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

<p>Abstract</p> <p>Background</p> <p>Practicing 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.</p> <p>Methods</p> <p>Fourteen 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.</p> <p>Results</p> <p>Participants 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.</p> <p>Conclusions</p> <p>Visual 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.</p

Planar robotic systems for upper-limb . . .

Rehabilitation is the only way to promote recovery of lost function in post

Position-based Dynamics Simulator of Brain Deformations for Path Planning and Intra-Operative Control in Keyhole Neurosurgery

Many tasks in robot-assisted surgery require planning and controlling manipulators' motions that interact with highly deformable objects. This study proposes a realistic, time-bounded simulator based on Position-based Dynamics (PBD) simulation that mocks brain deformations due to catheter insertion for pre-operative path planning and intra-operative guidance in keyhole surgical procedures. It maximizes the probability of success by accounting for uncertainty in deformation models, noisy sensing, and unpredictable actuation. The PBD deformation parameters were initialized on a parallelepiped-shaped simulated phantom to obtain a reasonable starting guess for the brain white matter. They were calibrated by comparing the obtained displacements with deformation data for catheter insertion in a composite hydrogel phantom. Knowing the gray matter brain structures' different behaviors, the parameters were fine-tuned to obtain a generalized human brain model. The brain structures' average displacement was compared with values in the literature. The simulator's numerical model uses a novel approach with respect to the literature, and it has proved to be a close match with real brain deformations through validation using recorded deformation data of in-vivo animal trials with a mean mismatch of 4.73$\pm$2.15%. The stability, accuracy, and real-time performance make this model suitable for creating a dynamic environment for KN path planning, pre-operative path planning, and intra-operative guidance.Comment: 8 pages, 8 figures. This article has been accepted for publication in a future issue of IEEE Robotics and Automation Letters, but has not been fully edited. Content may change prior to final publication. 2377-3766 (c) 2021 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. A. Segato and C. Di Vece equally contribute

