Sensory information is critical to correct performance errors online during the execution of complex tasks and can be complemented by augmented feedback (FB). Here, 2 groups of participants acquired a new bimanual coordination pattern under different augmented FB conditions: 1) visual input reflecting coordination between the 2 hands and 2) auditory pacing integrating the timing of both hands into a single temporal structure. Behavioral findings revealed that the visual group became dependent on this augmented FB for performance, whereas the auditory group performed equally well with or without augmented FB by the end of practice. Functional magnetic resonance imaging (fMRI) results corroborated these behavioral findings: the visual group showed neural activity increases in sensory-specific areas during practice, supporting increased reliance on augmented FB. Conversely, the auditory group showed a neural activity decrease, specifically in areas associated with cognitive/sensory monitoring of motor task performance, supporting the development of a control mode that was less reliant on augmented FB sources. Finally, some remnants of brain activity in sensory-specific areas in the absence of augmented FB were found for the visual group only, illustrating ongoing reliance on these areas. These findings provide the first neural account for the “guidance hypothesis of information FB,” extensively supported by behavioral research.

We propose a novel method for movement assistance that is based on adaptive oscillators, i.e., mathematical tools that are capable of extracting the high-level features (amplitude, frequency, and offset) of a periodic signal. Such an oscillator acts like a filter on these features, but keeps its output in phase with respect to the input signal. Using a simple inverse model, we predicted the torque produced by human participants during rhythmic flexion extension of the elbow. Feeding back a fraction of this estimated torque to the participant through an elbow exoskeleton, we were able to prove the assistance efficiency through a marked decrease of the biceps and triceps electromyography. Importantly, since the oscillator adapted to the movement imposed by the user, the method flexibly allowed us to change the movement pattern and was still efficient during the nonstationary epochs. This method holds promise for the development of new robot-assisted rehabilitation protocols because it does not require prespecifying a reference trajectory and does not require complex signal sensing or single-user calibration: the only signal that is measured is the position of the augmented joint. In this paper, we further demonstrate that this assistance was very intuitive for the participants who adapted almost instantaneously.

A sensory apparatus to monitor pressure distribution on the physical human-robot interface of lower-limb exoskeletons is presented. We propose a distributed measure of the interaction pressure over the whole contact area between the user and the machine as an alternative measurement method of human-robot interaction. To obtain this measure, an array of newly-developed soft silicone pressure sensors is inserted between the limb and the mechanical interface that connects the robot to the user, in direct contact with the wearer’s skin. Compared to state-of-the-art measures, the advantage of this approach is that it allows for a distributed measure of the interaction pressure, which could be useful for the assessment of safety and comfort of human-robot interaction. This paper presents the new sensor and its characterization, and the development of an interaction measurement apparatus, which is applied to a lower-limb rehabilitation robot. The system is calibrated, and an example its use during a prototypical gait training task is presented.