A Myoelectric Computer Interface for Reducing Abnormal Muscle Activations after Spinal Cord Injury.

Myoelectric Computer Interfaces (MCIs) are a viable option to promote the recovery of movements following spinal cord injury (SCI), stroke, or other neurological disorders that impair motor functions. We developed and tested a MCI interface with the goal of reducing abnormal muscular activations due to compensatory strategies or undesired co-contraction after SCI. The interface mapped surface electromyographic signals (sEMG) into the movement of a cursor on a computer monitor. First, we aimed to reduce the co-activation of muscles pairs: the activation of two muscles controlled orthogonal directions of the cursor movements. Furthermore, to decrease the undesired concurrent activation of a third muscle, we modulated the visual feedback related to the position of the cursor on the screen based on the activation of this muscle. We tested the interface with six unimpaired and two SCI participants. Participants were able to decrease the activity of the targeted muscle when it was associated with the visual feedback of the cursor, but, interestingly, after training, its activity increased again. As for the SCI participants, one successfully decreased the co-activation of arm muscles, while the other successfully improved the selective activation of leg muscles. This is a first proof of concept that people with SCI can acquire, through the proposed MCI, a greater awareness of their muscular activity, reducing abnormal muscle simultaneous activations.

Changes in neuromuscular activity during motor training with a body-machine interface after spinal cord injury.

Body machine interfaces (BMIs) are used by people with severe motor disabilities to control external devices, but they also offer the opportunity to focus on rehabilitative goals. In this study we introduced in a clinical setting a BMI that was integrated by the therapists in the rehabilitative treatments of 2 spinal cord injured (SCI) subjects for 5 weeks. The BMI mapped the user's residual upper body mobility onto the two coordinates of a cursor on a screen. By controlling the cursor, the user engaged in playing computer games. The BMI allowed the mapping between body and cursor spaces to be modified, gradually challenging the user to exercise more impaired movements. With this approach, we were able to change our subjects' behavior, who initially used almost exclusively their proximal upper body-shoulders and arms - for using the BMI. By the end of training, cursor control was shifted toward more distal body regions - forearms instead of upper arms - with an increase of mobility and strength of all the degrees of freedom involved in the control. The clinical tests and the electromyographic signals from the main muscles of the upper body confirmed the positive effect of the training. Encouraging the subjects to explore different and sometimes unusual movement combinations was beneficial for recovering distal arm functions and for increasing their overall mobility.

A body-machine interface for training selective pelvis movements in stroke survivors: A pilot study.

The body-machine interfaces (BMIs) map the subjects' movements into the low dimensional control space of external devices to reach assistive and/or rehabilitative goals. This work is a first proof of concept of this kind of BMI as tool for rehabilitation after stroke. We designed an exercise to improve the control of selective movements of the pelvis in stroke survivors, increasing the ability to decouple the motion in the sagittal and frontal planes and decreasing compensatory adjustments at the shoulder girdle. A Kinect sensor recorded the movements of the subjects. Subjects played different games by controlling the vertical and horizontal motion of a cursor on a screen with respectively the lateral tilt and the ante/retroversion of their pelvis. We monitored also the degrees of freedom not directly involved in cursor control, thus subjects could complete the task only with a correct posture. Our preliminary results highlight significant improvement not only in cursor control, but also in the Trunk Impairment Scale (TIS) and in the Five Times Sit to Stand Test (5xSST).

Therapist recognition of impaired muscle groups in simulated multi-joint hypertonia.

It is common in today's clinical practice for a therapist to physically manipulate patients' limbs to assess hypertonic conditions (e.g. spasticity, rigidity, dystonia, among others). We present a study that evaluates the capabilities of expert therapists to correctly identify the location of a hypertonic impairment of an arm through standard manipulation. Therapists interacted with a hypertonic virtual arms rendered on a robotic device. Our results show that testing joints independently can cause misjudgment of the mechanical contributions of pluri-articular muscles to multi-joint impairment.

