How does the CNS control arm reaching movements? Introducing a hierarchical nonlinear predictive control organization based on the idea of muscle synergies

Identifying the Effects of Age and Speed on Whole-Body Gait Symmetry by Using a Single Wearable Sensor

Studies on gait symmetry in healthy population have mainly been focused on small range of age categories, neglecting Teenagers (13–18 years old) and Middle-Aged persons (51–60 years old). Moreover, age-related effects on gait symmetry were found only when the symmetry evaluation was based on whole-body acceleration than on spatiotemporal parameters of the gait cycle. Here, we provide a more comprehensive analysis of this issue, using a Symmetry Index (SI) based on whole-body acceleration recorded on individuals aged 6 to 84 years old. Participants wore a single inertial sensor placed on the lower back and walked for 10 m at comfortable, slow and fast speeds. The SI was computed using the coefficient of correlation of whole-body acceleration measured at right and left gait cycles. Young Adults (19–35 years old) and Adults (36–50 years old) showed stable SI over the three speed conditions, while Children (6–12 years old), Teenagers (13–18 years old), Middle-Aged persons and Elderly (61–70 and 71–84 years old) exhibited lower SI values when walking at fast speed. Overall, this study confirms that whole-body gait symmetry is lower in Children and in Elderly persons over 60 years of age, showing, for the first time, that asymmetries appear also during teenage period and in Middle-Aged persons (51–60 years old).

Model-Based Mid-Level Regulation for Assist-As-Needed Hierarchical Control of Wearable Robots: A Computational Study of Human-Robot Adaptation

The closed-loop human–robot system requires developing an effective robotic controller that considers models of both the human and the robot, as well as human adaptation to the robot. This paper develops a mid-level controller providing assist-as-needed (AAN) policies in a hierarchical control setting using two novel methods: model-based and fuzzy logic rule. The goal of AAN is to provide the required extra torque because of the robot’s dynamics and external load compared to the human limb free movement. The human–robot adaptation is simulated using a nonlinear model predictive controller (NMPC) as the human central nervous system (CNS) for three conditions of initial (the initial session of wearing the robot, without any previous experience), short-term (the entire first session, e.g., 45 min), and long-term experiences. The results showed that the two methods (model-based and fuzzy logic) outperform the traditional proportional method in providing AAN by considering distinctive human and robot models. Additionally, the CNS actuator model has difficulty in the initial experience and activates both antagonist and agonist muscles to reduce movement oscillations. In the long-term experience, the simulation shows no oscillation when the CNS NMPC learns the robot model and modifies its weights to simulate realistic human behavior. We found that the desired strength of the robot should be increased gradually to ignore unexpected human–robot interactions (e.g., robot vibration, human spasticity). The proposed mid-level controllers can be used for wearable assistive devices, exoskeletons, and rehabilitation robots.

Costs of position, velocity, and force requirements in optimal control induce triphasic muscle activation during reaching movement

The nervous system activates a pair of agonist and antagonist muscles to determine the muscle activation pattern for a desired movement. Although there is a problem with redundancy, it is solved immediately, and movements are generated with characteristic muscle activation patterns in which antagonistic muscle pairs show alternate bursts with a triphasic shape. To investigate the requirements for deriving this pattern, this study simulated arm movement numerically by adopting a musculoskeletal arm model and an optimal control. The simulation reproduced the triphasic electromyogram (EMG) pattern observed in a reaching movement using a cost function that considered three terms: end-point position, velocity, and force required; the function minimised neural input. The first, second, and third bursts of muscle activity were generated by the cost terms of position, velocity, and force, respectively. Thus, we concluded that the costs of position, velocity, and force requirements in optimal control can induce triphasic EMG patterns. Therefore, we suggest that the nervous system may control the body by using an optimal control mechanism that adopts the costs of position, velocity, and force required; these costs serve to initiate, decelerate, and stabilise movement, respectively.