Riccardo Secoli

Effect of visual distraction and auditory feedback on patient effort during robot-assisted movement training after stroke

BackgroundPracticing arm and gait movements with robotic assistance after neurologic injury can help patients improve their movement ability, but patients sometimes reduce their effort during training in response to the assistance. Reduced effort has been hypothesized to diminish clinical outcomes of robotic training. To better understand patient slacking, we studied the role of visual distraction and auditory feedback in modulating patient effort during a common robot-assisted tracking task.MethodsFourteen participants with chronic left hemiparesis from stroke, five control participants with chronic right hemiparesis and fourteen non-impaired healthy control participants, tracked a visual target with their arms while receiving adaptive assistance from a robotic arm exoskeleton. We compared four practice conditions: the baseline tracking task alone; tracking while also performing a visual distracter task; tracking with the visual distracter and sound feedback; and tracking with sound feedback. For the distracter task, symbols were randomly displayed in the corners of the computer screen, and the participants were instructed to click a mouse button when a target symbol appeared. The sound feedback consisted of a repeating beep, with the frequency of repetition made to increase with increasing tracking error.ResultsParticipants with stroke halved their effort and doubled their tracking error when performing the visual distracter task with their left hemiparetic arm. With sound feedback, however, these participants increased their effort and decreased their tracking error close to their baseline levels, while also performing the distracter task successfully. These effects were significantly smaller for the participants who used their non-paretic arm and for the participants without stroke.ConclusionsVisual distraction decreased participants effort during a standard robot-assisted movement training task. This effect was greater for the hemiparetic arm, suggesting that the increased demands associated with controlling an affected arm make the motor system more prone to slack when distracted. Providing an alternate sensory channel for feedback, i.e., auditory feedback of tracking error, enabled the participants to simultaneously perform the tracking task and distracter task effectively. Thus, incorporating real-time auditory feedback of performance errors might improve clinical outcomes of robotic therapy systems.

Substituting auditory for visual feedback to adapt to altered dynamic and kinematic environments during reaching

The arm movement control system often relies on visual feedback to drive motor adaptation and to help specify desired trajectories. Here we studied whether kinematic errors that were indicated with auditory feedback could be used to control reaching in a way comparable with when vision was available. We randomized twenty healthy adult subjects to receive either visual or auditory feedback of their movement trajectory error with respect to a line as they performed timed reaching movements while holding a robotic joystick. We delivered auditory feedback using spatialized pink noise, the loudness and location of which reflected kinematic error. After a baseline period, we unexpectedly perturbed the reaching trajectories using a perpendicular viscous force field applied by the joystick. Subjects adapted to the force field as well with auditory feedback as they did with visual feedback and exhibited comparable after effects when the force field was removed. When we changed the reference trajectory to be a trapezoid instead of a line, subjects shifted their trajectories by about the same amount with either auditory or visual feedback of error. These results indicate that arm motor networks can readily incorporate auditory feedback to alter internal models and desired trajectories, a finding with implications for the organization of the arm motor control adaptation system as well as sensory substitution and motor training technologies.

Design and control of two planar cable-driven robots for upper-limb neurorehabilitation

Post-stroke robot-aided neurorehabilitation is an emerging research field, aiming to improve the intensity and the effectiveness of post-stroke rehabilitation protocols by using robotic technology and virtual reality. One classification that has been proposed for therapy robots is between exoskeletons and end-effector based machines. The latter are those devices whose interaction with the patients arm takes place at the end-effector level. This paper presents the design of two novel end-effector based robots for upper-limb rehabilitation, named Sophia-4 and Sophia-3. Although the devices are based on a common concept (the cable-drive actuation over a planar workspace), the latter differs from the former by the number of employed cables (4 and 3, respectively), and, by several design solutions, such as the introduction of a moving pulley-block to enhance workspace and a tilting table to better target the patients shoulder. Both mechanical and control system design are addressed and a comparison of performances is presented.

Improving robotics for neurorehabilitation: Enhancing engagement, performance, and learning with auditory feedback

This paper reports on an ongoing research collaboration between the University of Padua and the University of California Irvine, on the use of continuous auditory-feedback in robot-assisted neurorehabilitation of post-stroke patients. This feedback modality is mostly underexploited in current robotic rehabilitation systems, that usually implement very basic auditory feedback interfaces. The results of this research show that generating a proper sound cue during robot assisted movement training can help patients in improving engagement, performance and learning in the exercise.

Planar Robotic Systems for Upper-Limb Post-Stroke Rehabilitation

Rehabilitation is the only way to promote recovery of lost function in post-stroke hemiplegic subjects, leading to independence and early reintegration into social and domestic life. In particular, upper limb rehabilitation is fundamental to regain ability in Activities of Daily Living (ADLs). Robot-aided rehabilitation is an emerging field seeking to employ leading-edge robotic systems to increase patient recovery in the rehabilitation treatment. Even though the effectiveness of robotic therapy is still being discussed, the use of robotic devices can increase therapists’ efficiency by alleviating the labor-intensive aspects of physical rehabilitation, and can produce a reduction in treatment costs. This paper presents a comparison between different planar robotic devices designed for upper-limb rehabilitation in chronic patients. A planar configuration of the workspace leads to straightforward mechanical and control system design, and allows to define very simple and understandable treatment exercises. Also, the graphical user interface becomes very intuitive for the patient, and a set of Cartesian-based measures of the patient’s performance can be defined easily. In the paper, SCARA (Selective Compliance Assembly Robot Arm) robots such as the MIT-Manus, Cartesian robots and cable-driven robots are considered and compared in terms of inertial properties and force exertion capabilities. Two cable-driven devices, designed at the Robotics Lab of the Department if Innovation In Mechanics and Management, University of Padua, Italy, are presented for the first time. The first robot employs four driven cables to produce a planar force on the end-effector, whereas the second one is based on a three-cable configuration plus a linear actuator to obtain better overall robot performance.© 2008 ASME