Haptic recognition of dystonia and spasticity in simulated multi-joint hypertonia.

This paper investigates the capability of naïve individuals to recognize dystonic- or spastic- like conditions through physical manipulation of a virtual arm. Subjects physically interact with a two-joint, six-muscle hypertonic arm model, rendered on a two degrees-of-freedom robotic manipulandum. This paradigm aims to identify the limitation of manual manipulation during diagnosis of hypertonia. Our results indicate that there are difficulties to discriminate between the two conditions at low to medium level of severity. We found that the sample entropy of the executed motion and the force experienced during physical manipulation, tended to be higher during incorrectly identified trials than in those correctly assessed.

Haptic perception of multi-joint hypertonia during simulated patient-therapist physical tele-interaction.

A potential solution to provide individualized physical therapy in remote areas is tele-interaction via robotic devices. To maintain stability during tele-interaction, transmission delay-compensation algorithms bound the impedance between the patient and the therapist. This can compromise the haptic perception of the patient being assessed, which can in turn lead to a bad diagnosis or intervention. We investigated how the perception of the severity of hypertonia (a common condition after neurological disorders) varied by modifying the connection impedance on a physical simulator. We found that assessing hypetonia using a low impedance connection may result in an overestimation of mild impairments.

Perception and Action in Teleoperated Needle Insertion.

We studied the effect of delay on perception and action in contact with a force field that emulates elastic soft tissue with a rigid nonlinear boundary. Such a field is similar to forces exerted on a needle during teleoperated needle insertion. We found that delay causes motor underestimation of the stiffness of this nonlinear soft tissue, without perceptual change. These experimental results are supported by simulation of a simplified mechanical model of the arm and neural controller, and a model for perception of stiffness, which is based on regression in the force-position space. In addition, we show that changing the gain of the teleoperation channel cancels the motor effect of delay without adding perceptual distortion. We conclude that it is possible to achieve perceptual and motor transparency in virtual one-dimensional remote needle insertion task.

Multijoint arm stiffness during movements following stroke: implications for robot therapy.

Impaired arm movements in stroke appear as a set of stereotypical kinematic patterns, characterized by abnormal joint coupling, which have a direct consequence on arm mechanics and can be quantified by the net arm stiffness at the hand. The current available measures of arm stiffness during functional tasks have limited clinical use, since they require several repetitions of the same test movement in many directions. Such procedure is difficult to obtain in stroke survivors who have lower fatigue threshold and increased variability compared to unimpaired individuals. The present study proposes a novel, fast quantitative measure of arm stiffness during movements by means of a Time-Frequency technique and the use of a reassigned spectrogram, applied on a trial-by-trial basis with a single perturbation. We tested the technique feasibility during robot mediated therapy, where a robot helped stroke survivors to regain arm mobility by providing assistive forces during a hitting task to 13 targets covering the entire reachable workspace. The endpoint stiffness of the paretic arm was estimated at the end of each hitting movements by suddenly switching of the assistive forces and observing the ensuing recoil movements. In addition, we considered how assistive forces influence stiffness. This method will provide therapists with improved tools to target the treatment to the individual's specific impairment and to verify the effects of the proposed exercises.

Body machine interface: remapping motor skills after spinal cord injury.

The goal of a body-machine interface (BMI) is to map the residual motor skills of the users into efficient patterns of control. The interface is subject to two processes of learning: while users practice controlling the assistive device, the interface modifies itself based on the user's residual abilities and preferences. In this study, we combined virtual reality and movement capture technologies to investigate the reorganization of movements that occurs when individuals with spinal cord injury (SCI) are allowed to use a broad spectrum of body motions to perform different tasks. Subjects, over multiple sessions, used their upper body movements to engage in exercises that required different operational functions such as controlling a keyboard for playing a videogame, driving a simulated wheelchair in a virtual reality (VR) environment, and piloting a cursor on a screen for reaching targets. In particular, we investigated the possibility of reducing the dimensionality of the control signals by finding repeatable and stable correlations of movement signals, established both by the presence of biomechanical constraints and by learned patterns of coordination. The outcomes of these investigations will provide guidance for further studies of efficient remapping of motor coordination for the control of assistive devices and are a basis for a new training paradigm in which the burden of learning is significantly removed from the impaired subjects and shifted to the devices.

