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.

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Needle steering systems have shown potential advantages in minimally invasive surgery in soft-tissue due to their ability to reach deep-seated targets while avoiding obstacles. In general, the control strategies employed to drive the insertion use simplified kinematic models, providing limited control of the trajectory between an entry site and a deep seated target in cases of unmodelled tissue-needle dynamics. In this work, we present the first Adaptive Path-Following (APF) controller for a bio-inspired multi-part needle, able to steer along threedimensional (3D) paths within a compliant medium by means of the cyclical motion of interlocked segments and without the need for duty-cycle spinning along the insertion axis. The control strategy is outlined in two parts: a high-level controller, which provides driving commands to follow a predefined 3D path smoothly; and a low-level controller, able to counteract unmodelled tissue-needle nonlinearities and kinematic model uncertainties. A simulation that mimics the needle's mechanical behavior during insertion is achieved by using an Experimental Fitting Model (EFM), obtained from previous experimental trials. The Simulation results demonstrate the robustness and adaptability of the proposed control strategy.

Identifying tumor cells infiltrating normal-appearing brain tissue is critical to achieve a total glioma resection. Raman spectroscopy (RS) is an optical technique with potential for real-time glioma detection. Most RS reports are based on formalin-fixed or frozen samples, with only a few studies deployed on fresh untreated tissue. We aimed to probe RS on untreated brain biopsies exploring novel Raman bands useful in distinguishing glioma and normal brain tissue. Sixty-three fresh tissue biopsies were analyzed within few minutes after resection. A total of 3450 spectra were collected, with 1377 labelled as Healthy and 2073 as Tumor. Machine learning methods were used to classify spectra compared to the histo-pathological standard. The algorithms extracted information from 60 different Raman peaks identified as the most representative among 135 peaks screened. We were able to distinguish between tumor and healthy brain tissue with accuracy and precision of 83% and 82%, respectively. We identified 19 new Raman shifts with known biological significance. Raman spectroscopy was effective and accurate in discriminating glioma tissue from healthy brain ex-vivo in fresh samples. This study added new spectroscopic data that can contribute to further develop Raman Spectroscopy as an intraoperative tool for in-vivo glioma detection.