Closed-loop 3D motion modeling and control of a steerable needle for soft tissue surgery

Percutaneous intervention has become a topic of interest in recent years, due to the many potential advantages for the patient. To date, several novel needle steering systems have been developed to improve both the accuracy and applicability of this type of surgery, but many of these can still only provide limited control of the trajectory between an entry site and a deep seated target. Our previous work describes the first prototype of a bio-inspired multi-part needle, codenamed STING, which can steer along planar trajectories within a compliant medium by means of a novel programmable bevel, where the steering angle is a function of the offset between interlocked needle segments. This paper presents our first attempt to model a bio-inspired 4-part needle, an extension of the planar steering system with the potential to steer along three-dimensional (3D) trajectories within a compliant medium. This paper introduces a 3D kinematic model and closed-loop controller for the needle, which is inspired by the modeling strategy employed for under-actuated underwater vehicles, followed by simulation results which demonstrate that 3D trajectory tracking can be completed successfully.

Using Sound feedback to counteract visual distractor during robot-assisted movement training

Patient engagement and effort are thought to be important for maximizing the therapeutic benefit of robot-assisted movement exercise after neurologic injury, but little is understood about how the audio-visual feedback presented during training affects engagement and effort. For the study reported here, we hypothesized that visual distraction would decrease engagement during robot-assisted movement training, but that appropriate auditory feedback would counteract this effect. Non-disabled participants (n = 10) participated in a common therapeutic exercise in which they attempted to track a moving target presented on a computer screen as a robotic exoskeleton compliantly assisted their arm movement. Introducing a simple visual distractor significantly increased both tracking error and the interaction forces between the participants and the robot. Introducing auditory feedback of tracking error reduced the effect of the visual distractor, decreasing tracking error and interaction forces toward normative values. If tracking error is taken as a surrogate measure of participant engagement, these results indicate that the presence of even a modest level of visual distraction in the training environment may decrease engagement, and thus present a hazard to the effectiveness of training. However, providing appropriate task feedback through the auditory system can reduce the effect of visual distractors. Therefore, an integrated design of the audio, visual, and haptic environment is important for optimizing robot-assisted movement training.

Fast and Adaptive Fractal Tree-Based Path Planning for Programmable Bevel Tip Steerable Needles

Steerable needles are a promising technology for minimally invasive surgery, as they can provide access to difficult to reach locations while avoiding delicate anatomical regions. However, due to the unpredictable tissue deformation associated with needle insertion and the complexity of many surgical scenarios, a real-time path planning algorithm with high update frequency would be advantageous. Real-time path planning for nonholonomic systems is commonly used in a broad variety of fields, ranging from aerospace to submarine navigation. In this letter, we propose to take advantage of the architecture of graphics processing units (GPUs) to apply fractal theory and thus parallelize real-time path planning computation. This novel approach, termed adaptive fractal trees (AFT), allows for the creation of a database of paths covering the entire domain, which are dense, invariant, procedurally produced, adaptable in size, and present a recursive structure. The generated cache of paths can in turn be analyzed in parallel to determine the most suitable path in a fraction of a second. The ability to cope with nonholonomic constraints, as well as constraints in the space of states of any complexity or number, is intrinsic to the AFT approach, rendering it highly versatile. Three-dimensional (3-D) simulations applied to needle steering in neurosurgery show that our approach can successfully compute paths in real-time, enabling complex brain navigation.

A low-cost, high-field-strength magnetic resonance imaging-compatible actuator.

To perform minimally invasive surgical interventions with the aid of robotic systems within a magnetic resonance imaging scanner offers significant advantages compared to conventional surgery. However, despite the numerous exciting potential applications of this technology, the introduction of magnetic resonance imaging–compatible robotics has been hampered by safety, reliability and cost concerns: the robots should not be attracted by the strong magnetic field of the scanner and should operate reliably in the field without causing distortion to the scan data. Development of non-conventional sensors and/or actuators is thus required to meet these strict operational and safety requirements. These demands commonly result in expensive actuators, which mean that cost effectiveness remains a major challenge for such robotic systems. This work presents a low-cost, high-field-strength magnetic resonance imaging–compatible actuator: a pneumatic stepper motor which is controllable in open loop or closed loop, along with a rotary encoder, both fully manufactured in plastic, which are shown to perform reliably via a set of in vitro trials while generating negligible artifacts when imaged within a standard clinical scanner.

Associating locations from wearable cameras

In this paper, we address a specific use-case of wearable or hand-held camera technology: indoor navigation. We explore the possibility of crowdsourcing navigational data in the form of video sequences that are captured from wearable or hand-held cameras. Without using geometric inference techniques (such as SLAM), we test video data for navigational content, and algorithms for extracting that content. We do not include tracking in this evaluation; our purpose is to explore the hypothesis that visual content, on its own, contains cues that can be mined to infer a person’s location. We test this hypothesis through estimating positional error distributions inferred during one journey with respect to other journeys along the same approximate path. The contributions of this work are threefold. First, we propose alternative methods for video feature extraction that identify candidate matches between query sequences and a database of sequences from journeys made at different times. Secondly, we suggest an evaluation methodology that estimates the error distributions in inferred position with respect to a ground truth. We assess and compare standard approaches from the field of image retrieval, such as SIFT and HOG3D, to establish associations between frames. The final contribution is a publicly available database comprising over 90,000 frames of video-sequences with positional ground-truth. The data was acquired along more than 3 km worth of indoor journeys with a hand-held device (Nexus 4) and a wearable device (Google Glass).