Adaptive restriction rules provide functional and safe stimulation pattern for foot drop correction

Feasibility of using peroneal nerve recordings for deriving stimulation timing in a foot drop correction system

The objective of this research was to demonstrate the potential of using peroneal nerve activity to derive timing control for stimulation in foot drop correction and to attempt recording and stimulation through the same electrode. Two subjects were implanted with cuff electrodes on the peroneal nerve. An input domain was derived from the recorded electroneurogram (ENG) and fed to a detection algorithm based on an Adaptive Logic Network (ALN) for predicting stimulation timing. A switching circuit was furthermore built for switching between stimulator and recorder for combined use of the cuff electrode.  The detection was successful, but the accuracy depended on the signal to noise ratio of the recorded ENG. The switching circuit successfully allowed for simultaneous recording and stimulation through the same cuff electrode. We conclude that the peroneal nerve can potentially be used to record sensory information for derivation of a stimulator control signal in a foot drop application, while at the same time being stimulated to activate foot dorsiflexors.

Gait phase detection from sciatic nerve recordings in functional electrical stimulation systems for foot drop correction

Cutaneous afferent activities recorded by a nerve cuff electrode have been used to detect the stance phase in a functional electrical stimulation system for foot drop correction. However, the implantation procedure was difficult, as the cuff electrode had to be located on the distal branches of a multi-fascicular nerve to exclude muscle afferent and efferent activities. This paper proposes a new gait phase detection scheme that can be applied to a proximal nerve root that includes cutaneous afferent fibers as well as muscle afferent and efferent fibers. To test the feasibility of this scheme, electroneurogram (ENG) signals were measured from the rat sciatic nerve during treadmill walking at several speeds, and the signal properties of the sciatic nerve were analyzed for a comparison with kinematic data from the ankle joint. On the basis of these experiments, a wavelet packet transform was tested to define a feature vector from the sciatic ENG signals according to the gait phases. We also propose a Gaussian mixture model (GMM) classifier and investigate whether it could be used successfully to discriminate feature vectors into the stance and swing phases. In spite of no significant differences in the rectified bin-integrated values between the stance and swing phases, the sciatic ENG signals could be reliably classified using the proposed wavelet packet transform and GMM classification methods.

Integrating sensory nerve signals into neural prosthesis devices

Electrical signals can be recorded using long-term implanted nerve cuff electrodes in human peripheral nerves. Reliable detection of sensory nerve signals is essential if such signals are to be of use in sensory-based functional electrical stimulation neural prosthetics as a replacement for artificial sensors (switches, strain gauges, etc.). In this review, the signal characteristics of the sensors, the nerve interface, signal processing, and an example of human application to restore motor functions are described.

Compression-caused peroneal neuropathy: commentary from a biopsychologist

Compression is the most common cause of damage to the fibular head, the site of most peroneal nerve injuries which cause foot drop. Compression injuries can be caused by prolonged immobility and habitual leg-crossing. A review of the literature does not reveal the existence of a nationwide study that investigates the prevalence of compression-caused foot drop, nor does the literature contain encouragement to arrange medical practices to prevent its occurrence (e.g., soft substrates for sitting, frequent reminders for the patient to uncross the legs). Treatments for foot drop do not appear to be strongly scientifically based and they do not incorporate the use of sensory integration, specifically use of the visual sense, during rehabilitation. Finally, compression-caused foot drop may be preventable, a conclusion that could ultimately have important implications in the context of Medicare and Medicaid reimbursement.

On the use of longitudinal intrafascicular peripheral interfaces for the control of cybernetic hand prostheses in amputees

Significant strides have been recently made to develop highly sensorized cybernetic prostheses aimed at restoring sensorimotor limb functions to those who have lost them because of a traumatic event (amputation). In these cases, one of the main goals is to create a bidirectional link between the artificial devices (e.g., robotic hands, arms, or legs) and the nervous system. Several human-machine interfaces (HMIs) are currently used to this aim. Among them, interfaces with the peripheral nervous system and in particular longitudinal intrafascicular electrodes can be a promising solution able to improve the current situation. In this paper, the potentials and limits of the use of this interface to control robotic devices are presented. Specific information is provided on: 1) the neurophysiological bases for the use peripheral nerve interfaces; 2) a comparison of the potentials of the different peripheral neural interfaces; 3) the possibility of extracting and appropriately interpreting the neural code for motor commands and of delivering sensory feedback by stimulating afferent fibers by using longitudinal intrafascicular electrodes; 4) a preliminary comparative analysis of the performance of this approach with the ones of others HMIs; 5) the open issues which have to be addressed for a chronic usability of this approach.

