An adaptive low-dimensional control to compensate for actuator redundancy and FES-induced muscle fatigue in a hybrid neuroprosthesis
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Keywords:
Functional electrical stimulation
Feed forward
Neuroprosthetics
Powered exoskeleton
Inverse dynamics
Muscle Fatigue
Functional electrical stimulation (FES) is an external application of electrical pulses to skeletal
muscles to produce desired limb movements. It is prescribed as a rehabilitation intervention
to restore standing and walking functions in people with paraplegia. However, its
clinical implementation is hindered by a rapid onset of muscle fatigue that limits its use for
longer durations. To overcome the FES-induced muscle fatigue, hybrid neuroprostheses that
combine FES with powered exoskeletons were proposed recently. However, how to coordinate
FES and powered exoskeleton in a hybrid neuroprosthesis still remains an open issue.
The long-term goal of this research is to develop control methods that can optimally
coordinate FES and the powered exoskeleton by considering muscle fatigue dynamics during
standing and walking activities. The research objective in this dissertation was to derive
robust and adaptive optimal control methods for two hybrid neuroprostheses: a hybrid leg
extension machine (HLEM) and a full lower-body neuroprosthesis (FLBN).
Firstly, a model predictive control (MPC) method that coordinates FES and an electric
motor in the HLEM is developed. However, due to inaccurate system identification, day-today
variations in the model, and partially measurable state, it is challenging to implement
this method in a clinical setting. Therefore, robust and adaptive versions of the MPC method
were derived. To overcome modeling uncertainties, a tube-based robust MPC was derived.
This MPC has a feedback controller that can drive the actual state into a region centered by
the nominal state. This ensures recursive feasibility and stability despite disturbances. Later,
a recurrent neural network (RNN) was developed to capture the non-autonomous behavior in the musculoskeletal system, and then a nonlinear MPC and a reinforcement learning (RL)
method were derived to sub-optimally compute the control actions for the system. To achieve
a standing-up motion, a ratio-allocation method was developed to determine the ratio of the
FES-induced torque to the motor torque at the knee joint. The dynamically varied estimated
muscle fatigue was used as an index that guided the optimal allocation. Experiments were
performed to validate the robust and adaptive methods. The results show a potential of the
proposed methods for clinical implementation.
Neuroprosthetics
Functional electrical stimulation
Rehabilitation engineering
Powered exoskeleton
Model Predictive Control
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Neuroprosthetics
Functional electrical stimulation
Rehabilitation engineering
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Neuroprosthesis (NP) is an emerging rehabilitation technique for replacing a function that is lost because of injury or disease of the central nervous system (CNS). A new, micro-controller-based portable neuromuscular stimulation device for drop-foot correction and other assistive applications has been developed with low cost. The portable stimulator features fully programmable, constant current, single-channel stimulation system. Rectangular stimulation intensity envelope algorithms are provided for drop foot correction and for other assistive applications. The effectiveness of functional electrical stimulation (FES) has been investigated in chronic hemiplegia. The reported benefits has shown, a significant reduction in physiological cost index (PCI), increasing walking speed, increasing step length, cadence, and reduction in composite spasticity score, accompanied by an increase in electromyogram analysis in the FES therapy group.
Neuroprosthetics
Functional electrical stimulation
Cadence
Foot drop
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Neuroprosthetics
Functional electrical stimulation
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Abstract Background FES (Functional Electrical Stimulation) neuroprostheses have long been a permanent feature in the rehabilitation and gait support of people who had a stroke or have a Spinal Cord Injury (SCI). Over time the well-known foot switch triggered drop foot neuroprosthesis, was extended to a multichannel full-leg support neuroprosthesis enabling improved support and rehabilitation. However, these neuroprostheses had to be manually tuned and could not adapt to the persons’ individual needs. In recent research, a learning controller was added to the drop foot neuroprosthesis, so that the full stimulation pattern during the swing phase could be adapted by measuring the joint angles of previous steps. Methods The aim of this research is to begin developing a learning full-leg supporting neuroprosthesis, which controls the antagonistic muscle pairs for knee flexion and extension, as well as for ankle joint dorsi- and plantarflexion during all gait phases. A method was established that allows a continuous assessment of knee and foot joint angles with every step. This method can warp the physiological joint angles of healthy subjects to match the individual pathological gait of the subject and thus allows a direct comparison of the two. A new kind of Iterative Learning Controller (ILC) is proposed which works independent of the step duration of the individual and uses physiological joint angle reference bands. Results In a first test with four people with an incomplete SCI, the results showed that the proposed neuroprosthesis was able to generate individually fitted stimulation patterns for three of the participants. The other participant was more severely affected and had to be excluded due to the resulting false triggering of the gait phase detection. For two of the three remaining participants, a slight improvement in the average foot angles could be observed, for one participant slight improvements in the averaged knee angles. These improvements where in the range of 4 c i r c at the times of peak dorsiflexion, peak plantarflexion, or peak knee flexion. Conclusions Direct adaptation to the current gait of the participants could be achieved with the proposed method. The preliminary first test with people with a SCI showed that the neuroprosthesis can generate individual stimulation patterns. The sensitivity to the knee angle reset, timing problems in participants with significant gait fluctuations, and the automatic ILC gain tuning are remaining issues that need be addressed. Subsequently, future studies should compare the improved, long-term rehabilitation effects of the here presented neuroprosthesis, with conventional multichannel FES neuroprostheses.
