sEMG-MMG State-Space Model for the Continuous Estimation of Multijoint Angle

Continuous joint angle estimation plays an important role in motion intention recognition and rehabilitation training. In this study, a surface electromyography- (sEMG-) mechanomyography (MMG) state-space model is proposed to estimate continuous multijoint movements from sEMG and MMG signals accurately. The model combines forward dynamics with a Hill-based muscle model that estimates joint torque only in a nonfeedback form, making the extended model capable of predicting the multijoint motion directly. The sEMG and MMG features, including the Wilson amplitude and permutation entropy, are then extracted to construct a measurement equation to reduce system error and external disturbances. Using the proposed model, a closed-loop prediction-correction approach, unscented particle filtering, is used to estimate the joint angle from sEMG and MMG signals. Comprehensive experiments are conducted on the human elbow and shoulder joint, and remarkable improvements are demonstrated compared with conventional methods.

Loaded Vertical Jumping: Force–Velocity Relationship, Work, and Power

The aims of the current study were to explore the pattern of the force–velocity (F–V) relationship of leg muscles, evaluate the reliability and concurrent validity of the obtained parameters, and explore the load associated changes in the muscle work and power output. Subjects performed maximum vertical countermovement jumps with a vest ranging 0–40% of their body mass. The ground reaction force and leg joint kinematics and kinetics were recorded. The data revealed a strong and approximately linear F–V relationship (individual correlation coefficients ranged from 0.78–0.93). The relationship slopes, F- and V-intercepts, and the calculated power were moderately to highly reliable (0.67 < ICC < 0.91), while the concurrent validity F- and V-intercepts, and power with respect to the directly measured values, was (on average) moderate. Despite that a load increase was associated with a decrease in both the countermovement depth and absolute power, the absolute work done increased, as well as the relative contribution of the knee work. The obtained findings generally suggest that the loaded vertical jumps could not only be developed into a routine method for testing the capacities of leg muscles, but also reveal the mechanisms of adaptation of multijoint movements to different loading conditions.

Intersegmental dynamics shape joint coordination during catching in typically developing children but not in children with developmental coordination disorder

Factors shaping joint coordination during multijoint movements were studied using a one-handed ball-catching task. Typically developing (TD) boys between 9 and 12 yr of age, at which catching becomes consistently successful, and boys with developmental coordination disorder (DCD) of the same age participated in the study. The arm was initially stretched down. Catching was performed by flexing the shoulder and elbow and extending the wrist in the parasagittal plane. Catching success rate was substantially lower in children with DCD. Amplitudes and directions of joint motions were similar in both groups. Group differences were found in shoulder and elbow coordination patterns. TD children performed the movement predominantly by actively accelerating into flexion, one joint at a time—first the elbow and then the shoulder—and allowing passive interaction torque (IT) to accelerate the other joint into extension. Children with DCD tended to accelerate both joints into flexion simultaneously, suppressing IT. The results suggest that the TD joint coordination was shaped by the tendency to minimize active control of IT despite the complexity of the emergent joint kinematics. The inefficient control of IT in children with DCD points to deficiency of the internal model of intersegmental dynamics. Together, the findings advocate that joint coordination throughout a multijoint movement is a by-product of the control strategy that benefits from movement dynamics by actively accelerating a single joint and using IT for rotation of the other joint. Reduction of control-dependent noise is discussed as a possible advantage of this control strategy.

Identifying Representative Synergy Matrices for Describing Muscular Activation Patterns During Multidirectional Reaching in the Horizontal Plane

Muscle synergies have been proposed as a simplifying principle of generation of movements based on a low-dimensional control by the CNS. This principle may be useful for movement restoration by, e.g., functional electrical stimulation (FES), if a limited set of synergies can describe several functional tasks. This study investigates the possibility of describing a multijoint reaching task of the upper limb by a linear combination of one set of muscle synergies common to multiple directions. Surface electromyographic (EMG) signals were recorded from 12 muscles of the dominant upper limb of eight healthy men during single-joint movements and a multijoint reaching task in 12 directions in the horizontal plane. The movement kinematics was recorded by a motion analysis system. Muscle synergies were extracted with nonnegative matrix factorization of the EMG envelopes. Synergies were computed either from the single-joint movements to describe the two degrees of freedom independently or from the multijoint movements. On average, the multijoint reaching task could be accurately described in all the directions (coefficient of determination >0.85) by a linear combination of either four synergies extracted from the individual degrees of freedom or three synergies extracted from multijoint movements in at least three reaching directions. These results indicate that a large set of multijoint movements can be generated by a synergy matrix of limited dimensionality and common to all directions if the synergies are extracted from a representative number of directions. The linear combination of synergies may thus be used in strategies for restoring functions, such as FES.

Iterative Learning without Reinforcement or Reward for Multijoint Movements: A Revisit of Bernstein's DOF Problem on Dexterity

A robot designed to mimic a human becomes kinematically redundant and its total degrees of freedom becomes larger than the number of physical variables required for describing a given task. Kinematic redundancy may contribute to enhancement of dexterity and versatility but it incurs a problem of ill-posedness of inverse kinematics from the task space to the joint space. This ill-posedness was originally found by Bernstein, who tried to unveil the secret of the central nervous system and how nicely it coordinates a skeletomotor system with many DOFs interacting in complex ways. In the history of robotics research, such ill-posedness has not yet been resolved directly but circumvented by introducing an artificial performance index and determining uniquely an inverse kinematics solution by minimization. This paper tackles such Bernstein's problem and proposes a new method for resolving the ill-posedness in a natural way without invoking any artificial index. First, given a curve on a horizontal plane for a redundant robot arm whose endpoint is imposed to trace the curve, the existence of a unique ideal joint trajectory is proved. Second, such a uniquely determined motion can be acquired eventually as a joint control signal through iterative learning without reinforcement or reward.