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In many physical human–robot interaction scenarios, for successful completion of the tasks, robots should be able to recognize the human partner's intention. One of such scenarios that is studied in this letter is the collaborative task of carrying an object by a human–humanoid pair in which the humanoid should be able to interpret specific human partner's intentions (e.g., start/stop-walking, accelerate, etc.) only through haptic feedback. To address this problem, we first performed human-human experiments and obtained a multiclass classifier (with more than 90% of accuracy) for human intention detection using, as features, arm position relative to the shoulder and interaction forces. The results of the multiclass classification, without any modifications, have been used to develop an interlimb coordinator that was integrated in a modular control architecture into human-robot experiments. The interlimb coordinator receives the sensory data of the upper-body and sends appropriate commands (including start/stop-walking, accelerate, and decelerate commands) to the lower body controller, which is responsible for achieving a stable walking gait. This modular control approach is successfully tested in the human–humanoid experiments with the COMAN robot.

Gait recovery after neurological disorders requires remastering the interplay between body mechanics and gravitational forces. Despite the importance of gravity-dependent gait interactions and active participation for promoting this learning, these essential components of gait rehabilitation have received comparatively little attention. To address these issues, we developed an adaptive algorithm that personalizes multidirectional forces applied to the trunk based on patient-specific motor deficits. Implementation of this algorithm in a robotic interface reestablished gait dynamics during highly participative locomotion within a large and safe environment. This multidirectional gravity-assist enabled natural walking in nonambulatory individuals with spinal cord injury or stroke and enhanced skilled locomotor control in the less-impaired subjects. A 1-hour training session with multidirectional gravity-assist improved locomotor performance tested without robotic assistance immediately after training, whereas walking the same distance on a treadmill did not ameliorate gait. These results highlight the importance of precise trunk support to deliver gait rehabilitation protocols and establish a practical framework to apply these concepts in clinical routine.

Post-contrastographic Dynamic Contrast Enhancement (DCE) is a consolidated MRI technique to perform non-invasive analysis via Imaging. The dynamics perfusion of specific drugs, named Contrast Agents (CA), allows to highlight anomalies in tissues, since the vascular substrate is differently developed (hypervascularized) for diseases compared with healthy parenchyma. The goodness of the analysis procedure influences the inferred diagnosis: the more objective the analysis, the more accurate the prognosis and the therapies follow-up. Deformations and displacements affect organs and soft tissues and their correction is challenging for DCE analysis; the complexity lies also on artefacts of image registration process because of the backscattered image signal intensity varies over time due to the diffusion/perfusion phenomenon induced by the CA injection. In this work we present a combined approach based on the Active Contours framework and on the Optimal Mass Transportation for the automatic depicting and tracking of deformable objects. Active Contours provide the automatical contour's depiction of a deformable object by minimizing an energy cost functional; Optimal Mass Transportation allows to tracking a deformable object by minimizing the (object's) transport cost under the (object) mass conservation principle. We applied these methodologies to post-contrastographic DCE MR images' series and we are going to show the improvement in the goodness of DCE analysis compared with results gathered with other tracking strategies.

This paper presents a teleoperation system for two six-degree-of-freedom ARMin arm rehabilitation skeletons. Two control strategies are presented: unilateral teleoperation, which is realized with proportional-derivative controllers, and bilateral teleoperation, which is realized with compliance control and torque feedback. An empirical procedure for identifying the best control gains of the unilateral teleoperation system is presented, and the stability of the bilateral teleoperation system is demonstrated theoretically. Evaluations with healthy subjects showed that trajectories can be learned through the teleoperation system and that the torque feedback allows one human user to determine whether the other human is participating in the movement, resisting it, or remaining passive. The paper concludes with a discussion of potential applications of the system, including telerehabilitation and group rehabilitation.

This paper investigates the interactive locomotion for physically coupled bipedal agents using a human-human object carrying experiment and a simple mathematical model. The model is based on the Spring Loaded Inverted Pendulum (SLIP) and on the assumption that the coupling can be modeled as a spring-damper. By setting appropriate parameters, the model can achieve stable walking gaits and coordinated foot-fall patterns. Human-human interactive locomotion data are also analyzed in order to evaluate how the model can be useful to investigate humans’ interaction. The kinematic study of how the rigid coupling between two humans can influence their own behaviour is presented for the first time to our knowledge. Moreover a good match between experimental and model data emerges from the comparison between several gait parameters.