Using a virtual integration environment in treating phantom limb pain.

The Revolutionizing Prosthetics 2009 program conducted by the Defense Advanced Research Projects Agency (DARPA) has resulted in a Virtual Integration Environment (VIE) that provides a common development platform for researchers and clinicians that design, model and build prosthetic limbs and then integrate and test them with patients. One clinical need that arose during the VIE development was a feature to easily create and model animations that represent patient activities of daily living (ADLs) and simultaneously capture real-time surface EMG activity from the residual limb corresponding to the ADLs. An application of this feature is being made by the Walter Reed Military Amputee Research Program (MARP) where they are utilizing the VIE to investigate methods of reducing upper extremity amputee phantom limb pain (PLP).

Switching curve control of functional electrical stimulation assisted rowing exercise in paraplegia.

An indoor rowing machine has been modified for functional electrical stimulation (FES) assisted rowing exercise in paraplegia. To perform the rowing manoeuvre successfully, however, the voluntarily controlled upper body movements must be co-ordinated with the movements of the electrically stimulated paralysed legs. To achieve such co-ordination, an automatic FES controller was developed that employs two levels of hierarchy. At the upper level, a finite state controller identifies the state or phase of the rowing cycle and activates the appropriate lower-level controller, in which electrical stimulation to the paralysed leg muscles is applied with reference to switching curves representing the desired seat velocity as a function of the seat position. In a pilot study, the hierarchical control of FES rowing was shown to be intuitive, reliable and easy to use. Compared with open-loop control of stimulation, all three variants of the closed-loop switching curve controllers used less muscle stimulation per rowing cycle (73% of the open-loop control on average). Further, the closed-loop controller that used switching curves derived from normal rowing kinematics used the lowest muscle stimulation (65% of the open-loop control) and was the most convenient to use for the client.

Advanced modeling environment for developing and testing FES control systems.

Realistic models of neuromusculoskeletal systems can provide a safe and convenient environment for the design and evaluation of controllers for functional electrical stimulation (FES) prior to clinical trials. We have developed a set of integrated musculoskeletal modeling tools to facilitate the model building process. Simulink models of musculoskeletal systems are created using two software packages developed in our laboratory, Musculoskeletal Modeling in Simulink (MMS) and virtual muscle, in addition to one software package available commercially, SIMM (Musculographics Inc., USA). MMS converts anatomically accurate musculoskeletal models generated by SIMM into Simulink(R) blocks. It also removes run-time constraints on kinetic simulations in SIMM, and allows the development of complex musculoskeletal models without writing a line of code. Virtual muscle builds realistic Simulink models of muscles responding to either natural recruitment or FES. Models of sensorimotor control systems can be developed using various Matlab (Mathworks Inc., USA) toolboxes and integrated easily with these musculoskeletal blocks in the graphical environment of Simulink.

Predicting the voluntary arm forces in FES-assisted standing up using neural networks.

For individuals with paraplegia, standing up requires activation of paralyzed leg muscles by an artificial functional electrical stimulation (FES) controller and voluntary control of arm forces by the individual. Any knowledge of such voluntary control, particularly its prediction, could be used to design more effective FES controllers. Therefore, artificial neural network models were developed to predict voluntary arm forces from measured angular positions of the ankle, knee, and hip joints during FES-assisted standing up in paraplegia. The training data were collected from eight paraplegic subjects in repeated standing-up trials, and divided into two categories for training and validation. The predictions of the models closely followed both the training and validation data, showing good accuracy and generalization. The comparison of the models showed that, although there are striking similarities among the voluntary controls adopted by different subjects, each subject develops his/her own 'personal strategy' to control the arm forces, which is consistent from trial to trial. The level of consistency was dependent on the experience in using FES, injury level, body weight, and other subject-specific parameters.

Optimal control of FES-assisted standing up in paraplegia using genetic algorithms.

A practical system for Functional Electrical Stimulation (FES) assisted standing up in paraplegia should involve only a minimum of manual set up and tuning. An improved tuning method, using a genetic algorithm (GA) is proposed and demonstrated using computer simulation. Specifically, the GA adjusts the parameters of fuzzy logic (FL) and gain-scheduling proportional integral derivative (GS-PID) controllers that electrically stimulate the hip and knee musculature during the sit-stand maneuver. These new GA designed controllers were found to be effective in coordinating volitional and FES control according to formulated criteria. The latter was based on the deviations from a desired trajectory of the knee and hip joints and the magnitude of the voluntary upper body forces. The magnitude of the average arm forces were slightly higher when compared with the open-loop maximal stimulation of the hip and knee musculature; however, the terminal knee velocities were significantly reduced to less than 10 degrees /s. For practical implementation, the number of trials required to optimize the FL and GS-PID controllers can be reduced by a proposed pre-training procedure using a computer model scaled to the individual. The GA designed controllers remain near optimal provided the model-subject mismatch is small.

Computer simulation of FES standing up in paraplegia: a self-adaptive fuzzy controller with reinforcement learning.

Using computer simulation, the theoretical feasibility of functional electrical stimulation (FES) assisted standing up is demonstrated using a closed-loop self-adaptive fuzzy logic controller based on reinforcement machine learning (FLC-RL). The control goal was to minimize upper limb forces and the terminal velocity of the knee joint. The reinforcement learning (RL) technique was extended to multicontroller problems in continuous state and action spaces. The validated algorithms were used to synthesize FES controllers for the knee and hip joints in simulated paraplegic standing up. The FLC-RL controller was able to achieve the maneuver with only 22% of the upper limb force required to stand-up without FES and to simultaneously reduce the terminal velocity of the knee joint close to zero. The FLC-RL controller demonstrated, as expected, the closed loop fuzzy logic control and on-line self-adaptation capability of the RL was able to accommodate for simulated disturbances due to voluntary arm forces, FES induced muscle fatigue and anthropometric differences between individuals. A method of incorporating a priori heuristic rule based knowledge is described that could reduce the number of the learning trials required to establish a usable control strategy. We also discuss how such heuristics may also be incorporated into the initial FLC-RL controller to ensure safe operation from the onset.

Control of FES in paraplegia: modeling voluntary arm forces.

Skilled behavior is difficult or impossible to articulate explicitly by the performers. Likewise biomechanical models of skilled motor actions are often limited by the lack of knowledge of the underlying mechanisms. A 'behavioral cloning' technique is described, based on a trained artificial neural network (ANN), that precisely mimics an individual's learned skill. In this paper the motor skill considered is that of paraplegics using their upper limbs whilst standing-up with FES. In a group of eight paraplegics with complete spinal injuries, it was possible to develop clones that followed closely the observed behavior of the subjects. Each subject used a unique and consistent voluntary control strategy. Subjects with more experience in using FES were more consistent in the use of their arms from trial to trial. Comparison of the clones revealed features suggestive of some common underlying voluntary control strategies.