Magnetic tracking algorithms can be used to determine the position and orientation of magnetic markers or devices. These techniques are particularly interesting for biomedical applications such as teleoperated surgical robots or the control of upper limb prostheses. The performance of different algorithms used for magnetic tracking was compared in the past. However, in most cases, those algorithms were required to track a single magnet.Here we investigated the performance of three localization algorithms in tracking up to 9 magnets: two optimization-based (Levenberg-Marquardt algorithm, LMA, and Trust Region Reflective algorithm, TRRA) and one recursion-based (Unscented Kalman Filter, UKF). The tracking accuracy of the algorithms and their computation time were investigated through simulations.The accuracy of the three algorithms, when tracking up to six magnets, was similar, leading to estimation errors varying from 0.06 ± 0.02 mm to 2.26 ± 0.07 mm within a 100 mm × 54 mm × 100 mm workspace, at the highest sampling frequency. In all cases, computation times under 300 ms for the UKF and 45 ms for the LMA/TRRA were obtained. The TRRA showed the best tracking performance overall.These outcomes are of interest for a wide range of robotics applications that require remote tracking.

Tactile feedback is pivotal for grasping and manipulation in humans. Providing functionally effective sensory feedback to prostheses users is an open challenge. Past paradigms were mostly based on vibro- or electrotactile stimulations. However, the tactile sensitivity on the targeted body parts (usually the forearm) is greatly less than that of the hand/fingertips, restricting the amount of information that can be provided through this channel. Visual feedback is the most investigated technique in motor learning studies, where it showed positive effects in learning both simple and complex tasks; however, it was not exploited in prosthetics due to technological limitations. Here, we investigated if visual information provided in the form of augmented reality (AR) feedback can be integrated by able-bodied participants in their sensorimotor control of a pick-and-lift task while controlling a robotic hand. For this purpose, we provided visual continuous feedback related to grip force and hand closure to the participants. Each variable was mapped to the length of one of the two ellipse axes visualized on the screen of wearable single-eye display AR glasses. We observed changes in behavior when subtle (i.e., not announced to the participants) manipulation of the AR feedback was introduced, which indicated that the participants integrated the artificial feedback within the sensorimotor control of the task. These results demonstrate that it is possible to deliver effective information through AR feedback in a compact and wearable fashion. This feedback modality may be exploited for delivering sensory feedback to amputees in a clinical scenario.

A 2 × 2 array of four microelectromechanical system (MEMS) tactile microsensors is integrated with readout electronics in the distal phalanx of an anthropomorphic robotic finger. A total of 16 sensing elements are available in a 22.3-mm area (i.e., 72 units/cm ) of the artificial finger, thus achieving a density comparable with human Merkel mechanoreceptors. The MEMS array is covered by a polymeric packaging with biomimetic fingerprints enhancing the sensitivity in roughness encoding. This paper shows the ability of the sensor array to encode roughness for discrimination of surfaces, without requiring dedicated proprioceptive sensors for end-effector velocity. Three fine surfaces with 400-, 440-, and 480- μm spatial periods are quantitatively evaluated. Core experiments consisted in active-touch exploration of surfaces by the finger executing a stereotyped human-like movement. A time-frequency analysis on pairs of tactile array outputs shows a clustering of the fundamental frequency, thus yielding 97.6% worst-case discrimination accuracy with a k -nearest-neighbor (k-NN) classifier. Hence, surfaces differing down to 40 μm are identified in active-touch by both hardware and processing methods based on exteroceptive tactile information. Finally, active-touch results with five textiles (which differ in texture or orientation) are shown as a preliminary qualitative assessment of discrimination in a more realistic tactile-stimulation scenario.

This paper presents the mechatronic design of an anthropomorphic 16 degree of freedom, 4 degree of mobility prosthetic hand for use by transradial amputees. Underactuated fingers and differential mechanisms have been used in order to fit all the functional components (actuators, sensors and electronics) in the size of a human hand. Moreover innovative non-back-drivable mechanisms have been designed in order to overcome some of the actual drawbacks of the mechanisms commonly used in prosthetic hands. The new hand is provided with a comprehensive sensory system embedded in the structure of the fingers composed of 40 proprioceptive and exteroceptive sensors. The large employment of sensors, combined with an actuation architecture that allows for grasping and gestures, will permit this hand to be used in clinical experimentation in the near future.

An anthropomorphic underactuated prosthetic hand, endowed with position and force sensors and controlled by means of myoelectric commands, is used to perform experiments of hierarchical shared control. Three different hierarchical control strategies combined with a vibrotactile feedback system have been developed and tested by able-bodied subjects through grasping tasks used in activities of daily living (ADLs). The first goal is to find a good tradeoff between good grasping capabilities and low attention required by the user to complete grasping tasks, without addressing advanced algorithm for electromyographic processing. The second goal is to understand whether a vibrotactile feedback system is subjectively or objectively useful and how it changes users' performance. Experiments showed that users were able to successfully operate the device in the three control strategies, and that the grasp success increased with more interactive control. Practice has proven that when too much effort is required, subjects do not do their best, preferring, instead, a less-interactive control strategy. Moreover, the experiments showed that when grasping tasks are performed under visual control, the enhanced proprioception offered by a vibrotactile system is practically not exploited. Nevertheless, in subjective opinion, feedback seems to be quite important.

The paper presents a controller for a multi-fingered underactuated prosthetic hand based on tendon transmission. The control architecture is implemented in two subsequent and different phases. The first one is the pre-shaping of the hand, based on the grasp type is implemented by stand-alone motion controllers using PID position control algorithms. The desired force closure value is calculated according to the pre-shape measured tendons tension and to the grasping force. In the second phase, the involved fingers rapidly close around the object to perform a balanced distribution of the forces within the hand, as it happens in the human hand. The controller is thus able to perform successfully many stable grasps. An overview of the hardware architecture and the results in terms of grasping capabilities based on Cutkosky grasp taxonomy are also presented