Fuzzy logic: A “simple” solution for complexities in neurosciences?

Background: Fuzzy logic is a multi-valued logic which is similar to human thinking and interpretation. It has the potential of combining human heuristics into computer-assisted decision making, which is applicable to individual patients as it takes into account all the factors and complexities of individuals. Fuzzy logic has been applied in all disciplines of medicine in some form and recently its applicability in neurosciences has also gained momentum.Methods: This review focuses on the use of this concept in various branches of neurosciences including basic neuroscience, neurology, neurosurgery, psychiatry and psychology.Results: The applicability of fuzzy logic is not limited to research related to neuroanatomy, imaging nerve fibers and understanding neurophysiology, but it is also a sensitive and specific tool for interpretation of EEGs, EMGs and MRIs and an effective controller device in intensive care units. It has been used for risk stratification of stroke, diagnosis of different psychiatric illnesses and even planning neurosurgical procedures.Conclusions: In the future, fuzzy logic has the potential of becoming the basis of all clinical decision making and our understanding of neurosciences

Functional Rehabilitation: Coordination of Artificial and Natural Controllers

International audienceWalking and standing abilities, though important for quality of life and participation in social and economic activities, can be adversely affected by central nervous system (CNS) disorders such as spinal cord injury, stroke or traumatic brain injury. One characteristic of motor deficiencies which affect lower extremities is their impact on both static and dynamic postural equilibrium. Depending on the impairment level, functional rehabilitation techniques may be needed for a patient to stand up and walk (Popovic and Sinkjær, 2003). Functional electrical stimulation (FES) can induce contraction of skeletal muscles by applying electrical stimuli to sensory-motor system via electrodes which can be placed on the skin (Kralj et al., 1983), or implanted (Guiraud et al., 2006). FES applications applied to lower limbs include foot drop correction, single joint control, cycling, standing up, walking... (Zhang and Zhu, 2007)..

Muscle Synergies Facilitate Computational Prediction of Subject-Specific Walking Motions.

Researchers have explored a variety of neurorehabilitation approaches to restore normal walking function following a stroke. However, there is currently no objective means for prescribing and implementing treatments that are likely to maximize recovery of walking function for any particular patient. As a first step toward optimizing neurorehabilitation effectiveness, this study develops and evaluates a patient-specific synergy-controlled neuromusculoskeletal simulation framework that can predict walking motions for an individual post-stroke. The main question we addressed was whether driving a subject-specific neuromusculoskeletal model with muscle synergy controls (5 per leg) facilitates generation of accurate walking predictions compared to a model driven by muscle activation controls (35 per leg) or joint torque controls (5 per leg). To explore this question, we developed a subject-specific neuromusculoskeletal model of a single high-functioning hemiparetic subject using instrumented treadmill walking data collected at the subject's self-selected speed of 0.5 m/s. The model included subject-specific representations of lower-body kinematic structure, foot-ground contact behavior, electromyography-driven muscle force generation, and neural control limitations and remaining capabilities. Using direct collocation optimal control and the subject-specific model, we evaluated the ability of the three control approaches to predict the subject's walking kinematics and kinetics at two speeds (0.5 and 0.8 m/s) for which experimental data were available from the subject. We also evaluated whether synergy controls could predict a physically realistic gait period at one speed (1.1 m/s) for which no experimental data were available. All three control approaches predicted the subject's walking kinematics and kinetics (including ground reaction forces) well for the model calibration speed of 0.5 m/s. However, only activation and synergy controls could predict the subject's walking kinematics and kinetics well for the faster non-calibration speed of 0.8 m/s, with synergy controls predicting the new gait period the most accurately. When used to predict how the subject would walk at 1.1 m/s, synergy controls predicted a gait period close to that estimated from the linear relationship between gait speed and stride length. These findings suggest that our neuromusculoskeletal simulation framework may be able to bridge the gap between patient-specific muscle synergy information and resulting functional capabilities and limitations

Feasibility of Using an Equilibrium Point Strategy to Control Reaching Movements of Paralyzed Arms with Functional Electrical Stimulation