Clinical application of sensory protection of denervated muscle

Following proximal peripheral nerve injury, motor recovery is often poor due to prolonged muscle denervation and loss of regenerative potential. The transfer of a sensory nerve to denervated muscle results in improved functional recovery in experimental models. The authors here report the first clinical case of sensory protection. Following a total hip arthroplasty, this patient experienced a complete sciatic nerve palsy with no recovery at 3 months postsurgery and profound denervation confirmed electrodiagnostically. He underwent simultaneous neurolysis of the sciatic nerve and saphenous nerve transfers to the tibialis anterior branch of the peroneal nerve and gastrocnemius branch from the tibial nerve. He noted an early proprioceptive response. Electromyography demonstrated initially selective amelioration of denervation potentials followed by improved motor recovery in sensory protected muscles only. The patient reported clinically significant functional improvements in activities of daily living. The authors hypothesize that the presence of a sensory nerve during muscle denervation can improve functional motor recovery.

Clinical application of acceleration sensor to detect the swing phase of stroke gait in functional electrical stimulation

Functional electrical stimulation (FES) can improve the gait of stroke patients by stimulating the peroneal nerve in the swing phase of the affected leg, causing dorsiflexion of the foot that allows the toes to clear the ground. A sensor can trigger the electrical stimulation automatically during the stroke gait. We previously used a heel sensor system, which detects the contact pressure of the heel, in FES to correct foot drop gait. However, the heel sensor has disadvantages in cosmetics and durability. Therefore, we have replaced the heel sensor with an acceleration sensor that can detect the swing phase based on the acceleration speed of the affected leg, using a machine learning technique (Neural Network). We have used a signal for heel contact in a gait using the heel sensor before training with the Neural Network. The accuracy of the Neural Network detector was compared with a swing phase detector based on the heel sensor. The Neural Network detector was able to detect similarly the swing phase in the heel sensor. The largest difference in timing of the swing phase was less than 60 milliseconds in normal subjects and 80 milliseconds in stroke patients. We were able to correct foot drop gait using FES with an acceleration sensor and Neural Network detector. The present results indicate that an acceleration sensor positioned on the thigh, which is cosmetically preferable to systems in which the sensor is farther from the entry point of the electrodes, is useful for correction of stroke gait using FES.

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.

Context-Dependent Modulation of Interlimb Cutaneous Reflexes in Arm Muscles as a Function of Stability Threat During Walking

Cutaneous reflexes evoked in the muscles of the arms with electrical stimulation of nerves of the foot (“interlimb reflexes”) are observed during walking. These reflexes have been suggested to coordinate the actions of the legs and arms when walking is disturbed. Recently, we showed that cutaneous reflexes evoked in the leg muscles after stimulation at the foot are modulated according to the level of postural threat during walking. We hypothesized that the amplitude of interlimb cutaneous reflexes would similarly be modulated when subjects walk in unstable environments. Subjects walked on a treadmill under four walking conditions: 1) normal; 2) normal with unpredictable anterior–posterior (AP) perturbations; 3) arms crossed; and 4) arms crossed with unpredictable AP perturbations. Interlimb reflexes evoked from electrical stimulation of the right superficial peroneal or sural nerves were recorded bilaterally, at four points of the step cycle. These reflexes were compared between conditions in which the arms were moving in a similar manner: 1) normal versus AP walking and 2) arms crossed versus arms crossed with AP perturbations. Differences in reflex amplitudes between arms-crossed conditions were observed in most upper limb muscles when subjects were perturbed while walking compared with undisturbed walking. This effect was less apparent when the arms were swinging freely. The results indicate that the strength of interlimb connections is influenced by the level of postural threat (i.e., the context of the behavior), thereby suggesting that these reflexes serve a functional link between the legs and arms during locomotion.

