific reflex modulation tuned to the mechanical properties of the environment relative to those of the human arm. They demonstrate a highly adaptable, involuntary mechanism that may be used to modulate limb impedance along specific directions.
The motor cortex assumes an increasingly important role in higher mammals relative to that in lower mammals. This is true to such an extent that the human motor cortex is deeply involved in reflex regulation and it is common to speak of "transcortical reflex loops." Such loops appear to add flexibility to the human stretch reflex, once considered to be immutable, allowing it to adapt across a range of functional tasks. However, the purpose of this adaptation remains unclear. A common proposal is that stretch reflexes contribute to the regulation of limb stability; increased reflex sensitivity during tasks performed in unstable environments supports this hypothesis. Alternatively, before movement onset, stretch reflexes can assist an imposed stretch, opposite to what would be expected from a stabilizing response. Here we show that stretch reflex modulation in tasks that require changes in limb stability is mediated by motor cortical pathways, and that these differ from pathways contributing to reflex modulation that depend on how the subject is instructed to react to an imposed perturbation. By timing muscle stretches such that the modulated portion of the reflex occurred within a cortical silent period induced by transcranial magnetic stimulation, we abolished the increase in reflex sensitivity observed when individuals stabilized arm posture within a compliant environment. Conversely, reflex modulation caused by altered task instruction was unaffected by cortical silence. These results demonstrate that task-dependent changes in reflex function can be mediated through multiple neural pathways and that these pathways have task-specific roles.
Robot-assisted training is increasingly being investigated in upper limb rehabilitation for individuals with stroke. Many studies have suggested that an appropriate synchronization of voluntary motor commands and limb movement is critical for long-term efficacy. Bimanual training is one method for enhancing this synchronization or motor coordination. The purpose of the study was to evaluate the potential efficacy of bimanual robot-assisted movements by comparing the relative timing of muscle activation and forces to those generated during unimanual robot-assisted movement. A secondary goal was to compare bimanual robot-assisted movement to bimanual voluntary movement, where both limbs moved independently without robotics. Subjects performed reaching tasks while attached to one or two robotic manipulators. A predefined movement trajectory was prescribed during unimanual robot-assisted movement; in bimanual robot-assisted movement the paretic limb trajectory mirrored the nonparetic limb. Relative to unimanual movements, during bimanual movements the timing of muscle activation and initial interface forces was more similar to the nonparetic limb. However, there were limited differences in these measures between bimanual voluntary and bimanual robot-assisted movements. Bimanual robot-assisted movements resulted in superior motor coordination compared to unimanual movements and could be beneficial for individuals with a restricted movement range. Bimanual movements without robotics were just as efficacious and may be preferred for individuals who can generate movement without assistance.
Loss of hand use is considered by many spinal cord injury survivors to be the most devastating consequence of their injury. Functional electrical stimulation (FES) of forearm and hand muscles has been used to provide basic, voluntary hand grasp to hundreds of human patients. Current approaches typically grade pre-programmed patterns of muscle activation using simple control signals, such as those derived from residual movement or muscle activity. However, the use of such fixed stimulation patterns limits hand function to the few tasks programmed into the controller. In contrast, we are developing a system that uses neural signals recorded from a multi-electrode array implanted in the motor cortex; this system has the potential to provide independent control of multiple muscles over a broad range of functional tasks. Two monkeys were able to use this cortically controlled FES system to control the contraction of four forearm muscles despite temporary limb paralysis. The amount of wrist force the monkeys were able to produce in a one-dimensional force tracking task was significantly increased. Furthermore, the monkeys were able to control the magnitude and time course of the force with sufficient accuracy to track visually displayed force targets at speeds reduced by only one-third to one-half of normal. Although these results were achieved by controlling only four muscles, there is no fundamental reason why the same methods could not be scaled up to control a larger number of muscles. We believe these results provide an important proof of concept that brain-controlled FES prostheses could ultimately be of great benefit to paralyzed patients with injuries in the mid-cervical spinal cord.
The human motor system is highly redundant, having more kinematic degrees of freedom than necessary to complete a given task. Understanding how kinematic redundancies are utilized in different tasks remains a fundamental question in motor control. One possibility is that they can be used to tune the mechanical properties of a limb to the specific requirements of a task. For example, many tasks such as tool usage compromise arm stability along specific directions. These tasks only can be completed if the nervous system adapts the mechanical properties of the arm such that the arm, coupled to the tool, remains stable. The purpose of this study was to determine if posture selection is a critical component of endpoint stiffness regulation during unconstrained tasks.