CONTROLLO DI ROBOT PER LA RIABILITAZIONE DELL'ARTO SUPERIORE DI PAZIENTI POST-STROKE

Stroke is the third leading cause of death after cardiovascular diseases and cancer, and represents the greatest cause of severe disability and impairment in the industrialized world [1]. Every year in the U.S. and Europe there are 200 to 300 new stroke cases per 100.000, the 30% of whom survive with severe invalidity and marked limitations in daily activities, mainly deriving from impaired motor control and loss of dexterity in the use of the arm [1, 2]. Due to population aging, this trend is going to grow further in the next decades [2]. Motor training after stroke is thus becoming a primary societal goal, based on the increasing evidence that the motor system is plastic following stroke and can be influenced by motor training [3]. Individuals typically receive intensive hands-on therapy for several months after stroke to treat hemi-paresis and improve independence. Encouragingly, an intensity-effect relationship has now been established between the amount of therapy individuals receive and the movement gains achieved [4, 5, 6, 7, 8, 9]. However, the amount of therapy a patient receives involving direct contact with rehabilitation therapists is often limited by cost considerations [10, 11, 12]. Patients may exercise apart from a therapist, however independent movement practice is particularly difficult for individuals who are unable to lift the arm against gravity or have minimal hand movement ability, which may contribute to the reported poor compliance with home exercise programs [13, 14, 15]. It is essential to develop new approaches for delivering effective forms of therapy at reduced cost, so that people can exercise for longer periods and maximize recovery. In response to this need, over the past two decades there has been a surge in the number of research groups and companies that are developing robotic devices for assisting in the movement rehabilitation of persons with disabilities (see reviews [16, 17, 18, 19, 20, 21, 22]). Most of this work has focused on rehabilitation of movement after stroke because survivors of stroke are a large target population, although there is also some work on robotic movement training after spinal cord injury, cerebral palsy, and multiple sclerosis. Developers typically state three main goals for this activity: automating the repetitive and strenuous aspects of physical therapy, delivering rehabilitation therapy in a more repeatable manner, and quantifying outcomes with greater precision. Devices have been developed for assisting in rehabilitation of the arm, hand, and legs. The most commonly explored paradigm is to use a robotic device to physically assist in completing desired motions of the arms, hands, or legs as the patient plays computer games presented on a screen. A variety of assistive control strategies have been designed (see review: [23]), ranging from robots that rigidly move limbs along fixed paths, to robots that assist only if patient performance fails to stay within some spatial or temporal bound, to soft robots that form a model of the patient’s weakness. Two recent reviews on the first Randomized Controlled Trials (RCTs) of upper-limb robot-assisted rehabilitation outlined that clinical results are still far from being fully satisfactory [21, 24]. In fact, even though motor recovery is usually greater in therapy groups than in control groups, only few studies on acute and sub-acute phase rehabilitation showed some positive results at the functional level (i.e., in the activities of daily living), the summary effect size of all the studies being very close to zero. These results suggest that the therapy devices, exercises and protocols developed so far still need to be improved and optimized. Two notable recent efforts in this direction are the “assist-as-needed control proposed by Reinkensemyer for the neu-WREX, a pneumatic exoskeleton for arm rehabilitation, and the performance-based progressive assistance proposed by Krebs for the pioneering arm-training robot MIT-MANUS [25, 26, 27], which assists in arm movement in the horizontal plane. The former is a robot control algorithm that allows the effort of the patient to be modulated while maintaining the kinematics of the patient’s arm within close bounds to a specified desired movement [28, 29]. The latter is a method to adapt robotic assistance to patient performance (H. I. Krebs, unpublished conference presentation). The goal of both algorithms is to encourage patient effort and engagement during the execution of the exercise. Perhaps the most fundamental problem that robotic movement therapy must address to continue to make progress is that there is still a lack of knowledge on how motor learning during neuro-rehabilitation works at a level of detail sufficient to dictate robotic therapy device design [30]. We know that repetition, with active engagement by the participant, promotes re-organization [31, 32]. We also know that kinematic error drives motor adaptation [33, 34, 35]. Some examples of attempts of correlating patient effort or recovery to kinematic error are [34, 36, 37, 38]. In these works, some mathematical models of healthy persons or patients behavior are proposed and compared to experimental results. There is also evidence that a proper task-related auditory feedback may help individuals in learning a motor task [39], even though auditory feedback is still under-employed in robotic rehabilitation systems. But the precise ways that mental