Functional electrical stimulation (FES) is a technology capable of improving the quality of life for those with the loss of limb movement related to spinal cord injuries. Individuals with high-level tetraplegia, in particular, have lost all movement capabilities below the neck. FES has shown promise in bypassing spinal cord damage by sending electrical impulses directly to a nerve or muscle to trigger a desired function. Despite advancements in FES, full-arm reaching motions have not been achieved, leaving patients unable to perform fundamental tasks such as eating and grooming. To overcome the inability in current FES models to achieve multi-joint coordination, a controller utilizing muscle activations to achieve full-arm reaching motions using equilibrium point control on a computer-simulated human arm was developed. Initial simulations performed on the virtual arm generated muscle activations and joint torques required to hold a static position. This data was used as a model for Gaussian Process Regression to obtain muscle activations required to hold any desired static position. The accuracy of the controller was tested on twenty joint positions and was capable of holding the virtual arm within a mean of 1.1 ± 0.13 cm from an original target position. Once held in a static position, external forces were introduced to the simulation to evaluate if muscle activations returned the arm towards the original position after being moved away within a basin of attraction. It was found that the basin of attraction was limited to a 15 cm sphere around the joint position, regardless of position in the workspace. Muscle activations were then tested and found to successfully perform movements between points within the basin. The development of a controller capable of equilibrium point controlled movement is the initial step in recreating these movements in high-level tetraplegia patients with an implanted FES

Feasibility of Using an Equilibrium Point Strategy to Control Reaching Movements of Paralyzed Arms with Functional Electrical Stimulation

Functional electrical stimulation (FES) is a technology capable of improving the quality of life for those with the loss of limb movement related to spinal cord injuries. Individuals with high-level tetraplegia, in particular, have lost all movement capabilities below the neck. FES has shown promise in bypassing spinal cord damage by sending electrical impulses directly to a nerve or muscle to trigger a desired function. Despite advancements in FES, full-arm reaching motions have not been achieved, leaving patients unable to perform fundamental tasks such as eating and grooming. To overcome the inability in current FES models to achieve multi-joint coordination, a controller utilizing muscle activations to achieve full-arm reaching motions using equilibrium point control on a computer-simulated human arm was developed. Initial simulations performed on the virtual arm generated muscle activations and joint torques required to hold a static position. This data was used as a model for Gaussian Process Regression to obtain muscle activations required to hold any desired static position. The accuracy of the controller was tested on twenty joint positions and was capable of holding the virtual arm within a mean of 1.1 ± 0.13 cm from an original target position. Once held in a static position, external forces were introduced to the simulation to evaluate if muscle activations returned the arm towards the original position after being moved away within a basin of attraction. It was found that the basin of attraction was limited to a 15 cm sphere around the joint position, regardless of position in the workspace. Muscle activations were then tested and found to successfully perform movements between points within the basin. The development of a controller capable of equilibrium point controlled movement is the initial step in recreating these movements in high-level tetraplegia patients with an implanted FES

Extracting Kinematic Parameters for Monkey Bipedal Walking from Cortical Neuronal Ensemble Activity

The ability to walk may be critically impacted as the result of neurological injury or disease. While recent advances in brain–machine interfaces (BMIs) have demonstrated the feasibility of upper-limb neuroprostheses, BMIs have not been evaluated as a means to restore walking. Here, we demonstrate that chronic recordings from ensembles of cortical neurons can be used to predict the kinematics of bipedal walking in rhesus macaques – both offline and in real time. Linear decoders extracted 3D coordinates of leg joints and leg muscle electromyograms from the activity of hundreds of cortical neurons. As more complex patterns of walking were produced by varying the gait speed and direction, larger neuronal populations were needed to accurately extract walking patterns. Extraction was further improved using a switching decoder which designated a submodel for each walking paradigm. We propose that BMIs may one day allow severely paralyzed patients to walk again

Development of a Neuromechanical Model for Investigating Sensorimotor Interactions During Locomotion

