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

A hybrid orthosis combining functional electrical stimulation and soft robotics for improved assistance of drop-foot

Drop-foot is characterised by an inability to lift the foot, and affects an estimated 3 million people worldwide. Current treatment methods include rigid splints, electromechanical systems, and functional electrical stimulation (FES). However, these all have limitations, with electromechanical systems being bulky and FES leading to muscle fatigue. This paper addresses the limitations with current treatments by developing a novel orthosis combining FES with a pneumatic artificial muscle (PAM). It is the first system to combine FES and soft robotics for application to the lower limb, as well as the first to employ a model of their interaction within the control scheme. The system embeds a hybrid controller based on model predictive control (MPC), which combines FES and PAM components to optimally balance gait cycle tracking, fatigue reduction and pressure demands. Model parameters are found using a clinically feasible model identification procedure. Experimental evaluation using the system with three healthy subjects demonstrated a reduction in fatigue compared with the case of only using FES, which is supported by numerical simulation results.

Football APP based on smart phone with FES in drop foot rehabilitation

BACKGROUND

Long-term, sustained progress is necessary in drop foot rehabilitation. The necessary inconvenient body training movements, the return trips to the hospital and repetitive boring training using functional electrical stimulation (FES) often results in the patient suspending their training. The patient's drop foot rehabilitation will not progress if training is suspended.

OBJECTIVE

A fast spread, highly portable drop foot rehabilitation training device based on the smart phone is presented. This device is combined with a self-made football APP and feedback controlled FES. The drop foot patient can easily engage in long term rehabilitation training that is more convenient and interesting.

METHODS

An interactive game is established on the smart phone with the Android system using the originally built-in wireless communications. The ankle angle information is detected by an external portable device as the game input signal. The electrical stimulation command to the external device is supplemented with FES stimulation for inadequate ankle efforts.

RESULTS

After six-weeks training using six cases, the results indicated that this training device showed significant performance improvement (p< 0.05) in the patient's ankle dorsiflexion strength, ankle dorsiflexion angle, control timing and Timed Up and Go.

CONCLUSIONS

Preliminary results show that this training device provides significant positive help to drop foot patients. Moreover, this device is based on existing and universally popular mobile processing, which can be rapidly promoted. The responses of clinical cases also show this system is easy to operate, convenient and entertaining. All of these features can improve the patient's willingness to engage in long term rehabilitation.

The adaptive drop foot stimulator - Multivariable learning control of foot pitch and roll motion in paretic gait

Many stroke patients suffer from the drop foot syndrome, which is characterized by a limited ability to lift (the lateral and/or medial edge of) the foot and leads to a pathological gait. In this contribution, we consider the treatment of this syndrome via functional electrical stimulation (FES) of the peroneal nerve during the swing phase of the paretic foot. A novel three-electrodes setup allows us to manipulate the recruitment of m. tibialis anterior and m. fibularis longus via two independent FES channels without violating the zero-net-current requirement of FES. We characterize the domain of admissible stimulation intensities that results from the nonlinearities in patients' stimulation intensity tolerance. To compensate most of the cross-couplings between the FES intensities and the foot motion, we apply a nonlinear controller output mapping. Gait phase transitions as well as foot pitch and roll angles are assessed in realtime by means of an Inertial Measurement Unit (IMU). A decentralized Iterative Learning Control (ILC) scheme is used to adjust the stimulation to the current needs of the individual patient. We evaluate the effectiveness of this approach in experimental trials with drop foot patients walking on a treadmill and on level ground. Starting from conventional stimulation parameters, the controller automatically determines individual stimulation parameters and thus achieves physiological foot pitch and roll angle trajectories within at most two strides.

Technical developments of functional electrical stimulation to correct drop foot: sensing, actuation and control strategies

This work presents a review on the technological advancements over the last decades of functional electrical stimulation based neuroprostheses to correct drop foot. Functional electrical stimulation is a technique that has been put into practice for several years now, and has been shown to functionally restore and rehabilitate individuals with movement disorders, such as stroke, multiple sclerosis and traumatic brain injury, among others. The purpose of this technical review is to bring together information from a variety of sources and shed light on the field's most important challenges, to help in identifying new research directions. The review covers the main causes of drop foot and its associated gait implications, along with several functional electrical stimulation-based neuroprostheses used to correct it, developed within academia and currently available in the market. These systems are thoroughly analyzed and discussed with particular emphasis on actuation, sensing and control of open- and closed-loop architectures. In the last part of this work, recommendations on future research directions are suggested.

