Feedback control of unsupported standing in paraplegia. Part I: optimal control approach

This is the first of a pair of papers which describe an investigation into the feasibility of providing artificial balance to paraplegics using electrical stimulation of the paralyzed muscles. By bracing the body above the shanks, only stimulation of the plantarflexors is necessary. This arrangement prevents any influence from the intact neuromuscular system above the spinal cord lesion. Here, the authors extend the design of the controllers to a nested-loop LQG (linear quadratic Gaussian) stimulation controller which has ankle moment feedback (inner loops) and inverted pendulum angle feedback (outer loop). Each control loop is tuned by two parameters, the control weighting and an observer rise-time, which together determine the behavior. The nested structure was chosen because it is robust, despite changes in the muscle properties (fatigue) and interference from spasticity

Estimation of Time-Varying Ankle Joint Stiffness Under Dynamic Conditions via System Identification Techniques

An important goal in the design of next-generation exoskeletons and limb prostheses is to replicate human limb dynamics. Joint impedance determines the dynamic relation between joint displacement and torque. Joint stiffness is the position-dependent component of joint impedance and is key in postural control and movement. However, the mechanisms to modulate joint stiffness are not fully understood yet. The goal of this study is to conduct a systematic analysis on how humans modulate ankle stiffness. Time-varying stiffness was estimated for six healthy subjects under isometric, as well as quick and slow dynamic conditions via system identification techniques; specifically, an ensemble-based algorithm using short segments of ankle torque and position recordings. Our results show that stiffness had the lowest magnitude under quick dynamic conditions. Under isometric conditions, with fixed position and varying muscle activity, stiffness exhibited a higher magnitude. Finally, under slow dynamic conditions, stiffness was found to be the highest. Our results highlight, for the first time, the variability in stiffness modulation strategies across conditions, especially across movement velocity

New control strategies for neuroprosthetic systems

The availability of techniques to artificially excite paralyzed muscles opens enormous potential for restoring both upper and lower extremity movements with\ud neuroprostheses. Neuroprostheses must stimulate muscle, and control and regulate the artificial movements produced. Control methods to accomplish these tasks include feedforward (open-loop), feedback, and adaptive control. Feedforward control requires a great deal of information about the biomechanical behavior of the limb. For the upper extremity, an artificial motor program was developed to provide such movement program input to a neuroprosthesis. In lower extremity control, one group achieved their best results by attempting to meet naturally perceived gait objectives rather than to follow an exact joint angle trajectory. Adaptive feedforward control, as implemented in the cycleto-cycle controller, gave good compensation for the gradual decrease in performance observed with open-loop control. A neural network controller was able to control its system to customize stimulation parameters in order to generate a desired output trajectory in a given individual and to maintain tracking performance in the presence of muscle fatigue. The authors believe that practical FNS control systems must\ud exhibit many of these features of neurophysiological systems

Muscle Synergies Facilitate Computational Prediction of Subject-Specific Walking Motions.

Researchers have explored a variety of neurorehabilitation approaches to restore normal walking function following a stroke. However, there is currently no objective means for prescribing and implementing treatments that are likely to maximize recovery of walking function for any particular patient. As a first step toward optimizing neurorehabilitation effectiveness, this study develops and evaluates a patient-specific synergy-controlled neuromusculoskeletal simulation framework that can predict walking motions for an individual post-stroke. The main question we addressed was whether driving a subject-specific neuromusculoskeletal model with muscle synergy controls (5 per leg) facilitates generation of accurate walking predictions compared to a model driven by muscle activation controls (35 per leg) or joint torque controls (5 per leg). To explore this question, we developed a subject-specific neuromusculoskeletal model of a single high-functioning hemiparetic subject using instrumented treadmill walking data collected at the subject's self-selected speed of 0.5 m/s. The model included subject-specific representations of lower-body kinematic structure, foot-ground contact behavior, electromyography-driven muscle force generation, and neural control limitations and remaining capabilities. Using direct collocation optimal control and the subject-specific model, we evaluated the ability of the three control approaches to predict the subject's walking kinematics and kinetics at two speeds (0.5 and 0.8 m/s) for which experimental data were available from the subject. We also evaluated whether synergy controls could predict a physically realistic gait period at one speed (1.1 m/s) for which no experimental data were available. All three control approaches predicted the subject's walking kinematics and kinetics (including ground reaction forces) well for the model calibration speed of 0.5 m/s. However, only activation and synergy controls could predict the subject's walking kinematics and kinetics well for the faster non-calibration speed of 0.8 m/s, with synergy controls predicting the new gait period the most accurately. When used to predict how the subject would walk at 1.1 m/s, synergy controls predicted a gait period close to that estimated from the linear relationship between gait speed and stride length. These findings suggest that our neuromusculoskeletal simulation framework may be able to bridge the gap between patient-specific muscle synergy information and resulting functional capabilities and limitations