Recent experiments to characterize the short-range stiffness (SRS)-force relationship in several cat hindlimb muscles suggested that the there are differences in the tendon elastic moduli across muscles [Cui, L., Perreault, E.J., Maas, H., Sandercock, T.G., 2008. Modeling short-range stiffness of feline lower hindlimb muscles. J. Biomech. 41 (9), 1945-1952.]. Those conclusions were inferred from whole muscle experiments and a computational model of SRS. The present study sought to directly measure tendon elasticity, the material property most relevant to SRS, during physiological loading to confirm the previous modeling results. Measurements were made from the medial gastrocnemius (MG), tibialis anterior (TA) and extensor digitorum longus (EDL) muscles during loading. For the latter, the model indicated a substantially different elastic modulus than for MG and TA. For each muscle, the stress-strain relationship of the external tendon was measured in situ during the loading phase of isometric contractions conducted at optimum length. Youngs moduli were assessed at equal strain levels (1%, 2% and 3%), as well as at peak strain. The stress-strain relationship was significantly different between EDL and MG/TA, but not between MG and TA. EDL had a more apparent toe region (i.e., lower Youngs modulus at 1% strain), followed by a more rapid increase in the slope of the stress-strain curve (i.e., higher Youngs modulus at 2% and 3% strain). Youngs modulus at peak strain also was significantly higher in EDL compared to MG/TA, whereas no significant difference was found between MG and TA. These results indicate that during natural loading, tendon Youngs moduli can vary considerably across muscles. This creates challenges to estimating muscle behavior in biomechanical models for which direct measures of tendon properties are not available.
The mechanical properties of the joint influence how we interact with our environment and hence are important in the control of both posture and movement. Many studies have investigated how the mechanical properties-specifically the impedance-of different joints vary with different postural tasks. However, studies on how joint impedance varies with movement remain limited. The few studies that have investigated how impedance varies with movement have found that impedance is lower during movement than during posture. In this study we investigated how impedance changed as people transitioned from a postural task to a movement task. We found that subjects joint impedances decreased at the initiation of movement, prior to increasing at the cessation of movement. This decrease in impedance occurred even though the subjects torque and EMG levels increased. These findings suggest that during movement the central nervous system may control joint impedance independently of muscle activation.
For rehabilitative devices to restore functional movement to paralyzed individuals, user intent must be determined from signals that remain under voluntary control. Tracking eye movements is a natural way to learn about an intended reach target and, when combined with just a small set of electromyograms (EMGs) in a probabilistic mixture model, can reliably generate accurate trajectories even when the target information is uncertain. To experimentally assess the effectiveness of our algorithm in closed-loop control, we developed a robotic system to simulate a reaching neuroprosthetic. Incorporating target information by tracking subjects gaze greatly improved performance when the set of EMGs was most limited. In addition we found that online performance was better than predicted by the offline accuracy of the training data. By enhancing the trajectory model with target information the decoder relied less on neural control signals, reducing the burden on the user.
A major challenge in controlling multiple-input multiple output functional electrical stimulation systems is the large amount of time required to identify a workable system model due to the high dimensionality of the space of inputs. To address this challenge we are exploring optimal methods to sample the input space. In this paper we present two methods for optimally sampling isometric muscle force recruitment curves. One method maximizes the information about the recruitment curve parameters, and the second method minimizes the average variance of the predicted output force. We compared these methods to two previously-used methods in simulation. The simulation model was identified from recruitment data collected during experiments with a human subject with a high spinal cord injury. The optimal sampling methods on average produced estimates of the output force with less error than the two previously-used methods. The optimal sampling methods require fewer system identification experiments to identify models with similar output prediction accuracy.
Many common tasks compromise arm stability along specific directions. Such tasks can be completed only if the impedance of the arm is sufficient to compensate for the destabilizing effects of the task. During movement, it has been demonstrated that the direction of maximal arm stiffness, the static component of impedance, can be preferentially increased to compensate for directionally unstable environments. In contrast, numerous studies have shown that such control is not possible during postural tasks. It remains unknown if these findings represent a fundamental difference in the control of arm mechanics during posture and movement or an involuntary response to the destabilizing environments used in the movement studies but not yet tested during posture maintenance. Our goal was to quantify how arm impedance is adapted during postural tasks that compromise stability along specific directions. Our results demonstrate that impedance can be modulated to compensate for these instabilities during postural tasks but that the changes are modest relative to those previously reported during reaching. Our observed changes were primarily in the magnitude of end-point stiffness, but these were not sufficient to alter the direction of maximal stiffness. Furthermore, there were no substantial changes in the magnitude of end-point viscosity or inertia, suggesting that the primary change to arm impedance was a selective increase in stiffness to compensate for the destabilizing stiffness properties of the environment. We suggest that these modest changes provide an initial involuntary response to destabilizing environments prior to the larger changes that can be affected through voluntary interventions.
