Synergistic control of forearm based on accelerometer data and artificial neural networks.

In the present study, we modeled a reaching task as a two-link mechanism. The upper arm and forearm motion trajectories during vertical arm movements were estimated from the measured angular accelerations with dual-axis accelerometers. A data set of reaching synergies from able-bodied individuals was used to train a radial basis function artificial neural network with upper arm/forearm tangential angular accelerations. The trained radial basis function artificial neural network for the specific movements predicted forearm motion from new upper arm trajectories with high correlation (mean, 0.9149-0.941). For all other movements, prediction was low (range, 0.0316-0.8302). Results suggest that the proposed algorithm is successful in generalization over similar motions and subjects. Such networks may be used as a high-level controller that could predict forearm kinematics from voluntary movements of the upper arm. This methodology is suitable for restoring the upper limb functions of individuals with motor disabilities of the forearm, but not of the upper arm. The developed control paradigm is applicable to upper-limb orthotic systems employing functional electrical stimulation. The proposed approach is of great significance particularly for humans with spinal cord injuries in a free-living environment. The implication of a measurement system with dual-axis accelerometers, developed for this study, is further seen in the evaluation of movement during the course of rehabilitation. For this purpose, training-related changes in synergies apparent from movement kinematics during rehabilitation would characterize the extent and the course of recovery. As such, a simple system using this methodology is of particular importance for stroke patients. The results underlie the important issue of upper-limb coordination.

Synergistic control of forearm based on accelerometer data and artificial neural networks

In the present study, we modeled a reaching task as a two-link mechanism. The upper arm and forearm motion trajectories during vertical arm movements were estimated from the measured angular accelerations with dual-axis accelerometers. A data set of reaching synergies from able-bodied individuals was used to train a radial basis function artificial neural network with upper arm/forearm tangential angular accelerations. The trained radial basis function artificial neural network for the specific movements predicted forearm motion from new upper arm trajectories with high correlation (mean, 0.9149-0.941). For all other movements, prediction was low (range, 0.0316-0.8302). Results suggest that the proposed algorithm is successful in generalization over similar motions and subjects. Such networks may be used as a high-level controller that could predict forearm kinematics from voluntary movements of the upper arm. This methodology is suitable for restoring the upper limb functions of individuals with motor disabilities of the forearm, but not of the upper arm. The developed control paradigm is applicable to upper-limb orthotic systems employing functional electrical stimulation. The proposed approach is of great significance particularly for humans with spinal cord injuries in a free-living environment. The implication of a measurement system with dual-axis accelerometers, developed for this study, is further seen in the evaluation of movement during the course of rehabilitation. For this purpose, training-related changes in synergies apparent from movement kinematics during rehabilitation would characterize the extent and the course of recovery. As such, a simple system using this methodology is of particular importance for stroke patients. The results underlie the important issue of upper-limb coordination.

Neuromodulation of lower limb monoparesis: functional electrical therapy of walking.

After Cerebro-Vascular Accident (CVA), restoration of normal function, such as locomotion, depends on reorganization of existing central nervous system (CNS) circuitry. This capacity for reorganization, generally referred to as plasticity, is thought to underlie many instances of functional recovery after injury as well as learning and memory in the undamaged CNS. Both the reorganization of the supraspinal and spinal circuitry are highly important for the recovery of walking. The neural mechanisms responsible for learning and adapting processes are thought to involve changes both in the efficacy of synaptic function and the pattern of synaptic connections within neural circuits. In the uninjured CNS, these changes occur as a result of alterations in the amount of neural activity within circuits and are, therefore, termed activity-dependent. In this chapter, we will present several therapies of walking that provide effective input for the training of the existing CNS circuitry; thereby, contribute to long term recovery of sensory-motor functions. The focus of this chapter is Functional Electrical Therapy (FET) of walking, that is, the multi-channel electrical stimulation of sensory-motor systems that lead to more normal stance and swing of the paretic leg during the walking exercise.

New controller for functional electrical stimulation systems.

A novel, self-contained controller for functional electrical stimulation systems has been designed. The development was motivated by the need to have a general purpose, easy to use controller capable of stimulating many muscle groups, thus restoring complex motor functions (e.g. standing, walking, reaching, and grasping). The designed controller can regulate the frequency, pulse duration, and charge balance on up to 16 channels, and execute pre-programmed and sensory-driven control operations. The controller supports up to eight analog and six digital sensors, and comprises a memory block for including history of the sensory data (time series). Five independent timers provide the basis for the multi-modal and multi-level control of movement. The PC compatible interface is realised via an IR serial communication channel. The PC based software is user friendly and fully menu driven. This paper also presents a case study where the controller was implemented to restore walking in a paraplegic subject. The assistive system comprised the novel controller, the power and output stages of an eight-channel FES system (IEEE Trans Rehabil Eng, TRE-2 (1994) 234), ankle-foot orthoses, and a rolling walker. Stimulation was applied with surface electrodes positioned over the motoneurons that innervate muscles responsible for the hip and knee flexion and extension. The sensory system included goniometers at knee and hip joints, force-sensing resistors built in the shoe insoles, and digital accelerometers at the hips. A rule-based control algorithm was generated following a two-step procedure: (1) simulation and (2) machine learning as described in earlier studies (IEEE Trans Rehab Eng, TRE-7 (1999) 69). The paraplegic subject walked faster, and with less physiological effort, when automatic control was applied as compared to hand-control. This case study, as well as a previous one for assisting grasping (The design and testing of a new programmable electronic stimulator. N. Fisekovic, MS thesis. University of Belgrade, Belgrade, 2000) indicate that the novel control unit is effectively applicable to FES systems.

