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Here we compared two different supplementary feedback contents concerning the status of a robotic limb in 2-DoFs configuration: one encoding the Cartesian coordinates of the end-effector of the robotic arm (i.e., Task-space feedback) and another and encoding the robot joints angles (i.e., Joint-space feedback). Feedback was delivered to blindfolded participants through vibrotactile stimulation applied on participants’ leg. After a 1.5-hour training with both feedbacks, participants were significantly more accurate with Task compared to Joint-space feedback, as shown by lower position and aiming errors, albeit not faster (i.e., similar onset delay). However, learning index during training was significantly higher in Joint space feedback compared to Task-space feedback. These results suggest that Task-space feedback is probably more intuitive and more suited for activities which require short training sessions, while Joint space feedback showed potential for long-term improvement. We speculate that the latter, despite performing worse in the present work, might be ultimately more suited for applications requiring long training, such as the control of supernumerary robotic limbs for surgical robotics, heavy industrial manufacturing, or more generally, in the context of human movement augmentation.

The successful completion of complex tasks like hanging a picture or laparoscopic surgery requires coordinated motion of more than two limbs. User-controlled supernumerary robotic limbs (SL) have been proposed to bypass the need for coordination with a partner in such tasks. However, neither the capability to control multiple limbs alone relative to collaborative control with partners, nor how that capability varies across different tasks, is well understood. In this work, we present an investigation of tasks requiring three-hands where the foot was used as an additional source of motor commands. We considered: (1) how does simultaneous control of three hands compare to a cooperating dyad; (2) how this relative performance was altered by the existence of constraints emanating from real or virtual physical connections (mechanical constraints) or from cognitive limits (cognitive constraints). It was found that a cooperating dyad outperformed a single user in all scenarios in terms of task score, path efficiency and motion smoothness. However, while the participants were able to reach more targets with increasing mechanical constraints/decreasing number of simultaneous goals, the relative difference in performance between a dyad and a participant performing trimanual activities decreased, suggesting further potential for SLs in this class of scenario.

Tele-operational tasks often suffer from instability issues and limited reliability during unpredictable interactions. We propose a real-time control law reproducing the impedance and kinematic behaviour of a subject's arm (shoulder and elbow) on a remote avatar in a 2-DoF task. The human arm impedance and kinematics are estimated respectively from EMG and M-IMU data and then mapped into the avatar arm through an impedance control. Contrary to literature methods, our portable tele-impedance controller relies only on wearable sensors and enables an easy use in unstructured environments. The good performance (R

Objective: This article presents the development and validation of a new robotic system for Transcranial Magnetic Stimulation (TMS), characterized by a new control approach, and an ad-hoc calibration methodology, specifically devised for the TMS application. Methods: The robotic TMS platform is composed of a 7 dof manipulator, controlled by an impedance control, and a camera-based neuronavigation system. The proposed calibration method was optimized on the workspace useful for the specific TMS application (spherical shell around the subject's head), and tested on three different hand-eye and robot-world calibration algorithms. The platform functionality was tested on six healthy subjects during a real TMS procedure, over the left primary motor cortex. Results: employing our method significantly decreases ( $p ) the calibration error by 34% for the position and 19% for the orientation. The robotic TMS platform achieved greater orientation accuracy than the expert operators, significantly reducing orientation errors by 46% ( $p ). No significant differences were found in the position errors and in the amplitude of the motor evoked potentials (MEPs) between the robot-aided TMS and the expert operators. Conclusion: The proposed calibration represents a valid method to significantly reduce the calibration errors in robot-aided TMS applications. Results showed the efficacy of the proposed platform (including the control algorithm) in administering a real TMS procedure, achieving better coil positioning than expert operators, and similar results in terms of MEPs. Significance: This article spotlights how to improve the performance of a robotic TMS platform, providing a reproducible and low-cost alternative to the few devices commercially available.

In this paper we compare three approaches to solve the hand-eye and robot-world calibration problem, for their application to a Transcranial Magnetic Stimulation (TMS) system. The selected approaches are: i) non-orthogonal approach (QR24); ii) stochastic global optimization (SGO); iii) quaternion-based (QUAT) method. Performance were evaluated in term of translation and rotation errors, and computational time. The experimental setup is composed of a 7 dof Panda robot (by Franka Emika GmbH) and a Polaris Vicra camera (by Northern Digital Inc) combined with the SofTaxic Optic software (by E.M.S. srl). The SGO method resulted to have the best performance, since it provides lowest errors and high stability over different datasets and number of calibration points. The only drawback is its computational time, which is higher than the other two, but this parameter is not relevant for TMS application. Over the different dataset used in our tests, the small workspace (sphere with radius of 0.05m) and a number of calibration points around 150 allow to achieve the best performance with the SGO method, with an average error of $0.83\pm 0.35mm$ for position and $0.22\pm 0.12deg$ for orientation.

The aim of this work is to present a novel wearable mechatronic device (called PDMeter) designed to objectively assess the wrist rigidity in Parkinson's Disease (PD) patients. The system is low-weight, long-term wearable and portable in order to i) perform clinical assessments during Activities of Daily Living (ADLs) in unstructured environments, and ii) to provide several rigidity measurements per day. In this scenario, we defined two different working modalities: i) measurement mode, in which the system measures the wrist rigidity, and ii) backdrivable mode, in which it does not measure, but it has to be transparent for the user during ADLs. In this paper we present the overall mechatronic design of the PDMeter, including the kinematic structure, the actuation system, the sensory system (both force and position) and the control electronics. The overall structure is optimized in terms of dimension and weight: the design of the electronic system allow to integrate in a single compact PCB both the control system and the wireless communication with an external device (laptop or smartphone); the mechanical structure, characterized by one active degree of freedom and five passive ones, is entirely made of aluminium alloy (A16082), and the whole system with the electronics embedded has an overall mass of about 0.46 kg. Future efforts will focus on the implementation and testing of the most suitable algorithms to assess the wrist rigidity, and their validation in clinical trials.