Previous studies in neurologically intact subjects have shown that motor coordination can be described by task-dependent combinations of a few muscle synergies, defined here as a fixed pattern of activation across a set of muscles. Arm function in severely impaired stroke survivors is characterized by stereotypical postural and movement patterns involving the shoulder and elbow. Accordingly, we hypothesized that muscle synergy composition is altered in severely impaired stroke survivors. Using an isometric force matching protocol, we examined the spatial activation patterns of elbow and shoulder muscles in the affected arm of 10 stroke survivors (Fugl-Meyer <25/66) and in both arms of six age-matched controls. Underlying muscle synergies were identified using non-negative matrix factorization. In both groups, muscle activation patterns could be reconstructed by combinations of a few muscle synergies (typically 4). We did not find abnormal coupling of shoulder and elbow muscles within individual muscle synergies. In stroke survivors, as in controls, two of the synergies were comprised of isolated activation of the elbow flexors and extensors. However, muscle synergies involving proximal muscles exhibited consistent alterations following stroke. Unlike controls, the anterior deltoid was coactivated with medial and posterior deltoids within the shoulder abductor/extensor synergy and the shoulder adductor/flexor synergy in stroke was dominated by activation of pectoralis major, with limited anterior deltoid activation. Recruitment of the altered shoulder muscle synergies was strongly associated with abnormal task performance. Overall, our results suggest that an impaired control of the individual deltoid heads may contribute to poststroke deficits in arm function.
System identification of physiological systems poses unique challenges, especially when the structure of the system under study is uncertain. Nonparametric techniques can be useful for identifying system structure, but these typically assume stationarity and require large amounts of data. Both of these requirements are often not easily obtained in the study of physiological systems. Ensemble methods for time-varying nonparametric estimation have been developed to address the issue of stationarity, but these require an amount of data that can be prohibitive for many experimental systems. To address this issue, we developed a novel algorithm that uses multiple short data segments. Using simulation studies, we showed that this algorithm produces system estimates with lower variability than previous methods when limited data are present. Furthermore, we showed that the new algorithm generates time-varying system estimates with lower total error than an ensemble method. Thus, this algorithm is well suited for the identification of physiological systems that vary with time or from which only short segments of stationary data can be collected.
Following stroke, reaching movements are slow, segmented, and variable. It is unclear if these deficits result from a poorly constructed movement plan or an inability to voluntarily execute an appropriate plan. The acoustic startle reflex provides a means to initiate a motor plan involuntarily. In the presence of a movement plan, startling acoustic stimulus triggers non-voluntary early execution of planned movement, a phenomenon known as the startReact response. In unimpaired individuals, the startReact response is identical to a voluntarily initiated movement, except that it is elicited 30-40 ms. As the startReact response is thought to be mediated by brainstem pathways, we hypothesized that the startReact response is intact in stroke subjects. If startReact is intact, it may be possible to elicit more task-appropriate patterns of muscle activation than can be elicited voluntarily. We found that startReact responses were intact following stroke. Responses were initiated as rapidly as those in unimpaired subjects, and with muscle coordination patterns resembling those seen during unimpaired volitional movements. Results were striking for elbow flexion movements, which demonstrated no significant differences between the startReact responses elicited in our stroke and unimpaired subject groups. The results during planned extension movements were less straightforward for stroke subjects, since the startReact response exhibited task inappropriate activity in the flexors. This inappropriate activity diminished over time. This adaptation suggests that the inappropriate activity was transient in nature and not related to the underlying movement plan. We hypothesize that the task-inappropriate flexor activity during extension results from an inability to suppress the classic startle reflex, which primarily influences flexor muscles and adapts rapidly with successive stimuli. These results indicate that stroke subjects are capable of planning ballistic elbow movements, and that when these planned movements are involuntarily executed they can be as rapid and appropriate as those in unimpaired individuals.
