Open-loop control of the freely-swinging paralyzed leg

Synthesis of optimal electrical stimulation patterns for functional motion restoration: applied to spinal cord-injured patients

We investigated the synthesis of electrical stimulation patterns for functional movement restoration in human paralyzed limbs. We considered the knee joint system, co-activated by the stimulated quadriceps and hamstring muscles. This synthesis is based on optimized functional electrical stimulation (FES) patterns to minimize muscular energy consumption and movement efficiency criteria. This two-part work includes a multi-scale physiological muscle model, based on Huxley's formulation. In the simulation, three synthesis strategies were investigated and compared in terms of muscular energy consumption and co-contraction levels. In the experimental validation, the synthesized FES patterns were carried out on the quadriceps-knee joint system of four complete spinal cord injured subjects. Surface stimulation was applied to all subjects, except for one FES-implanted subject who received neural stimulation. In each experimental validation, the model was adapted to the subject through a parameter identification procedure. Simulation results were successful and showed high co-contraction levels when reference trajectories were tracked. Experimental validation results were encouraging, as the desired and measured trajectories showed good agreement, with an 8.4 % rms error in a subject without substantial time-varying behavior. We updated the maximal isometric force in the model to account for time-varying behavior, which improved the average rms errors from 31.4 to 13.9 % for all subjects.

Radial Basis Function Neural Network-based PID model for functional electrical stimulation system control

Functional electrical stimulation (FES) has been widely used in the area of neural engineering. It utilizes electrical current to activate nerves innervating extremities affected by paralysis. An effective combination of a traditional PID controller and a neural network, being capable of nonlinear expression and adaptive learning property, supply a more reliable approach to construct FES controller that help the paraplegia complete the action they want. A FES system tuned by Radial Basis Function (RBF) Neural Network-based Proportional-Integral-Derivative (PID) model was designed to control the knee joint according to the desired trajectory through stimulation of lower limbs muscles in this paper. Experiment result shows that the FES system with RBF Neural Network-based PID model get a better performance when tracking the preset trajectory of knee angle comparing with the system adjusted by Ziegler- Nichols tuning PID model.

Neural network and fuzzy control in FES-assisted locomotion for the hemiplegic

This study is aimed at establishing a neural network and fuzzy feedback control FES system used for adjusting the optimum electrical stimulating current to control the motion of an ankle joint. The proposed method further improves the drop-foot problem existing in hemiplegia patients. The proposed system includes both hardware and software. The hardware system determines the patient's ankle joint angle using a position sensor located in the patient's affected side. This sensor stimulates the tibialis anterior with an electrical stimulator that induces the dorsiflexion action and achieves the ideal ankle joint trace motion. The software system estimates the stimulating current using a neural network. The fuzzy controller solves the nonlinear problem by compensating the motion trace errors between the neural network control and actual system. The control qualities of various controllers for four subjects were compared in the clinical test. It was found that both the root mean square error and the mean error were minimal when using the neural network and fuzzy controller. The drop-foot problem in hemiplegic's locomotion was effectively improved by incorporating the neural network and fuzzy controller with the functional electrical simulator.

Adaptive control of cyclic movements as muscles fatigue using functional neuromuscular stimulation

For individuals with spinal cord injuries, functional neuromuscular stimulation (FNS) systems can be used to activate paralyzed muscles in order to restore function, provide exercise, or assist in movement therapy. In previous work, the pattern generator/pattern shaper (PG/PS) adaptive controller was evaluated on subjects with spinal cord injuries and was able to automatically adjust stimulation parameters to account for individual subject differences and system response nonlinearities. In this study, the PG/PS control system was utilized in extended trials. Results indicated that the controller adapted stimulation patterns in an online manner to account for changes in system properties due to fatigue.

Adaptive neural network control of cyclic movements using functional neuromuscular stimulation

In this study, we evaluated the performance of an adaptive feedforward controller and its ability to automatically develop and customize stimulation patterns for use in functional neuromuscular stimulation (FNS) systems. Results from previous experiments using the pattern generator/pattern shaper (PG/PS) controller to generate isometric contractions demonstrated its ability to adjust stimulation patterns to account for recruitment nonlinearities and muscle dynamics. In this study, the PG/PS controller was tested under isotonic conditions. This evaluation required the PG/PS controller to account for muscle length-tension and force-velocity properties as well as limb dynamics. The performance of the adaptive controller was also compared with that of a proportional-derivative (PD) feedback controller. The PG/PS controller is composed of a neural network system that adaptively filters a periodic signal to produce a muscle stimulation pattern for generating cyclic movements. We used computer-simulated models to determine controller parameters for the PG/PS and PD controller that perform well across a variety of musculoskeletal systems. The controllers were then experimentally evaluated on both legs of two subjects with spinal cord injury. Results indicated that the PG/PS controller was able to achieve and maintain better tracking performance than the PD controller. This study indicates that the PG/PS control system may provide an effective mechanism for automatically customizing stimulation patterns for individuals using FNS systems.

