Paraplegic standing supported by FES-controlled ankle stiffness

The objective of this paper was to investigate whether a paraplegic subject-is able to maintain balance during standing by means of voluntary and reflex activity of the upper body while being supported by closed loop controlled ankle stiffness using FES. The knees and hips of the subject were held in extended positions by a mechanical apparatus, which restricted movement to the sagittal plane. The subject underwent several training sessions where the appropriate level of stiffness around the ankles was maintained by the mechanical apparatus. This enabled the subject to learn how to use the upper body for. balancing. After the subject gained adequate skills closed-loop FES was employed to regulate ankle stiffness, replacing the stiffness provided by the apparatus. A method to control antagonist muscle moment was implemented. In subsequent standing sessions, the subject had no difficulties in maintaining balance. When the FES, support was withheld, the ability to balance was lost

Adaptive intermittent control: A computational model explaining motor intermittency observed in human behavior

It is a fundamental question how our brain performs a given motor task in a real-time fashion with the slow sensorimotor system. Computational theory proposed an influential idea of feed-forward control, but it has mainly treated the case that the movement is ballistic (such as reaching) because the motor commands should be calculated in advance of movement execution. As a possible mechanism for operating feed-forward control in continuous motor tasks (such as target tracking), we propose a control model called "adaptive intermittent control" or "segmented control," that brain adaptively divides the continuous time axis into discrete segments and executes feed-forward control in each segment. The idea of intermittent control has been proposed in the fields of control theory, biological modeling and nonlinear dynamical system. Compared with these previous models, the key of the proposed model is that the system speculatively determines the segmentation based on the future prediction and its uncertainty. The result of computer simulation showed that the proposed model realized faithful visuo-manual tracking with realistic sensorimotor delays and with less computational costs (i.e., with fewer number of segments). Furthermore, it replicated "motor intermittency", that is, intermittent discontinuities commonly observed in human movement trajectories. We discuss that the temporally segmented control is an inevitable strategy for brain which has to achieve a given task with small computational (or cognitive) cost, using a slow control system in an uncertain variable environment, and the motor intermittency is the side-effect of this strategy

Evaluating Morphological Computation in Muscle and DC-motor Driven Models of Human Hopping

In the context of embodied artificial intelligence, morphological computation refers to processes which are conducted by the body (and environment) that otherwise would have to be performed by the brain. Exploiting environmental and morphological properties is an important feature of embodied systems. The main reason is that it allows to significantly reduce the controller complexity. An important aspect of morphological computation is that it cannot be assigned to an embodied system per se, but that it is, as we show, behavior- and state-dependent. In this work, we evaluate two different measures of morphological computation that can be applied in robotic systems and in computer simulations of biological movement. As an example, these measures were evaluated on muscle and DC-motor driven hopping models. We show that a state-dependent analysis of the hopping behaviors provides additional insights that cannot be gained from the averaged measures alone. This work includes algorithms and computer code for the measures.Comment: 10 pages, 4 figures, 1 table, 5 algorithm

Human Like Adaptation of Force and Impedance in Stable and Unstable Tasks

Abstract—This paper presents a novel human-like learning con-troller to interact with unknown environments. Strictly derived from the minimization of instability, motion error, and effort, the controller compensates for the disturbance in the environment in interaction tasks by adapting feedforward force and impedance. In contrast with conventional learning controllers, the new controller can deal with unstable situations that are typical of tool use and gradually acquire a desired stability margin. Simulations show that this controller is a good model of human motor adaptation. Robotic implementations further demonstrate its capabilities to optimally adapt interaction with dynamic environments and humans in joint torque controlled robots and variable impedance actuators, with-out requiring interaction force sensing. Index Terms—Feedforward force, human motor control, impedance, robotic control. I

Regulating stepping during fixed-speed and self-paced treadmill walking

textBackground: Treadmill walking should closely simulate overground walking for research validation and optimal skill transfer. Traditional fixed-speed treadmill (FS) walking may not simulate natural walking because of the fixed belt speed and lack of visual cues. Self-paced (SP) treadmill walking, especially feedback controlled SP treadmill walking, enables close-to-real-time belt speed changes with users' speed changes. Different sensitivity levels of SP treadmill feedback determine how fast the treadmill respond to user's speed change. Few studies have examined the differences between FS and SP treadmill walking, or the difference between sensitivity levels of SP treadmills, and their methods were questionable because of averaging kinematics and kinetics parameters, and failing to examine directly treadmill and subjects' speed data. This study compared FS with two SP modes with variation of treadmill speed and user's speed as dependent variables. Method: Thirteen young healthy subjects participated. Subjects walked on a motorized split-belt treadmill under FS, high sensitivity SP (SP-H) and low sensitivity SP (SP-L) conditions at normal walking speed. Root mean square error (RMSE) for subject's pelvis global speed (Vpg), pelvis speed with respect to treadmill speed (Vpt), and treadmill speed (Vtg) data were collected for all trials. Results: Significant condition effects were found between FS and the two SP modes in all RMSE values (p < 0.001). The two sensitivity levels of SP had similar speed patterns. Large subject × condition interaction effects were found for all variables (p < 0.001). Only small subject effects were found. Conclusions: The results of the study reveal different walking patterns between FS and SP. However, the two sensitivity levels failed to differ much. More habituation time may be needed for subjects to learn to optimally respond to the SP algorithm. Future work should include training subjects for more natural responses, applying a feed-forward algorithm, and testing the effect of optic flow on FS and SP speed variation.Kinesiology and Health Educatio

New control strategies for neuroprosthetic systems

The availability of techniques to artificially excite paralyzed muscles opens enormous potential for restoring both upper and lower extremity movements with\ud neuroprostheses. Neuroprostheses must stimulate muscle, and control and regulate the artificial movements produced. Control methods to accomplish these tasks include feedforward (open-loop), feedback, and adaptive control. Feedforward control requires a great deal of information about the biomechanical behavior of the limb. For the upper extremity, an artificial motor program was developed to provide such movement program input to a neuroprosthesis. In lower extremity control, one group achieved their best results by attempting to meet naturally perceived gait objectives rather than to follow an exact joint angle trajectory. Adaptive feedforward control, as implemented in the cycleto-cycle controller, gave good compensation for the gradual decrease in performance observed with open-loop control. A neural network controller was able to control its system to customize stimulation parameters in order to generate a desired output trajectory in a given individual and to maintain tracking performance in the presence of muscle fatigue. The authors believe that practical FNS control systems must\ud exhibit many of these features of neurophysiological systems