The remapping of space in motor learning and human-machine interfaces.

Studies of motor adaptation to patterns of deterministic forces have revealed the ability of the motor control system to form and use predictive representations of the environment. One of the most fundamental elements of our environment is space itself. This article focuses on the notion of Euclidean space as it applies to common sensory motor experiences. Starting from the assumption that we interact with the world through a system of neural signals, we observe that these signals are not inherently endowed with metric properties of the ordinary Euclidean space. The ability of the nervous system to represent these properties depends on adaptive mechanisms that reconstruct the Euclidean metric from signals that are not Euclidean. Gaining access to these mechanisms will reveal the process by which the nervous system handles novel sophisticated coordinate transformation tasks, thus highlighting possible avenues to create functional human-machine interfaces that can make that task much easier. A set of experiments is presented that demonstrate the ability of the sensory-motor system to reorganize coordination in novel geometrical environments. In these environments multiple degrees of freedom of body motions are used to control the coordinates of a point in a two-dimensional Euclidean space. We discuss how practice leads to the acquisition of the metric properties of the controlled space. Methods of machine learning based on the reduction of reaching errors are tested as a means to facilitate learning by adaptively changing he map from body motions to controlled device. We discuss the relevance of the results to the development of adaptive human-machine interfaces and optimal control.

Robot-amplified manual exploration improves load identification.

We tested how manual exploration with anisotropic loading (Viscosity-Only (negative), Inertia-Only, or Combined-Load) influenced skill transfer to the isolated inertial load. Intact subjects (N=39) performed manual exploration with an anisotropic load before evaluation with prescribed circular movements. Combined-Load resulted in lower error (6.89±3.25%) compared to Inertia-Only (8.40±4.32%) and Viscosity-Only (8.17±4.13%) according to radial deviation analysis (% of trial mean radius). An analysis of sensitivity to load variation in normal and catch trials reveals performance differences were likely due to changes in feedforward mass compensation. Analysis of exploration movement revealed higher average speeds (12.0%) and endpoint forces (22.9%) with Combined-Load exploration compared to Inertia-Only. Our findings suggest that free movements amplified by negative viscosity can enhance the ability to identify changes in inertial loading.

A Regression and Boundary-Crossing-Based Model for the Perception of Delayed Stiffness.

The stiffness of the environment with which we come in contact is the local derivative of a force field. The boundary of an elastic field is a singular region where local stiffness is ill-defined. We found that subjects interacting with delayed force fields tend to underestimate stiffness if they do not move across the boundary. In contrast, they tend to overestimate stiffness when they move across the elastic field boundary. We propose a unifying computational model of stiffness perception based on an active process that combines the concurrent operations of a force and of a position-control system.

Robotics and virtual reality: the development of a life-sized 3-D system for the rehabilitation of motor function.

We have been developing and combining state-of-art devices that allow humans to visualize and feel synthetic objects superimposed on the real world. This effort stems from the need of platform for extending experiments on motor control and learning to realistic human motor tasks and environments, not currently represented in the practice of research. This paper's goal is to outline our motivations, progress, and objectives. Because the system is a general tool, we also hope to motivate researchers in related fields to join in. The platform under development, an augmented reality system combined with a haptic-interface robot, will be a new tool for contributing to the scientific knowledge base in the area of human movement control and rehabilitation robotics. Because this is a prototype, the system will also guide new methods by probing the levels of quality necessary for future design cycles and related technology. Inevitably, it should also lead the way to commercialization of such systems.

Does the motor control system use multiple models and context switching to cope with a variable environment?

