Although the merits of electromyography (EMG)-based control of powered assistive systems have been certified, the factors that affect the performance of EMG-based human-robot cooperation, which are very important, have received little attention. This study investigates whether a more physiologically appropriate model could improve the performance of human-robot cooperation control for an ankle power-assist exoskeleton robot. To achieve the goal, an EMG-driven Hill-type neuromusculoskeletal model (HNM) and a linear proportional model (LPM) were developed and calibrated through maximum isometric voluntary dorsiflexion (MIVD). The two control models could estimate the real-time ankle joint torque, and HNM is more accurate and can account for the change of the joint angle and muscle dynamics. Then, eight healthy volunteers were recruited to wear the ankle exoskeleton robot and complete a series of sinusoidal tracking tasks in the vertical plane. With the various levels of assist based on the two calibrated models, the subjects were instructed to track the target displayed on the screen as accurately as possible by performing ankle dorsiflexion and plantarflexion. Two measurements, the root mean square error (RMSE) and root mean square jerk (RMSJ), were derived from the assistant torque and kinematic signals to characterize the movement performances, whereas the amplitudes of the recorded EMG signals from the tibialis anterior (TA) and the gastrocnemius (GAS) were obtained to reflect the muscular efforts. The results demonstrated that the muscular effort and smoothness of tracking movements decreased with an increase in the assistant ratio. Compared with LPM, subjects made lower physical efforts and generated smoother movements when using HNM, which implied that a more physiologically appropriate model could enable more natural and human-like human-robot cooperation and has potential value for improvement of human-exoskeleton interaction in future applications.

To explore the stroke- and aging-induced neurological changes in paretic muscles from an entropy point of view, fuzzy approximate entropy (fApEn) was utilized to represent the complexity of EMG signals in elbow-tracking tasks. In the experiment, 11 patients after stroke and 20 healthy control subjects (10 young and 10 age-matched adults) were recruited and asked to perform elbow sinusoidal trajectory tracking tasks. During the tests, the elbow angle and electromyographic (EMG) signals of the biceps brachii and triceps brachii were recorded simultaneously. The results showed significant differences in fApEn values of both biceps and triceps EMG among four groups at six velocities (p < 0.01), with fApEn values in the following order: affected sides of stroke patients < unaffected sides of stroke patients < age-matched controls < young controls. A possible mechanism underlying the smaller fApEn values in the affected sides in comparison with aged-matched controls and in the aged individuals in comparison with young controls might be the reduction in the number and firing rate of active motor units. This method and index provide evidence of neurological changes after stroke and aging by complexity analysis of the surface EMG signals. Further studies are needed to validate and facilitate the application in clinic.

Electromyography (EMG)-driven musculoskeletal modeling relies on high-quality measurements of muscle electrical activity to estimate muscle forces. However, a critical challenge for practical deployment of this approach is missing EMG data from muscles that contribute substantially to joint moments. This situation may arise due to either the inability to measure deep muscles with surface electrodes or the lack of a sufficient number of EMG channels. Muscle synergy analysis (MSA) is a dimensionality reduction approach that decomposes a large number of muscle excitations into a small number of time-varying synergy excitations along with time-invariant synergy weights that define the contribution of each synergy excitation to all muscle excitations. This study evaluates how well missing muscle excitations can be predicted using synergy excitations extracted from muscles with available EMG data (henceforth called “synergy extrapolation” or SynX). The method was evaluated using a gait data set collected from a stroke survivor walking on an instrumented treadmill at self-selected and fastest-comfortable speeds. The evaluation process started with full calibration of a lower-body EMG-driven model using 16 measured EMG channels (collected using surface and fine wire electrodes) per leg. One fine wire EMG channel (either iliopsoas or adductor longus) was then treated as unmeasured. The synergy weights associated with the unmeasured muscle excitation were predicted by solving a nonlinear optimization problem where the errors between inverse dynamics and EMG-driven joint moments were minimized. The prediction process was performed for different synergy analysis algorithms (principal component analysis and non-negative matrix factorization), EMG normalization methods, and numbers of synergies. SynX performance was most influenced by the choice of synergy analysis algorithm and number of synergies. Principal component analysis with five or six synergies consistently predicted unmeasured muscle excitations the most accurately and with the greatest robustness to EMG normalization method. Furthermore, the associated joint moment matching accuracy was comparable to that produced by initial EMG-driven model calibration using all 16 EMG channels per leg. SynX may facilitate the assessment of human neuromuscular control and biomechanics when important EMG signals are missing.

There are aging- and stroke-induced changes on sensorimotor control in daily activities, but their mechanisms have not been well investigated. This study explored speed-, aging-, and stroke-induced changes on sensorimotor control. Eleven stroke patients (affected sides and unaffected sides) and 20 control subjects (10 young and 10 age-matched individuals) were enrolled to perform elbow tracking tasks using sinusoidal trajectories, which included 6 target speeds (15.7, 31.4, 47.1, 62.8, 78.5, and 94.2 deg/s). The actual elbow angle was recorded and displayed on a screen as visual feedback, and three indicators, the root mean square error (RMSE), normalized integrated jerk (NIJ) and integral of the power spectrum density of normalized speed (IPNS), were used to investigate the strategy of sensorimotor control. Both NIJ and IPNS had significant differences among the four groups (P<0.01), and the values were ranked in the following order: young controls < age-matched controls

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