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Functional electrical stimulation
Foot drop
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Lower-limb rehabilitation for spinal cord injury (SCI) and other motor disorders is often a lengthy process for the patient. The combination of active orthoses and functional electrical stimulation (FES) promises to accelerate therapy outcome, while simultaneously reducing the physical burden of the therapist. In this work, we propose a controller to a hybrid neuroprosthesis (HNP) composed of a hip orthosis and FES-controlled knee motion. In our simulation analysis using a detailed musculoskeletal model, we use experimental data from an able-bodied subject during slow-speed walking to compare the performance provided by such a system. Furthermore, we analyzed the obtained results in comparison to gait data collected from experiments where we used an active hip orthosis. Although the knee stimulation controller still oscillated during gait, we acquired control results with errors smaller than five degrees. Besides, we were able to examine the performance at very slow speeds.
Functional electrical stimulation
Neuroprosthetics
Powered exoskeleton
Gait training
Motion Capture
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This paper proposed a neuroprosthesis system for lower limbs action based on functional electrical stimulation (FES) to facilitate patient-responsive ambulation by paralyzed patients with the sequelae of strokes and spinal cord injure. This neuroprosthesis system had four independent channels and seven modules in its hardware including controller, D/A converter, constant-current source, wave shaper, function keys, display device and power supply. To evaluate the system performance to assist standing, knee joint angular velocity were measured during hip stimulation on twelve subjects. Both the basic kinematics indicators of step index and time and knee joint angle under the best step threshold during lower leg stimulation were compared with those during normal walking. Experimental results showed the proposed system was reliable and may be widely used in rehabilitation clinics.
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This paper aims at presenting some research issues carried out by the SINPHA-11-068/2007 project which focus on the functional movement restoration of paralyzed limbs by the use of Functional Electrical Stimulation (FES). The main goal is to restore standing in paraplegia by means of the FES techniques and to test different control strategies that have to be embedded within a neuroprosthesis. Prior to use a neuroprosthesis on disabled people within a clinical environment, we propose that any new control strategy to be tested in simulation and on a mechatronic device that emulate the human body movements while an electrical stimulus is supposed to be applied over the controlled groups of muscles. Our tests have shown that a mechatronic device that mimic the human body movements while standing-up, standing and sitting-down in paraplegia may offer a better understanding on how to tune controller parameters for different control strategies that aim to support standing exercises in paraplegia.
Neuroprosthetics
Functional electrical stimulation
Paraplegia
Rehabilitation engineering
Sitting
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Neuroprosthetics
Functional electrical stimulation
Powered exoskeleton
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Through the application of functional electrical stimulation (FES) individuals with paraplegia can regain lost walking function. However, due to the rapid onset of muscle fatigue, the walking duration obtained with an FES-based neuroprosthesis is often relatively short. The rapid muscle fatigue can be compensated for by using a hybrid system that uses both FES and an active orthosis. In this paper, we demonstrate the initial testing of a semi-active hybrid walking neuroprosthesis. The semi-active hybrid orthosis (SEAHO) supports a user during the stance phase and standing while the electric motors attached to the hip section of the orthosis are used to generate hip flexion/extension. FES in SEAHO is mainly used to actuate knee flexion/extension and plantar flexion of the foot. SEAHO is controlled by a finite state machine that uses a recently developed nonlinear controller for position tracking control of the hip motors and cues from the hip angle to actuate FES and other components.
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Functional electrical stimulation
Orthotics
Paraplegia
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