Recently it has been suggested that the use of neuromechanical simulations could be used to further our understanding of the neural control mechanisms involved in the control of animal locomotion. The models used to carry out these neuromechanical simulations typically consist of a representation of the neural control systems involved in walking and a representation of the mechanical locomotor apparatus. These separate models are then integrated to produce motion of the locomotor apparatus based on signals that are generated by the neural control models. Typically in past neuromechanical simulations of human walking the parameters of the neural control model have been specifically chosen to produce a walking pattern that resembles the normal human walking pattern as closely as possible. Relatively few of these studies have systematically tested the effect of manipulating the control parameters on the walking pattern that is produced by the locomotor apparatus. The goal of this thesis was to develop models of the locomotor control system and the human locomotor apparatus and systematically manipulate several parameters of the neural control system and determine what effects these parameters would have on the walking pattern of the mechanical model. Specifically neural control models were created of the Central Pattern Generator (CPG), feedback mechanisms from muscle spindles and contact sensors that detect when the foot was contact with the ground. Two models of the human locomotor apparatus were used to evaluate the outputs of the neural control systems; the first was a rod pendulum, which represented a swinging lower-limb, while the second was a 5-segment biped model, which included contact dynamics with the ground and a support system model to maintain balance. The first study of this thesis tested the ability of a CPG model to control the frequency and amplitude of the pendulum model of the lower-limb, with a strictly feedforward control mechanism. It was found that the frequency of the pendulum’s motion was directly linked (or entrained) to the frequency of the CPG’s output. It was also found that the amplitude of the pendulum’s motion was affected by the frequency of the CPG’s output, with the greatest amplitude of motion occurring when the frequency of the CPG matched the pendulum’s natural frequency. The effects of altering several other parameters of the pendulum model, such as the initial angle, the magnitude of the applied viscous damping or the moment arms of the muscles, were also analyzed. The second study again used the pendulum model, and added feedback to the neural control model, via output from simulated muscle spindles. The output from these spindle models was used to trigger a simulated stretch reflex. It was found that the addition of feedback led to sensory entrainment of the CPG output to the natural frequency of the pendulum. The effects of altering the muscle spindle’s sensitivity to length and velocity changes were also examined. The ability of this type of feedback system to respond to mechanical perturbations was also analyzed. The third and fourth studies used a biped model of the musculoskeletal system to assess the effects of altering the parameters of the neural control systems that were developed in the first two studies. In the third study, the neural control system consisted only of feedforward control from the CPG model. It was found that the walking speed of the biped model could be controlled by altering the frequency of the CPG’s output. It was also observed that variability of the walking pattern was decreased when there was a moderate level of inhibition between the CPGs of the left and right hip joints. The final study added feedback from muscle receptors and from contact sensors with the ground. It was found that the most important source of feedback was from the contact sensors to the extensor centres of the CPG. This feedback increased the level of extensor activity and produced significantly faster walking speeds when compared to other types of feedback. This thesis was successful in testing the effects of several control parameters of the neural control system on the movement of mechanical systems. Particularly important findings included the importance of connectivity between the CPGs of the left and right hip joints and positive feedback regarding the loading of the limb for establishing an appropriate forward walking speed. It is hoped that the models developed in this thesis can form the basis of future neuromechanical models and that the simulations carried out in this thesis help provide a better understanding of the interactions between neural and mechanical systems during the control of locomotion

Multiple motor-unit muscle models for the design of FES systems

Many functional electrical stimulation (FES) controllers have been developed using a simulation approach, the performance of these controllers depends on the muscle model accuracy. Realistic models of neuro-musculoskeletal systems can provide a safe and convenient environment for the design and evaluation of FES controllers. A typical FES system consists of FES controller, an electrical stimulator, electrodes and sensors.During FES, the stimulation level can change in a continuous fashion such that different motor-units are recruited at different muscle lengths and at different times. Furthermore, it is also not accurate to use the instantaneous length as input to the force-length relationship in dynamic (non-isometric) situations. Although instantaneous CE length is commonly used in FES control studies, empirical data from the literature were reviewed and it was concluded that the CE length at initial recruitment is a key parameter influencing total muscle force. The author presents a new multiple motor-unit Hill-type muscle model that accounts for different motor units being recruited at different CE lengths and different times. Hence the model can account for a continuously changing recruitment level whilst using the individual motor unit lengths at initial recruitment as input to the force-length relationship. Moreover, the model is capable of modelling fatigue and force enhancement & depression for the individual motor-units (i.e. the recruitment and time history effects). The model can also take account of the different force-length and force-velocity relationships for different fibre types by modelling these properties for the individual motor-units.The new multiple motor-unit model is described in detail, implemented and tested in Matlab. Open-loop simulation protocols are made on single/multiple motor-unit models using different CE lengths for the force-length relationship; on single/multiple motor-unit fatigue sub-models; and on single/ multiple motor-unit force enhancement & depression sub-models.A general model that can be used to represent all relevant models from the literature was developed. This model can also be used to build new models at different levels of complexity. Such a “General Model” could be used to study the effect of model complexity on FES controller design so that appropriate trade-offs between model complexity and accuracy could be determined. Results, limitations and possible future work are discussed