Studies of arm movements have shown that subjects learn to compensate predictable mechanical perturbations by developing a representation of the relation between the state of motion of the arm and the perturbing forces. Here, we tested the hypothesis that subjects construct internal representations of two different force fields and switch between them when presented with an alternating sequence of these fields. Our results do not support this hypothesis. Subjects performed reaching movements in four sessions over 4 days. On the 1st day the robotic manipulandum perturbed the movement by perpendicular force that alternated its direction after each movement. Subjects were unable to construct the two underlying models and switch between them. On the 2nd day only one field was applied and well learned. On the 3rd day only the other field was applied and well learned. Then the experiment of the 1st day was repeated on the 4th day. Even after this extensive training subjects showed no signs of improved performance with alternating fields. This result combined with previous studies suggests that the central nervous system has a strong tendency to employ a single internal model when dealing with a sequence of perturbations.

Learning to move amid uncertainty.

We studied how subjects learned to make movements against unpredictable perturbations. Twelve healthy human subjects made goal-directed reaching movements in the horizontal plane while holding the handle of a two-joint robotic manipulator. The robot generated viscous force fields that perturbed the limb perpendicular to the desired direction of movement. The amplitude (but not the direction) of the viscous field varied randomly from trial to trial. Systems identification techniques were employed to characterize how subjects adapted to these random perturbations. Subject performance was quantified primarily using the peak deviation from a straight-line hand path. Subjects adapted their arm movements to the sequence of random force-field amplitudes. This adaptive response compensated for the approximate mean from the random sequence of perturbations and did not depend on the statistical distribution of that sequence. Subjects did not adapt by directly counteracting the mean field strength itself on each trial but rather by using information about perturbations and movement errors from a limited number of previous trials to adjust motor commands on subsequent trials. This strategy permitted subjects to achieve near-optimal performance (defined as minimizing movement errors in a least-squares sense) while maintaining computational efficiency. A simple model using information about movement errors and perturbation amplitudes from a single previous trial predicted subject performance in stochastic environments with a high degree of fidelity and further predicted key performance features observed in nonstochastic environments. This suggests that the neural structures modified during motor adaptation require only short-term memory. Explicit representations regarding movements made more than a few trials in the past are not used in generating optimal motor responses on any given trial.

Persistence of motor adaptation during constrained, multi-joint, arm movements.

We studied the stability of changes in motor performance associated with adaptation to a novel dynamic environment during goal-directed movements of the dominant arm. Eleven normal, human subjects made targeted reaching movements in the horizontal plane while holding the handle of a two-joint robotic manipulator. This robot was programmed to generate a novel viscous force field that perturbed the limb perpendicular to the desired direction of movement. Following adaptation to this force field, we sought to determine the relative role of kinematic errors and dynamic criteria in promoting recovery from the adapted state. In particular, we compared kinematic and dynamic measures of performance when kinematic errors were allowed to occur after removal of the viscous fields, or prevented by imposing a simulated, mechanical "channel" on movements. Hand forces recorded at the handle revealed that when kinematic errors were prevented from occurring by the application of the channel, recovery from adaptation to the novel field was much slower compared with when kinematic aftereffects were allowed to take place. In particular, when kinematic errors were prevented, subjects persisted in generating large forces that were unnecessary to generate an accurate reach. The magnitude of these forces decreased slowly over time, at a much slower rate than when subjects were allowed to make kinematic errors. This finding provides strong experimental evidence that both kinematic and dynamic criteria influence motor adaptation, and that kinematic-dependent factors play a dominant role in the rapid loss of adaptation after restoring the original dynamics.

Output units of motor behavior: an experimental and modeling study.