ESTIMATION OF MULTI-DIRECTIONAL ANKLE IMPEDANCE AS A FUNCTION OF LOWER EXTREMITY MUSCLE ACTIVATION

The purpose of this research is to investigate the relationship between the mechanical impedance of the human ankle and the corresponding lower extremity muscle activity. Three experimental studies were performed to measure the ankle impedance about multiple degrees of freedom (DOF), while the ankle was subjected to different loading conditions and different levels of muscle activity. The first study determined the non-loaded ankle impedance in the sagittal, frontal, and transverse anatomical planes while the ankle was suspended above the ground. The subjects actively co-contracted their agonist and antagonistic muscles to various levels, measured using electromyography (EMG). An Artificial Neural Network (ANN) was implemented to characterize the relationship between the EMG and non-loaded ankle impedance in 3-DOF. The next two studies determined the ankle impedance and muscle activity during standing, while the foot and ankle were subjected to ground perturbations in the sagittal and frontal planes. These studies investigate the performance of subject-dependent models, aggregated models, and the feasibility of a generic, subject-independent model to predict ankle impedance based on the muscle activity of any person. Several regression models, including Least Square, Support Vector Machine, Gaussian Process Regression, and ANN, and EMG feature extraction techniques were explored. The resulting subject-dependent and aggregated models were able to predict ankle impedance with reasonable accuracy. Furthermore, preliminary efforts toward a subject-independent model showed promising results for the design of an EMG-impedance model that can predict ankle impedance using new subjects. This work contributes to understanding the relationship between the lower extremity muscles and the mechanical impedance of the ankle in multiple DOF. Applications of this work could be used to improve user intent recognition for the control of active ankle-foot prostheses

Using Lower Extremity Muscle Activations to Estimate Human Ankle Impedance in the External-Internal Direction

For millions of people, mobility has been afflicted by lower limb amputation. Lower extremity prostheses have been used to improve the mobility of an amputee; however, they often require additional compensation from other joints and do not allow for natural maneuverability. To improve upon the functionality of ankle-foot prostheses, it is necessary to understand the role of different muscle activations in the modulation of mechanical impedance of a healthy human ankle. This report presents the results of using artificial neural networks (ANN) to determine the functional relationship between lower extremity electromyography (EMG) signals and ankle impedance in the transverse plane. The Anklebot was used to apply pseudo-random perturbations to the human ankle in the transverse plane, while motion of the ankle in the sagittal and frontal planes was constrained. Using a stochastic system identification method, the mechanical impedance of the ankle in external-internal (EI) direction was determined as a function of the applied torque and corresponding ankle motion. The impedance of the ankle and muscle EMG signals were determined for three muscle activation levels, including with relaxed muscles, and with muscles activated and 10% and 20% of the subject’s maximum voluntary contraction (MVC). This information was used as the input and target matrices to train an ANN for each subject. The resulting ankle impedance from the proposed ANN was effectively predicted within 85% accuracy for nine out of ten subjects, and was within ±5 Nm/rad of the target impedance for all subjects. This work provides more understanding of the neuromuscular characteristics of the ankle and provides insight toward future design and control of ankle-foot prostheses