A computational model for optimal muscle activity considering muscle viscoelasticity in wrist movements

Computational reproductions of external force field adaption without assuming desired trajectories

Optimal feedback control is an established framework that is used to characterize human movement. However, it is not fully understood how the brain computes optimal gains through interactions with the environment. In the past study, we proposed a model of motor learning that identifies a set of feedback and feedforward controllers and a state predictor of the arm musculoskeletal system to control free reaching movements. In this study, we applied the model to force field adaptation tasks where normal reaching movements are disturbed by an external force imposed on the hand. Without a priori knowledge about the arm and environment, the model was able to adapt to the force field by generating counteracting forces to overcome it in a manner similar to what is reported in the behavioral literature. The kinematics of the movements generated by our model share characteristic features of human movements observed before and after force field adaptation. In addition, we demonstrate that the structure and learning algorithm introduced in our model induced a shift in the end-point's equilibrium position and a static force modulation, accompanied by a fast and a slow learning process. Importantly, our model does not require desired trajectories, yields movements without specifying movement duration, and predicts force generation patterns by exploring the environment. Our model demonstrates a possible mechanism through which the central nervous system may control and adapt a point-to-point reaching movement without specifying a desired trajectory by continuously updating the body's musculoskeletal model.

Muscles Reduce Neuronal Information Load: Quantification of Control Effort in Biological vs. Robotic Pointing and Walking

It is hypothesized that the nonlinear muscle characteristic of biomechanical systems simplify control in the sense that the information the nervous system has to process is reduced through off-loading computation to the morphological structure. It has been proposed to quantify the required information with an information-entropy based approach, which evaluates the minimally required information to control a desired movement, i.e., control effort. The key idea is to compare the same movement but generated by different actuators, e.g., muscles and torque actuators, and determine which of the two morphologies requires less information to generate the same movement. In this work, for the first time, we apply this measure to numerical simulations of more complex human movements: point-to-point arm movements and walking. These models consider up to 24 control signals rendering the brute force approach of the previous implementation to search for the minimally required information futile. We therefore propose a novel algorithm based on the pattern search approach specifically designed to solve this constraint optimization problem. We apply this algorithm to numerical models, which include Hill-type muscle-tendon actuation as well as ideal torque sources acting directly on the joints. The controller for the point-to-point movements was obtained by deep reinforcement learning for muscle and torque actuators. Walking was controlled by proprioceptive neural feedback in the muscular system and a PD controller in the torque model. Results show that the neuromuscular models consistently require less information to successfully generate the movement than the torque-driven counterparts. These findings were consistent for all investigated controllers in our experiments, implying that this is a system property, not a controller property. The proposed algorithm to determine the control effort is more efficient than other standard optimization techniques and provided as open source.

Analysis of Visuo Motor Control between Dominant Hand and Non-Dominant Hand for Effective Human-Robot Collaboration

The human-in-the-loop technology requires studies on sensory-motor characteristics of each hand for an effective human-robot collaboration. This study aims to investigate the differences in visuomotor control between the dominant (DH) and non-dominant hands in tracking a target in the three-dimensional space. We compared the circular tracking performances of the hands on the frontal plane of the virtual reality space in terms of radial position error (ΔR), phase error (Δθ), acceleration error (Δa), and dimensionless squared jerk (DSJ) at four different speeds for 30 subjects. ΔR and Δθ significantly differed at relatively high speeds (ΔR: 0.5 Hz; Δθ: 0.5, 0.75 Hz), with maximum values of ≤1% compared to the target trajectory radius. DSJ significantly differed only at low speeds (0.125, 0.25 Hz), whereas Δa significantly differed at all speeds. In summary, the feedback-control mechanism of the DH has a wider range of speed control capability and is efficient according to an energy saving model. The central nervous system (CNS) uses different models for the two hands, which react dissimilarly. Despite the precise control of the DH, both hands exhibited dependences on limb kinematic properties at high speeds (0.75 Hz). Thus, the CNS uses a different strategy according to the model for optimal results.

Morphological Computation Increases From Lower- to Higher-Level of Biological Motor Control Hierarchy

Voluntary movements, like point-to-point or oscillatory human arm movements, are generated by the interaction of several structures. High-level neuronal circuits in the brain are responsible for planning and initiating a movement. Spinal circuits incorporate proprioceptive feedback to compensate for deviations from the desired movement. Muscle biochemistry and contraction dynamics generate movement driving forces and provide an immediate physical response to external forces, like a low-level decentralized controller. A simple central neuronal command like "initiate a movement" then recruits all these biological structures and processes leading to complex behavior, e.g., generate a stable oscillatory movement in resonance with an external spring-mass system. It has been discussed that the spinal feedback circuits, the biochemical processes, and the biomechanical muscle dynamics contribute to the movement generation, and, thus, take over some parts of the movement generation and stabilization which would otherwise have to be performed by the high-level controller. This contribution is termed morphological computation and can be quantified with information entropy-based approaches. However, it is unknown whether morphological computation actually differs between these different hierarchical levels of the control system. To investigate this, we simulated point-to-point and oscillatory human arm movements with a neuro-musculoskeletal model. We then quantify morphological computation on the different hierarchy levels. The results show that morphological computation is highest for the most central (highest) level of the modeled control hierarchy, where the movement initiation and timing are encoded. Furthermore, they show that the lowest neuronal control layer, the muscle stimulation input, exploits the morphological computation of the biochemical and biophysical muscle characteristics to generate smooth dynamic movements. This study provides evidence that the system's design in the mechanical as well as in the neurological structure can take over important contributions to control, which would otherwise need to be performed by the higher control levels.