A nonlinear mathematical model of electrically stimulated skeletal muscle

A new biophysically based mathematical model for a human musculotendon system is presented. This model is developed specifically for skeletal muscle activated by functional electrical stimulation (FES). The reverse-order recruitment dynamics of FES activated systems are modeled, as are the underlying processes of force generation in mammalian muscle. The resulting system model is both nonlinear and highly coupled, reflecting the fundamental structure and behavior of skeletal muscle. A new heterogeneous model structure for a contractile element is also presented that overcomes many of the problems which arise when attempting to describe all possible contraction modes. It is found that the new model is robust, numerically stable, and easily implemented. Simulation results are presented that demonstrate the model's ability to capture a variety of nonlinear behaviors observed in skeletal muscle activated by FES. Significant insight into the internal dynamics of force development in FES muscle may also be gained from the model. This model is intended as a possible alternative to those currently available in the literature. It may be of use to those conducting research into the modeling, control and optimization of FES generated motion, and neural feedback systems.

Neural network control of functional neuromuscular stimulation systems: computer simulation studies

A neural network control system has been designed for the control of cyclic movements in Functional Neuromuscular Stimulation (FNS) systems. The design directly addresses three major problems in FNS control systems: customization of control system parameters for a particular individual, adaptation during operation to account for changes in the musculoskeletal system, and attaining resistance to mechanical disturbances. The control system was implemented by a two-stage neural network that utilizes a combination of adaptive feedforward and feedback control techniques. A new learning algorithm was developed to provide rapid customization and adaptation. The control system was evaluated in a series of studies on a computer simulated musculoskeletal model. The model of electrically stimulated muscle used in the study included nonlinear recruitment, linear dynamics, and multiplicative nonlinear torque-angle and torque-velocity scaling factors. The skeletal model consisted of a one-segment planar system with passive constraints on joint movement. Results of the evaluation have demonstrated that the control system can provide automated customization of the feedforward controller parameters for a given musculoskeletal system. It can account for changes in the musculoskeletal system by adapting the feedforward controller parameters on-line and it can resist the effects of mechanical disturbances. These results suggest that this design may be suitable for the control of FNS systems and other dynamic systems.

Cycle-to-cycle control of swing phase of paraplegic gait induced by surface electrical stimulation

Parameterised swing phase of gait in paraplegics was obtained using surface electrical stimulation of the hip flexors, hamstrings and quadriceps; the hip flexors were stimulated to obtain a desired hip angle range, the hamstrings to provide foot clearance in the forward swing, and the quadriceps to acquire knee extension at the end of the swing phase. We report on two main aspects; optimisation of the initial stimulation parameters, and parameter adaptation (control). The initial stimulation patterns were experimentally optimised in two paraplegic subjects using a controlled stand device, resulting in an initial satisfactory swinging motion in both subjects. Intersubject differences appeared in the mechanical output (torque joint) per muscle group. During a prolonged open-loop controlled trial with the optimised but unregulated stimulation onsets and burst duration for the three muscle groups, the hip angle range per cycle initially increased above the desired value and subsequently decreased below it. The mechanical performance of the hamstrings and quadriceps remained relatively unaffected. A cycle-to-cycle controller was then designed, operating on the basis of the hip angle ranges obtained in previous swings. This controller successfully adapted the burst duration of the hip flexors to maintain the desired hip angle range.