Estimating mechanical parameters of leg segments in individuals with and without physical disabilities.

Methods are described for estimating the inertia, viscosity, and stiffness of the lower leg around the knee and of the whole leg around the hip that are applicable even to persons with considerable spasticity. These involve: 1) a "pull" test in which the limb is slowly moved throughout its range of motion while measuring angles (with an electrogoniometer) and torques (with a hand-held dynamometer) to determine passive stiffness and 2) a "pendulum" test in which the limb is moved against gravity and then dropped, while again measuring angles and torques. By limiting the extent of the movement and choosing a direction (flexion or extension) that minimizes reflex responses, the mechanical parameters can be determined accurately and efficiently using computer programs. In the sample of subjects studied (nine with disability related to spinal cord injury, head injury, or stroke, and nine with no neurological disability), the inertia of the lower leg was significantly reduced in the subjects with disability (p < 0.05) as a result of atrophy, but the stiffness and viscosity were within normal limits. The values of inertia were also compared with anthropometric data in the literature. The identification of these passive parameters is particularly important in designing systems for functional electrical stimulation of paralyzed muscles, but the methods may be widely applicable in rehabilitation medicine.

Machine learning in control of functional electrical stimulation systems for locomotion.

Two machine learning techniques were evaluated for automatic design of a rule-based control of functional electrical stimulation (FES) for locomotion of spinal cord injured humans. The task was to learn the invariant characteristics of the relationship between sensory information and the FES-control signal by using off-line supervised training. Sensory signals were recorded using pressure sensors installed in the insoles of a subject's shoes and goniometers attached across the joints of the affected leg. The FES-control consisted of pulses corresponding to time intervals when the subject pressed on the manual push-button to deliver the stimulation during FES-assisted ambulation. The machine learning techniques used were the adaptive logic network (ALN) [1] and the inductive learning algorithm (IL) [2]. Results to date suggest that, given the same training data, the IL learned faster than the ALN, while both performed the test rapidly. The generalization was estimated by measuring the test errors and it was better with an ALN, especially if past points were used to reflect the time dimension. Both techniques were able to predict future stimulation events. An advantage of the ALN over the IL was that ALN's can be retrained with new data without losing previously collected knowledge. The advantages of the IL over the ALN were that the IL produces small, explicit, comprehensible trees and that the relative importance of each sensory contribution can be quantified.

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.

Electrical systems for improving locomotion after incomplete spinal cord injury: an assessment.

Simple systems for electrical stimulation (1-4 channels) with either surface, percutaneous, or implanted electrodes during locomotion were assessed in 10 subjects who had chronic, incomplete spinal cord injury (SCI). On average, the speed of locomotion was increased by 4 m/min independently of the subject's speed of locomotion without stimulation (0-50 m/min) while oxygen consumption was reduced somewhat. These simple systems can provide practical help, particularly for incomplete SCI subjects who can stand but are lacking or have very limited ability to walk. Further improvement in locomotion requires stabilization and reduction in the duration of the stance phase of locomotion.

Finite state model of locomotion for functional electrical stimulation systems.

A finite state model of locomotion was developed to simplify a controller design for motor activities of handicapped humans. This paper presents a model developed for real time control of locomotion with functional electrical stimulation (FES) assistive systems. Hierarchical control of locomotion was adopted with three levels: voluntary, coordination and actuator level. This paper deals only with coordination level of control. In our previous studies we demonstrated that a skill-based expert system can be used for coordination level of control in multi-joint FES systems. Basic elements in this skill-based expert system are production rules. Production rules have the form of If-Then conditional expressions. A technique of automatic determination of these conditional expressions is presented in this paper. This technique for automatic synthesis of production rules uses fuzzy logic and artificial neural networks (ANN). The special class of fuzzy logic elements used in this research is called preferential neurons. The preferential neurons were used to estimate the relevance of each of the sensory inputs to the recognition of patterns defined as finite states. The combination of preferential neurons forms a preferential neural network. The preferential neural network belongs to a class of ANNs. The preferential neural network determined the set of finite states convenient for a skill-based expert system for different modalities of locomotion.