engagement, repetition, kinematic error and sensory information in general translate into a pattern of recovery is not well defined for rehabilitation [30]. The work presented in this Dissertation is the fist part of a research whose main goal is to identify the key mechanisms that determine the engagement of the patient during robotic arm movement training after stroke, in order to optimize the design of rehabilitation robotic systems. The key hypothesis behind the research is that patient engagement and effort are related to (and can be modulated by) the sensory information delivered by the robotic system, and that more highly engaged patients will experience increased benefits from robot-assisted training. In order to achieve this primary results, a new planar cable machine for upper limb rehabilitation of chronic stroke patients, that can be enough economic for a care-home use. In this machine, the ” assistive-as-needed” control was designed and improved to obtain a compliant control for engaging the patient during the therapy. The final goal of the research will be to develop a set of mathematical equations that relate certain variables (e.g. sensory feedback measures) to other variables (engagement measures), to model the way a patient interacts with the robotic system. In this way, we aim to understand patient response at a sufficient level to dictate robotic therapy device design. One fundamental point is the definition of the variables employed to quantify patient engagement and sensory inputs in the computational model, and the way they will be measured. In order to investigate this fundamental point, a multi-feedback interface was designed using sound feedback to increase the attention of the patient during a robot-theraphy session. Clinical trials with healthy and stroke patients were performed using the new interface and the modified ”assistive-as-needed” control. The results confirmed the starting hyphotesis: a multi-feedback interface with ”assistive-as-needed” control improve the patient’s performance during a robot-assistive therapy and the sound feedback can increase the attention during the exercise. The future work will concern an improvement of multi-feedback interface and the stroke patient’s computational model of motor control. Finally, to performed a comparison between the clinical trials with Pneu-Wrex and the S.O.P.H.I.A 4 in order to understand new guideline to design a mechanical structure of rehabilitation robot.L'ictus celebrale è la terza causa di morte dopo i decessi cardiovascolari e il cancro, e rappresenta una grave disabilà nell’epoca moderna [1]. Ogni anno in USA ed Europa ci sono tra i 200 e 300 nuovi casi ogni 100.000, in cui il 30% dei quali sopravvive con gravi invalidità e limitazioni sulle attività quotidiane, principalmente dovute ad un deterioramento del controllo motorio e alla perdita quindi, della destrezza nell’utilizzare gli arti [1, 2]. Considerando l’innalzamento dell’età media della popolazione, l’ictus rappresenta un fenomeno in via di crescita nei prossimi anni [2]. L’allenamento motorio post-ictus è diventato un bisogno primario sociale, basato sull’evidente beneficio che provoca sulla plasticità del sistema motorio a seguito di ictus [3]. Tipicamente i soggetti affetti da ictus ricevono delle cure fisioterapiche diversi mesi dopo lo stroke, per riuscire a migliorare le semi-paresi e per recuperare l’indipendenza motoria. Una relazione tra intensità ed effetto dei trattamenti si è instaurata tra la quantità di terapia individuale somministrata e il guadagno ottenuto nella mobilità motoria [4, 5, 6, 7, 8, 9]. E comunque da considerare che l’ammontare totale della terapia ricevuta, coinvolgendo direttamente il contatto diretto del fisioterapeuta, è limitato dai costi [10, 11, 12]. I pazienti tuttavia possono esercitarsi al di fuori delle sessioni fisioterapiche, ma i movimenti individuali sono particolarmente difficili per gli individui che non sono capaci di sollevare il proprio arto o con una minima mobilità alla mano, pertanto il contributo degli esercizi svolti a casa al fine del recupero motorio ha dato scarsi risultati [13, 14, 15]. E' necessario pertanto, sviluppare nuove strategie per la divulgazione delle terapie a basso costo, con l’obiettivo di permettere ai pazienti di esercitarsi per lungo tempo, massimizzando quindi il recupero motorio. Per far fronte a questo bisogno, nelle ultime due decadi si sono visti protagonisti un distinto numero di gruppi di ricerca ed industrie che hanno sviluppato dispositivi robotici per la riabilitazione di persone con disabilità (vedi revisioni [16, 17, 18, 19, 20, 21, 22]). La maggior parte di questo lavoro è incentrata nella riabilitazione dei movimenti a seguito di ictus poiché i sopravvissuti rappresentano una larga parte della popolazione presa in esame, sebbene vi siano altri lavori riguardanti il recupero motorio a seguito di paralisi celebrale infantile, sclerosi multipla e danni alla spina dorsale. Tipicamente sono tre gli obiettivi da raggiungere in questo settore: automatizzare la ripetibilità e l’arduo lavoro fisico della terapia, divulgare la terapia riabilitativa in più modi possibili, quantificare i risultati terapeutici con grande precisione. Dispositivi robotici sono stati sviluppati per assistere la riabilitazione di braccia, mani e gambe. Il paradigma più comune è utilizzare i dispositivi robotici per assistere fisicamente il completamento di movimenti desiderati di braccia, mani o gambe dei pazienti mentre svolgono dei giochi al computer. Diverse