Analysis of Control Characteristics between Dominant and Non-Dominant Hands by Transient Responses of Circular Tracking Movements in 3D Virtual Reality Space

Human movement is a controlled result of the sensory-motor system, and the motor control mechanism has been studied through diverse movements. The present study examined control characteristics of dominant and non-dominant hands by analyzing the transient responses of circular tracking movements in 3D virtual reality space. A visual target rotated in a circular trajectory at four different speeds, and 29 participants tracked the target with their hands. The position of each subject’s hand was measured, and the following three parameters were investigated: normalized initial peak velocity (IPV2), initial peak time (IPT2), and time delay (TD2). The IPV2 of both hands decreased as target speed increased. The results of IPT2 revealed that the dominant hand reached its peak velocity 0.0423 s earlier than the non-dominant hand, regardless of target speed. The TD2 of the hands diminished by 0.0218 s on average as target speed increased, but the dominant hand statistically revealed a 0.0417-s shorter TD2 than the non-dominant hand. Velocity-control performances from the IPV2 and IPT2 suggested that an identical internal model controls movement in both hands, whereas the dominant hand is likely more experienced than the non-dominant hand in reacting to neural commands, resulting in better reactivity in the movement task.

Muscle Synergy and Musculoskeletal Model-Based Continuous Multi-Dimensional Estimation of Wrist and Hand Motions

In this study, seven-channel electromyography signal-based two-dimensional wrist joint movement estimation with and without handgrip motions was carried out. Electromyography signals were analyzed using the synergy-based linear regression model and musculoskeletal model; they were subsequently compared with respect to single and combined wrist joint movements and handgrip. Using each one of wrist motion and grip trial as a training set, the synergy-based linear regression model exhibited a statistically significant performance with 0.7891 ± 0.0844 Pearson correlation coefficient (r) value in two-dimensional wrist motion estimation compared with 0.7608 ±  0.1037 r value of the musculoskeletal model. Estimates on the grip force produced 0.8463 ± 0.0503 r value with 0.2559 ± 0.1397 normalized root-mean-square error of the wrist motion range. This continuous wrist and handgrip estimation can be considered when electromyography-based multi-dimensional input signals in the prosthesis, virtual interface, and rehabilitation are needed.

Motor primitives in space and time via targeted gain modulation in cortical networks

Motor cortex (M1) exhibits a rich repertoire of neuronal activities to support the generation of complex movements. Although recent neuronal-network models capture many qualitative aspects of M1 dynamics, they can generate only a few distinct movements. Additionally, it is unclear how M1 efficiently controls movements over a wide range of shapes and speeds. We demonstrate that modulation of neuronal input–output gains in recurrent neuronal-network models with fixed architecture can dramatically reorganize neuronal activity and thus downstream muscle outputs. Consistent with the observation of diffuse neuromodulatory projections to M1, a relatively small number of modulatory control units provide sufficient flexibility to adjust high-dimensional network activity using a simple reward-based learning rule. Furthermore, it is possible to assemble novel movements from previously learned primitives, and one can separately change movement speed while preserving movement shape. Our results provide a new perspective on the role of modulatory systems in controlling recurrent cortical activity.

Quantitative evaluation of age-related decline in control of preprogramed movement

In this paper, we examined the age-related changes in control of preprogramed movement, with emphasis on its accuracy. Forty-nine healthy subjects participated in this study, and were divided into three groups depending on their ages: the young group (20–39 years) (n = 16), the middle-age group (40–59 years) (n = 16), and the elderly group (60–79 years) (n = 17). We asked the subjects to perform step-tracking movements of the wrist joint with a manipulandum, and recorded the movements. We evaluated the accuracy of control of preprogramed movement in the three groups in terms of the primary submovement, which was identified as the first segment of the step-tracking movement based on the bell-shaped velocity profile, and calculated the distance between the end position of the primary submovement and the target (i.e. error). The error in the young group was found to be significantly smaller than that in the middle-age and elderly groups, i.e., the error was larger for the higher age groups. These results suggest that young subjects have better control of preprogramed movement than middle-age or elderly subjects. Finally, we examined the temporal property of the primary submovement and its age-related changes. The duration of the primary submovement tended to be longer for the aged groups, although significance was reached only for the elderly group. In particular, the ratio of the duration of the primary submovement to total movement time tended to be lower for the aged groups, suggesting that the proportion of additional movements that are required to compensate for the incomplete control in the preprogramed movement, which are under feedback control, was higher for the aged groups. Consequently, our results indicate that the distance between the end point of the primary submovement and the target center (i.e. error) in the step-tracking movement is a useful parameter to evaluate the age-related changes in control of preprogramed movement.