Evaluating robustness of gait event detection based on machine learning and natural sensors

A real-time system for deriving timing control for functional electrical stimulation for foot-drop correction, using peripheral nerve activity as a sensor input, was tested for reliability to investigate the potential for clinical use. The system, which was previously reported on, was tested on a hemiplegic subject instrumented with a recording cuff electrode on the Sural nerve, and a stimulation cuff electrode on the Peroneal cuff. Implanted devices enabled recording and stimulation through telelinks. An input domain was derived from the recorded electroneurogram and fed to a detection algorithm based on an adaptive logic network for controlling the stimulation timing. The reliability was tested by letting the subject wear different foot wear and walk on different surfaces than when the training data was recorded. The detection system was also evaluated several months after training. The detection system proved able to successfully detect when walking with different footwear on varying surfaces up to 374 days after training, and thereby showed great potential for being clinically useful.

Biopotentials as command and feedback signals in functional electrical stimulation systems

Today Functional Electrical Stimulation (FES) is available as a clinical tool in muscle activation used for picking up objects, for standing and walking, for controlling bladder emptying, and for breathing. Despite substantial progress in development and new knowledge, many challenges remain to be resolved to provide a more efficient functionality of FES systems. The most important task of these challenges is to improve control of the activated muscles through open loop or feedback systems. Command and feedback signals can be extracted from biopotentials recorded from muscles (Electromyogram, EMG), nerves (Electroneurogram, ENG), and the brain (Electroencephalogram (EEG) or individual cells). This paper reviews work in which EMG, ENG, and EEG signals in humans have been used as command and feedback signals in systems using electrical stimulation of motor nerves to restore movements after an injury to the Central Nervous System (CNS). It is concluded that the technology is ready to push for more substantial clinical FES investigations in applying muscle and nerve signals. Brain-computer interface systems hold great prospects, but require further development of faster and clinically more acceptable technologies.

Real-time gait event detection for paraplegic FES walking

A real-time method for the detection of gait events that occur during the electrically stimulated locomotion of paraplegic subjects is described. It consists of a two-level algorithm for the processing of sensor signals and the determination of gait event times. Sensor signals and information about the progression of the stimulator though its pre-specified stimulation "pattern" are processed by a machine intelligence (fuzzy logic) algorithm to determine an initial estimate of the patient's current phase of gait. This is then reviewed and modified by a second algorithm that removes spurious gait estimates, and determines gait event times. These gait event times are known to the system within approximately one-half of a gait cycle. The resulting gait event detection system was successfully evaluated on three subjects. Detection accuracy is not adversely affected by day-to-day gait variability. This work resolved technical and practical issues that previously limited the real time application of these methods. In particular, cosmetically acceptable insole force transducers were used. This gait event detector is designed for use in a real time controller for the automatic adjustment of the intensity and timing of stimulation while the subject is walking using functional electrical stimulation (FES).

Sensor systems for lower limb functional electrical stimulation (FES) control

Two sensor systems comprising clusters of accelerometers, magnetic sensors, a rate gyroscope, and a strain gauge were designed. For one system, the clusters were located at the belt and AFO. In the other system, the clusters were located at the AFO and the thigh. The maximum cluster size was 14 cm(3) and 75 g. The clusters of each sensor system were interconnected by a single flexible wire bus, which minimized the effects of cabling. The sensors detected five phases of normal gait to a resolution of 40 ms in an able bodied test. Using a threshold method, the sensor system repeatedly predicted an incipient knee buckle in a paraplegic individual by a minimum of 30 ms. One system detected knee flexion angle analytically to an accuracy of 3.2 degrees during sit to stand trials. The second system determined knee and hip flexion angle to an accuracy of 3.8 degrees during sit to stand trials through neural networks. The signal processing of the acquired sensor signals in each system was performed on a MC68332 microcomputer in conjunction with the data sampling, and suggested the possibility for each sensor system to be used in real time control of FES.