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

MODEM: a multi-agent hierarchical structure to model the human motor control system

Abstract In this study, based on behavioral and neurophysiological facts, a new hierarchical multi-agent architecture is proposed to model the human motor control system. Performance of the proposed structure is investigated by simulating the control of sit to stand movement. To develop the model, concepts of mixture of experts, modular structure, and some aspects of equilibrium point hypothesis were brought together. We have called this architecture MODularized Experts Model (MODEM). Human motor system is modeled at the joint torque level and the role of the muscles has been embedded in the function of the joint compliance characteristics. The input to the motor system, i.e., the central command, is the reciprocal command. At the lower level, there are several experts to generate the central command to control the task according to the details of the movement. The number of experts depends on the task to be performed. At the higher level, a âgate selectorâ block selects the suitable subordinate expert considering the context of the task. Each expert consists of a main controller and a predictor as well as several auxiliary modules. The main controller of an expert learns to control the performance of a given task by generating appropriate central commands under given conditions and/or constraints. The auxiliary modules of this expert learn to scrutinize the generated central command by the main controller. Auxiliary modules increase their intervention to correct the central command if the movement error is increased due to an external disturbance. Each auxiliary module acts autonomously and can be interpreted as an agent. Each agent is responsible for one joint and, therefore, the number of the agents of each expert is equal to the number of joints. Our results indicate that this architecture is robust against external disturbances, signal-dependent noise in sensory information, and changes in the environment. We also discuss the neurophysiological and behavioral basis of the proposed model (MODEM)

An Equilibrium Point based Model Unifying Movement Control in Humanoids

Abstract — Despite all the dynamics methods effectively used in robotics control, few tackle the intricacies of the human musculoskeletal system itself. During movements, a huge amount of energy can be stored passively in the biomechanics of the muscle system. Controlling such a system in a way that takes advantage of the stored energy has lead to the Equilibrium-point hypothesis (EPH). In this paper, we propose a two-phase model based on the EPH. Our model is simple and general enough to be extended to various motions of all body parts. In the first phase, gradient descent is used to obtain one kinematics endpoint in joint space, given a task in Cartesian space. In the second phase where the movements are actually executed, we use damped springs to simulate muscles to drive the limb joints. The model is demonstrated by a humanoid doing walking, reaching, and grasping. I