Estimates of joint or limb impedance are commonly used in the study of how the nervous system controls posture and movement, and how that control is altered by injury to the neural or musculoskeletal systems. Impedance characterizes the dynamic relationship between an imposed perturbation of joint position and the torques generated in response. While there are many practical reasons for estimating impedance rather than its inverse, admittance, it is an acausal representation of the limb mechanics that can lead to difficulties in interpretation or use. The purpose of this study was to explore the acausal nature of nonparametric estimates of joint impedance representations to determine how they are influenced by common experimental and computational choices. This was accomplished by deriving discrete-time realizations of first- and second-order derivatives to illustrate two key difficulties in the physical interpretation of impedance impulse response functions. These illustrations were provided using both simulated and experimental data. It was found that the shape of the impedance impulse response depends critically on the selected sampling rate, and on the bandwidth and noise characteristics of the position perturbation used during the estimation process. These results provide important guidelines for designing experiments in which nonparametric estimates of impedance will be obtained, especially when those estimates are to be used in a multistep identification process.
Although many daily tasks tend to destabilize arm posture, it is still possible to have stable interactions with the environment by regulating the multijoint mechanics of the arm in a task-appropriate manner. For postural tasks, this regulation involves the appropriate control of endpoint stiffness, which represents the stiffness of the arm at the hand. Although experimental studies have been used to evaluate endpoint stiffness control, including the orientation of maximal stiffness, the underlying neural strategies remain unknown. Specifically, the relative importance of feedforward and feedback mechanisms has yet to be determined due to the difficulty separately identifying the contributions of these mechanisms in human experiments. This study used a previously validated three-dimensional musculoskeletal model of the arm to quantify the degree to which the orientation of maximal endpoint stiffness could be changed using only steady-state muscle activations, used to represent feedforward motor commands. Our hypothesis was that the feedforward control of endpoint stiffness orientation would be significantly constrained by the biomechanical properties of the musculoskeletal system. Our results supported this hypothesis, demonstrating substantial biomechanical constraints on the ability to regulate endpoint stiffness throughout the workspace. The ability to regulate stiffness orientation was further constrained by additional task requirements, such as the need to support the arm against gravity or exert forces on the environment. Together, these results bound the degree to which slowly varying feedforward motor commands can be used to regulate the orientation of maximum arm stiffness and provide a context for better understanding conditions in which feedback control may be needed.
During natural locomotion, the stiffness of the human knee is modulated continuously and subconsciously according to the demands of activity and terrain. Given modern actuator technology, powered transfemoral prostheses could theoretically provide a similar degree of sophistication and function. However, experimentally quantifying knee stiffness modulation during natural gait is challenging. Alternatively, joint stiffness could be estimated in a less disruptive manner using electromyography (EMG) combined with kinetic and kinematic measurements to estimate muscle force, together with models that relate muscle force to stiffness. Here we present the first step in that process, where we develop such an approach and evaluate it in isometric conditions, where experimental measurements are more feasible. Our EMG-guided modeling approach allows us to consider conditions with antagonistic muscle activation, a phenomenon commonly observed in physiological gait. Our validation shows that model-based estimates of knee joint stiffness coincide well with experimental data obtained using conventional perturbation techniques. We conclude that knee stiffness can be accurately estimated in isometric conditions without applying perturbations, which presents an important step toward our ultimate goal of quantifying knee stiffness during gait.
Neuroprosthetic devices promise to allow paralyzed patients to perform the necessary functions of everyday life. However, to allow patients to use such tools it is necessary to decode their intent from neural signals such as electromyograms (EMGs). Because these signals are noisy, state of the art decoders integrate information over time. One systematic way of doing this is by taking into account the natural evolution of the state of the body--by using a so-called trajectory model. Here we use two insights about movements to enhance our trajectory model: (1) at any given time, there is a small set of likely movement targets, potentially identified by gaze; (2) reaches are produced at varying speeds. We decoded natural reaching movements using EMGs of muscles that might be available from an individual with spinal cord injury. Target estimates found from tracking eye movements were incorporated into the trajectory model, while a mixture model accounted for the inherent uncertainty in these estimates. Warping the trajectory model in time using a continuous estimate of the reach speed enabled accurate decoding of faster reaches. We found that the choice of richer trajectory models, such as those incorporating target or speed, improves decoding particularly when there is a small number of EMGs available.
Withdrawal reflexes in the leg adapt in a context appropriate manner to remove the limb from noxious stimuli, but the extent to which withdrawal reflexes adapt in the arm remains unknown.
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