Cognitive approaches to motor control typically concern sequences of discrete actions without taking into account the stunning complexity of the geometry and dynamics of the muscles. This begs the question: Does the brain convert the intricate, continuous-time dynamics of the muscles into simpler discrete units of actions, and if so, how? One way for the brain to form discrete units of behavior from muscles is through the synergistic co-activation of muscles. While this possibility has long been known, the composition of potential muscle synergies has remained elusive. In this paper, we have focused on a method that allowed us to examine and compare the limb stabilization properties of all possible muscle combinations. We found that a small set (as few as 23 out of 65,536) of all possible combinations of 16 limb muscles are robust with respect to activation noise: these muscle combinations could stabilize the limb at predictable, restricted portions of the workspace in spite of broad variations in the force output of their component muscles. The locations at which the robust synergies stabilize the limb are not uniformly distributed throughout the leg's workspace, but rather, they cluster at four workspace areas. The simulated robust synergies are similar to the actual synergies we have previously found to be generated by activation of the spinal cord. Thus, we have developed a new analytical method that enabled us to select a few muscle synergies with interesting properties out of the set of possible muscle combinations. Beyond this, the identification of robustness as a common property of the synergies in simple motor behaviors will open the way to the study of dynamic stability, which is an important and distinct property of the vertebrate motor-control system.

Haptic interaction with virtual objects. Spatial perception and motor control.

This paper considers interaction of the human arm with "virtual" objects simulated mechanically by a planar robot. Haptic perception of spatial properties of objects is distorted. It is reasonable to expect that it may be distorted in a geometrically consistent way. Three experiments were performed to quantify perceptual distortion of length, angle and orientation. We found that spatial perception is geometrically inconsistent across these perceptual tasks. Given that spatial perception is distorted, it is plausible that motor behavior may be distorted in a way consistent with perceptual distortion. In a fourth experiment, subjects were asked to draw circles. The results were geometrically inconsistent with those of the length perception experiment. Interestingly, although the results were inconsistent (statistically different), this difference was not strong (the relative distortion between the observed distributions was small). Some computational implications of this research for haptic perception and motor planning are discussed.

Motor learning through the combination of primitives.

In this paper we discuss a new perspective on how the central nervous system (CNS) represents and solves some of the most fundamental computational problems of motor control. In particular, we consider the task of transforming a planned limb movement into an adequate set of motor commands. To carry out this task the CNS must solve a complex inverse dynamic problem. This problem involves the transformation from a desired motion to the forces that are needed to drive the limb. The inverse dynamic problem is a hard computational challenge because of the need to coordinate multiple limb segments and because of the continuous changes in the mechanical properties of the limbs and of the environment with which they come in contact. A number of studies of motor learning have provided support for the idea that the CNS creates, updates and exploits internal representations of limb dynamics in order to deal with the complexity of inverse dynamics. Here we discuss how such internal representations are likely to be built by combining the modular primitives in the spinal cord as well as other building blocks found in higher brain structures. Experimental studies on spinalized frogs and rats have led to the conclusion that the premotor circuits within the spinal cord are organized into a set of discrete modules. Each module, when activated, induces a specific force field and the simultaneous activation of multiple modules leads to the vectorial combination of the corresponding fields. We regard these force fields as computational primitives that are used by the CNS for generating a rich grammar of motor behaviours.

Connecting brains to robots: an artificial body for studying the computational properties of neural tissues.

We have created a hybrid neuro-robotic system that establishes two-way communication between the brain of a lamprey and a small mobile robot. The purpose of this system is to offer a new paradigm for investigating the behavioral, computational, and neurobiological mechanisms of sensory-motor learning in a unified context. The mobile robot acts as an artificial body that delivers sensory information to the neural tissue and receives command signals from it. The sensory information encodes the intensity of light generated by a fixed source. The closed-loop interaction between brain and robot generates autonomous behaviors whose features are strictly related to the structure and operation of the neural preparation. We provide a detailed description of the hybrid system, and we present experimental findings on its performance. In particular, we found (a) that the hybrid system generates stable behaviors, (b) that different preparations display different but systematic responses to the presentation of an optical stimulus, and (c) that alteration of the sensory input leads to short- and long-term adaptive changes in the robot responses. The comparison of the behaviors generated by the lamprey's brain stem with the behaviors generated by network models of the same neural system provides us with a new tool for investigating the computational properties of synaptic plasticity.