The generation of walking patterns is central to bio-inspired robotics and has been attained using methods encompassing diverse numerical as well as analog implementations. Here, we demonstrate the possibility of synthesizing viable gaits using a paradigmatic low-dimensional non-linear entity, namely, the Rössler system, as a dynamical unit. Through a minimalistic network wherein each instance is univocally associated with one leg, it is possible to readily reproduce the canonical gaits as well as generate new ones via changing the coupling scheme and the associated delays. Varying levels of irregularity can be introduced by rendering individual systems or the entire network chaotic. Moreover, through tailored mapping of the state variables to physical angles, adequate leg trajectories can be accessed directly from the coupled systems. The functionality of the resulting generator was confirmed in laboratory experiments by means of an instrumented six-legged ant-like robot. Owing to their simple form, the 18 coupled equations could be rapidly integrated on a bare-metal microcontroller, leading to the demonstration of real-time robot control navigating an arena using a brain–machine interface.

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Muscle synergy analysis via surface electromyography (EMG) is useful to study muscle coordination in motor learning, clinical diagnosis, and neurorehabilitation. However, current methods to extract muscle synergies in the upper limb suffer from two major issues. First, the necessary normalization of EMG signals is performed via maximum voluntary contraction (MVC), which requires maximal isometric force production in each muscle. However, some individuals with motor impairments have difficulties producing maximal effort in the MVC task. In addition, the MVC is known to be highly unreliable, with widely different forces produced in repeated measures. Second, synergy extraction in the upper limb is typically performed with a multidirection reaching task. However, some participants with motor impairments cannot perform this task because it requires precise motor control. In this study, we proposed a new isometric rotating task that does not require precise motor control or large forces. In this task, participants maintain a cursor controlled by the arm end-point force on a target that rotates at a constant angular velocity at a designated force level. To relax constraints on motor control precision, the target is widened and blurred. To obtain a reference EMG value for normalization without requiring maximal effort, we estimated a linear relationship between joint torques and muscle activations. We assessed the reliability of joint torque normalization and synergy extraction in the rotating task in young neurotypical individuals. Compared with normalization with MVC, joint torque normalization allowed reliable EMG normalization at low force levels. In addition, the extraction of synergies was as reliable and more stable than with the multidirection reaching task. The proposed rotating task can, therefore, be used in future motor learning, clinical diagnosis, and neurorehabilitation studies.

Augmented feedback provided by a coach or augmented reality system can facilitate the acquisition of a motor skill. Verbal instructions and visual aids can be effective in providing feedback about the kinematics of the desired movements. However, many skills require mastering not only kinematic, but also complex kinetic patterns, for which feedback is harder to convey. Here, we propose the electromyography (EMG) space similarity feedback, which may indirectly convey kinematic and kinetic feedback by comparing the muscle activations of the learner and an expert in the task. The EMG space similarity feedback is a score that reflects how well a set of muscle synergies extracted from the expert can reconstruct the learner’s EMG when performing the task. We tested the EMG space similarity feedback in a virtual bimanual polishing task that uses a robotic system to simulate the dynamics of a real polishing operation. We measured the expert’s and learner’s EMG from eight muscles in each arm during the real and virtual polishing tasks, respectively. The goal of the virtual task was to smoothen the surface of a virtual object. Therefore, we defined performance in the task as the smoothness of the object at the end of a trial. We separated learners into real feedback and null feedback groups to assess the effects of the EMG space similarity feedback. The real and null feedback groups received veridic and no EMG space similarity feedback, respectively. Subjects participated in five training sessions on different days, and we evaluated their performance on each day. Subjects in both groups were able to increase smoothness throughout the training sessions, with no significant differences between groups. However, subjects in the real feedback group were able to improve in the EMG space similarity score to a significantly greater extent than the null feedback group. Additionally, subjects in the real feedback group produced muscle activations that became increasingly consistent with an important muscle synergy found in the expert. Our results indicate that the EMG space similarity feedback promotes acquiring expert-like muscle activation patterns, suggesting that it may assist in the acquisition of complex motor skills.