Neuro-fuzzy decoding of sensory information from ensembles of simultaneously recorded dorsal root ganglion neurons for functional electrical stimulation applications

Functional electrical stimulation (FES) is used to improve motor function after injury to the central nervous system. Some FES systems use artificial sensors to switch between finite control states. To optimize FES control of the complex behavior of the musculo-skeletal system in activities of daily life, it is highly desirable to implement feedback control. In theory, sensory neural signals could provide the required control signals. Recent studies have demonstrated the feasibility of deriving limb-state estimates from the firing rates of primary afferent neurons recorded in dorsal root ganglia (DRG). These studies used multiple linear regression (MLR) methods to generate estimates of limb position and velocity based on a weighted sum of firing rates in an ensemble of simultaneously recorded DRG neurons. The aim of this study was to test whether the use of a neuro-fuzzy (NF) algorithm (the generalized dynamic fuzzy neural networks (GD-FNN)) could improve the performance, robustness and ability to generalize from training to test sets compared to the MLR technique. NF and MLR decoding methods were applied to ensemble DRG recordings obtained during passive and active limb movements in anesthetized and freely moving cats. The GD-FNN model provided more accurate estimates of limb state and generalized better to novel movement patterns. Future efforts will focus on implementing these neural recording and decoding methods in real time to provide closed-loop control of FES using the information extracted from sensory neurons.

A study on feedback error learning controller for functional electrical stimulation: generation of target trajectories by minimum jerk model

The Feedback Error Learning controller was found to be applicable to functional electrical stimulation control of wrist joint movements in control with subjects and computer simulation tests in our previous studies. However, sinusoidal trajectories were only used for the target joint angles and the artificial neural network (ANN) was trained for each trajectory. In this study, focusing on two-point reaching movement, target trajectories were generated by the minimum jerk model. In computer simulation tests, ANNs trained with different number of target trajectories under the same total number of control iterations (50 control trials) were compared. The inverse dynamics model (IDM) of the controlled limb realized by the trained ANN decreased the output power of the feedback controller and improved tracking performance to unlearned target trajectories. The IDM performed most effectively when target trajectory was changed every one control trial during ANN training.

The reliability of using accelerometer and gyroscope for gait event identification on persons with dropped foot

Identification of gait events using an optimal sensor set and a reliable algorithm would be useful in the clinical evaluation of patients with dropped foot. This article describes a threshold detection method for identifying gait events and evaluating the reliability of a system on ten subjects with dropped foot and three non-impaired controls. The system comprised three sensor units of accelerometers and gyroscopes attached at the thigh, shank and foot of the impaired leg in subjects with dropped foot, and the dominant leg in the controls. A performance index was devised to compare the values of different measuring directions of the sensor units and evaluate the system's reliability. The performance index, with the ideal value equal to 1, depended on the classification accuracy and timing variation of the turning points. These were obtained from the threshold detection method that distinguished the absolute maximum and minimum turning points from local maximum and minimum turning points. It was found that some specific turning points could effectively identify gait events with a high median value in the performance index. These turning points included: the minimum turning point in superior-inferior acceleration on the thigh at loading response (0.972); the minimum turning point in anterior-posterior angular velocity on the shank at pre-swing (0.955) and the maximum turning point in superior-inferior acceleration on the foot at initial swing (0.954). Combining the results of sensor measurements in different orientations and attachment locations could be used for gait event identification. It was shown that the threshold detection method is reliable. Portable gait-monitoring devices can be used for monitoring of daily activities and functional control.

Limb-state feedback from ensembles of simultaneously recorded dorsal root ganglion neurons

Functional electrical stimulation (FES) holds great potential for restoring motor functions after brain and spinal cord injury. Currently, most FES systems are under simple finite state control, using external sensors which tend to be bulky, uncomfortable and prone to failure. Sensory nerve signals offer an interesting alternative, with the possibility of continuous feedback control. To test feasibility, we recorded from ensembles of sensory neurons with microelectrode arrays implanted in the dorsal root ganglion (DRG) of walking cats. Limb position and velocity variables were estimated accurately (average R2 values >0.5) over a range of walking speeds (0.1-0.5 m s(-1)) using a linear combination of firing rates from 10 or more neurons. We tested the feasibility of sensory control of intraspinal FES by recording from DRG neurons during hindlimb movements evoked by intraspinal microstimulation of the lumbar spinal cord in an anesthetized cat. Although electrical stimulation generated artifacts, this problem was overcome by detecting and eliminating events that occurred synchronously across the array of microelectrodes. The sensory responses to limb movement could then be measured and decoded to generate an accurate estimate of the limb state. Multichannel afferent recordings may thus provide FES systems with the feedback needed for adaptive control and perturbation compensation, though long-term stability remains a challenge.