strategie di controllo sono state sviluppate (vedi revisione: [23]), e spaziano da robot che spostano rigidamente gli arti lungo un percorso predefinito, a robot che assistono il paziente solo se la performance di quest’ultimo non rientra dentro dei limiti spaziali o temporali, a robot che costruiscono un modello della disabilità del paziente. Due recenti revisioni del primo Randomized Controlled Trials (RCTs) di robot per la riabilitazione degli arti superiori hanno evidenziato che i risultati clinici sono distanti dall’essere soddisfacenti [21, 24]. Infatti, anche se il recupero motorio è maggiore nel gruppo della terapia robotica che in quello tradizionale, solo alcuni studi su pazienti in fase acuta e sub-acuta hanno dimostrato risultati positivi a livello funzionale (es. svolgimento delle attività quotidiane), complessivamente gli effetti complessivi sono tendenti a zero. Ciò suggerisce che le terapie, gli esercizi e i protocolli riabilitativi fin qui sviluppati devono essere ulteriormente perfezionati e ottimizzati. Due recenti sforzi in questa direzioni sono stati fatti: il controllo “assist-asneeded” proposto da Reinkensmayer per il Pneu-wrex, un esoscheletro ad attuazione pneumatica per la riabilitazione degli arti, e il controllo con assistenza progressiva in base alla performance del più famoso dispositivo riabilitativo per gli arti superiori il MIT-MANUS[25, 26, 27], il quale assiste il braccio del paziente nei movimenti svolti in un piano orizzontale. Il primo tipo di controllo permette di modulare lo sforzo del paziente mantenendolo vicino ad un percorso predefinito[28, 29]. Il secondo, è un metodo che adatta l’assistenza del robot alla performance del paziente (H.I. Krebs, unpublished conference presentation). Lo scopo di entrambi gli algoritmi è di incrementare lo sforzo e la partecipazione del paziente durante l’esecuzione degli esercizi. Forse, il problema fondamentale è che la terapia robotica non svolge un efficace progresso in questo senso è dovuto alla mancata conoscenza di come il motor learning funziona durante il lavoro di neuro-riabilitazione ad un livello tale da poter stabilire delle specifiche per la progettazione dei dispositivi robotici per la terapia [30]. Sappiamo che la ripetizione, con la partecipazione attiva del paziente, favorisce la riorganizzazione [31, 32]. Sappiamo che gli errori cinematici stimolano l’adattabilità motoria [33, 34, 35]. Alcuni esempi di correlazione tra sforzo del paziente o recupero dell’errore cinematico sono [34, 36, 37, 38]. In questi lavori, alcuni modelli matematici del comportamento di Soggetti sani o di Pazienti sono stati proposti e/o comparati con risultati sperimentali. Inoltre, ci sono anche dei test relativi all’utilizzo di feeback acustico per imparare ad eseguire dei task motori [39], anche se il sistema acustico è ancora largamente sottoutilizzato nei sistemi di riabilitazione robotica. I precisi processi di coinvolgimento mentale, le ripetizioni, gli errori cinematici e le informazioni sensoriali tradotte generalmente in un metodo di recupero non sono ancora state ben definite nella riabilitazione [30]. Il lavoro presentato in questa Tesi è la prima parte di una ricerca che ha come scopo principale di identificare i meccanismi chiave per determinare un coinvolgimento del paziente durante la terapia robotica assistita post-ictus, al fine di ottimizzare la progettazione dei dispositivi robotici. L’ipotesi chiave che sta dietro la ricerca è che il coinvolgimento del paziente e lo sforzo sono relazionati con le informazioni sensoriali fornite dal sistema robotico, e più il paziente sarà coinvolto più ci saranno degli incrementi nei benefici della terapia robotica assistita. Al fine di raggiungere questi risultati primari, è stata progettata una macchina planare a cavi per la riabilitazione degli arti superiori per pazienti post-ictus, abbastanza economica per l’utilizzo in ambulatorio. In questo dispositivo è stato progettato e perfezionato il controllo di tipo “assist-as-needed” per ottenere un controllore che coinvolga attivamente il paziente durante la terapia. Lo scopo finale di questo progetto sarà sviluppare una serie di equazioni matematiche che relazionino alcune variabili (es.: misurazioni del feedback) ad altre variabili (misura del coinvolgimento del paziente), per modellizzare il metodo comportamentale con cui il paziente interagisce con il robot. In questo modo si riuscirà a capire la risposta del paziente ad un livello sufficiente per dettare delle linee guida nella progettazione dei dispositivi robotici. Un punto fondamentale sarà definire le variabili impiegate per quantificare la partecipazione del paziente e gli ingressi sensoriali nel modello computazionale, e il loro metodo di misurazione. Per investigare su questo punto fondamentale è stata progettata un’interfaccia multi-feedback utilizzando un feedback sonoro per incrementare l’attenzione del paziente durante la terapia robotica assistita. Sono stati svolti dei test clinici con soggetti sani e pazienti post-stroke utilizzando la nuova interfaccia e il controllo “assist-as-needed” modificato. I risultati dei test hanno confermato le ipotesi iniziali: un’interfaccia multifeedback con il controllo “assist-as-needed” migliora le erformance dei pazienti durante la terapia robotica e il feedback sonoro incrementa l’attenzione durante gli esercizi. Uno step successivo del lavoro di Tesi, riguarderà il perfezionamento dell’interfaccia multi-feedback e del modello computazionale di controllo motorio per pazienti post-ictus