Sensory nerve recording for closed-loop control to restore motor functions

A method is developed for using neural recordings to control functional electrical stimulation (FES) to nerves and muscles. Experiments were done in chronic cats with a goal of designing a rule-based controller to generate rhythmic movements of the ankle joint during treadmill locomotion. Neural signals from the tibial and superficial peroneal nerves were recorded with cuff electrodes and processed simultaneously with muscular signals from ankle flexors and extensors in the cat's hind limb. Cuff electrodes are an effective method for long-term chronic recording in peripheral nerves without causing discomfort or damage to the nerve. For real-time operation we designed a low-noise amplifier with a blanking circuit to minimize stimulation artifacts. We used threshold detection to design a simple rule-based control and compared its output to the pattern determined using adaptive neural networks. Both the threshold detection and adaptive networks are robust enough to accommodate the variability in neural recordings. The adaptive logic network used for this study is effective in mapping transfer functions and therefore applicable for determination of gait invariants to be used for closed-loop control in an FES system. Simple rule-bases will probably be chosen for initial applications to human patients. However, more complex FES applications require more complex rule-bases and better mapping of continuous neural recordings and muscular activity. Adaptive neural networks have promise for these more complex applications.

Lower extremity applications of functional neuromuscular stimulation

Functional electrical stimulation (FES) applications in the lower extremity are common in research laboratories, but clinical applications are minimal. This review summarizes current knowledge with respect to clinical application. When electrical stimulation is used in clinical applications for functional movement such as standing and walking, it is typically applied in an open-loop manner; a predetermined stimulus pattern is delivered regardless of the consequences of the actual movement. Few clinical applications of FES involve closed-loop control because of the numerous difficulties involved in its application. As with any volitional muscle contraction, electrically stimulated muscle contractions will exhibit fatigue. Although the dynamics of fatigue may differ, electrically stimulated muscle contractions cannot be continuously sustained, and if the duty cycle is too severe, even alternating periods of rest and contraction cannot be sustained at a constant force level. The exact nature of fatigue is highly specific to the past history of the individual muscle and to the individual subject. Despite their intricate detail, quantitative modeling studies have not yet been applied extensively to clinical applications. Present implantable systems are not yet a viable option for clinical application. It is not clear whether more success with surface or percutaneous systems must first be achieved to justify implantation or whether greater improvements in implantable technology and surgical protocols are needed before implantable systems will become practical. It is clear that almost any reasonably designed stimulation protocol will increase muscle bulk. The existence of other therapeutic benefits and their cost/benefit ratios remain to be fully established. It is possible to stand through bilateral stimulation of the quadriceps. Using surface electrodes, this technique is achievable in any physical therapy clinic having minimal expertise in neuromuscular stimulation. FES-aided standing must be conducted as a research project with a protocol approved by the local institutional review board, as there are currently no FDA-approved stimulation devices for standing. Multichannel FES systems are not currently available for clinical application in the United States. This may change if the "Parastep" system receives FDA approval. Percutaneous and implanted systems are years away from commercialization and clinical availability. Hybrid systems, based primarily on the reciprocating gait orthosis (RGO), are presently the only clinically available form of walking that includes some form of FES assistance. The costs and benefits of adding FES to the RGO and the long-term user acceptance rate for these systems remain to be determined.

Control of FES-induced cyclical movements of the lower leg

The control of a cyclical movement of the lower leg with electrical stimulation of the quadriceps muscles is formulated as an optimal control problem. The time integral of knee torque is taken as the optimisation criterion. As an additional condition, every cycle a certain reference maximum angle should be reached. A model study indicates that one stimulation burst per cycle at the maximum recruitment level is a suboptimal solution to this problem. To compensate for the influence of muscle fatigue, the burst time is adaptively adjusted by a discrete time PID-controller on the basis of the performance in the previous cycles. This strategy appeared to be successful in experimental tests. A considerable time difference (about 0.15 s) was found between the end of the stimulation burst and the tracking of the passive state trajectory, which satisfies the maximum angle condition.

Regulating knee joint position by combining electrical stimulation with a controllable friction brake

Hybrid FES gait restoration systems which combine stimulation with controllable mechanical damping elements at the joints show promise for providing good control of limb motion despite variations in muscle properties. In this paper we compared three controllers for position tracking of the free swinging shank in able-bodied subjects. The controllers were open-loop (OL), proportional-derivative closed-loop (PD), and bang-bang plus controlled-brake control (CB). Both OL and PD controllers contained a forward path element, which inverted a model of the electrically stimulated muscle and limb system. The CB control was achieved by maximally activating the appropriate muscle group and controlling the brake to be a "moving-wall" against which the limb pushed. The CB control resulted in superior tracking performance for a wide range of position tracking tasks and muscle fatigue states but required no calibration or knowledge of muscle properties. The disadvantages of CB control include excess mechanical power dissipation in the brake and impact forces applied to the skeletal system.