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

Background: Practicing 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. Methods: Fourteen 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. Results: Participants 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. Conclusions: Visual 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

Glioma biopsies Classification Using Raman Spectroscopy and Machine Learning Models on Fresh Tissue Samples

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

Raman Spectroscopy and Machine Learning for IDH Genotyping of Unprocessed Glioma Biopsies

Isocitrate dehydrogenase (IDH) mutational status is pivotal in the management of gliomas. Patients with IDH-mutated (IDH-MUT) tumors have a better prognosis and benefit more from extended surgical resection than IDH wild-type (IDH-WT). Raman spectroscopy (RS) is a minimally invasive optical technique with great potential for intraoperative diagnosis. We evaluated the RS’s ability to characterize the IDH mutational status onto unprocessed glioma biopsies. We extracted 2073 Raman spectra from thirty-eight unprocessed samples. The classification performance was assessed using the eXtreme Gradient Boosted trees (XGB) and Support Vector Machine with Radial Basis Function kernel (RBF-SVM). Measured Raman spectra displayed differences between IDH-MUT and IDH-WT tumor tissue. From the 103 Raman shifts screened as input features, the cross-validation loop identified 52 shifts with the highest performance in the distinction of the two groups. Raman analysis showed differences in spectral features of lipids, collagen, DNA and cholesterol/phospholipids. We were able to distinguish between IDH-MUT and IDH-WT tumors with an accuracy and precision of 87%. RS is a valuable and accurate tool for characterizing the mutational status of IDH mutation in unprocessed glioma samples. This study improves RS knowledge for future personalized surgical strategy or in situ target therapies for glioma tumors

Design and Evaluation of the Kinect-Wheelchair Interface Controlled (KWIC) Smart Wheelchair for Pediatric Powered Mobility Training

<div><p>Background: Children with severe disabilities are sometimes unable to access powered mobility training. Thus, we developed the Kinect-Wheelchair Interface Controlled (KWIC) smart wheelchair trainer that converts a manual wheelchair into a powered wheelchair. The KWIC Trainer uses computer vision to create a virtual tether with adaptive shared-control between the wheelchair and a therapist during training. It also includes a mixed-reality video game system. Methods: We performed a year-long usability study of the KWIC Trainer at a local clinic, soliciting qualitative and quantitative feedback on the device after extended use. Results: Eight therapists used the KWIC Trainer for over 50 hours with 8 different children. Two of the children obtained their own powered wheelchair as a result of the training. The therapists indicated the device allowed them to provide mobility training for more children than would have been possible with a demo wheelchair, and they found use of the device to be as safe as or safer than conventional training. They viewed the shared control algorithm as counter-productive because it made it difficult for the child to discern when he or she was controlling the chair. They were enthusiastic about the video game integration for increasing motivation and engagement during training. They emphasized the need for additional access methods for controlling the device. Conclusion: The therapists confirmed that the KWIC Trainer is a useful tool for increasing access to powered mobility training and for engaging children during training sessions. However, some improvements would enhance its applicability for routine clinical use.</p></div