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Penalver-Andres JA, Buetler KA, Koenig T, Müri RM, Marchal-Crespo L. Resting-State Functional Networks Correlate with Motor Performance in a Complex Visuomotor Task: An EEG Microstate Pilot Study on Healthy Individuals. Brain Topogr 2024; 37:590-607. [PMID: 36566448 PMCID: PMC11199229 DOI: 10.1007/s10548-022-00934-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Accepted: 12/05/2022] [Indexed: 12/26/2022]
Abstract
Developing motor and cognitive skills is needed to achieve expert (motor) performance or functional recovery from a neurological condition, e.g., after stroke. While extensive practice plays an essential role in the acquisition of good motor performance, it is still unknown whether certain person-specific traits may predetermine the rate of motor learning. In particular, learners' functional brain organisation might play an important role in appropriately performing motor tasks. In this paper, we aimed to study how two critical cognitive brain networks-the Attention Network (AN) and the Default Mode Network (DMN)-affect the posterior motor performance in a complex visuomotor task: virtual surfing. We hypothesised that the preactivation of the AN would affect how participants divert their attention towards external stimuli, resulting in robust motor performance. Conversely, the excessive involvement of the DMN-linked to internally diverted attention and mind-wandering-would be detrimental for posterior motor performance. We extracted seven widely accepted microstates-representing participants mind states at rest-out of the Electroencephalography (EEG) resting-state recordings of 36 healthy volunteers, prior to execution of the virtual surfing task. By correlating neural biomarkers (microstates) and motor behavioural metrics, we confirmed that the preactivation of the posterior DMN was correlated with poor posterior performance in the motor task. However, we only found a non-significant association between AN preactivation and the posterior motor performance. In this EEG study, we propose the preactivation of the posterior DMN-imaged using EEG microstates-as a neural trait related to poor posterior motor performance. Our findings suggest that the role of the executive control system is to preserve an homeostasis between the AN and the DMN. Therefore, neurofeedback-based downregulation of DMN preactivation could help optimise motor training.
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Affiliation(s)
- Joaquin A Penalver-Andres
- Motor Learning and Neurorehabilitation Laboratory, ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland.
- Psychosomatic Medicine, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
| | - Karin A Buetler
- Motor Learning and Neurorehabilitation Laboratory, ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
| | - Thomas Koenig
- Translational Research Center, University Hospital of Psychiatry, University of Bern, Bern, Switzerland
| | - René M Müri
- Perception and Eye Movement Laboratory, Department of Biomedical Research (DBMR) and Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
- Gerontechnology and Rehabilitation Group, ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
| | - Laura Marchal-Crespo
- Motor Learning and Neurorehabilitation Laboratory, ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
- Department of Cognitive Robotics, Delft University of Technology, Delft, The Netherlands
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Nepomuceno P, Souza WH, Pakosh M, Musselman KE, Craven BC. Exoskeleton-based exercises for overground gait and balance rehabilitation in spinal cord injury: a systematic review of dose and dosage parameters. J Neuroeng Rehabil 2024; 21:73. [PMID: 38705999 PMCID: PMC11070073 DOI: 10.1186/s12984-024-01365-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Accepted: 04/22/2024] [Indexed: 05/07/2024] Open
Abstract
BACKGROUND Exoskeletons are increasingly applied during overground gait and balance rehabilitation following neurological impairment, although optimal parameters for specific indications are yet to be established. OBJECTIVE This systematic review aimed to identify dose and dosage of exoskeleton-based therapy protocols for overground locomotor training in spinal cord injury/disease. METHODS A systematic review was conducted in accordance with the Preferred Reporting Items Systematic Reviews and Meta-Analyses guidelines. A literature search was performed using the CINAHL Complete, Embase, Emcare Nursing, Medline ALL, and Web of Science databases. Studies in adults with subacute and/or chronic spinal cord injury/disease were included if they reported (1) dose (e.g., single session duration and total number of sessions) and dosage (e.g., frequency of sessions/week and total duration of intervention) parameters, and (2) at least one gait and/or balance outcome measure. RESULTS Of 2,108 studies identified, after removing duplicates and filtering for inclusion, 19 were selected and dose, dosage and efficacy were abstracted. Data revealed a great heterogeneity in dose, dosage, and indications, with overall recommendation of 60-min sessions delivered 3 times a week, for 9 weeks in 27 sessions. Specific protocols were also identified for functional restoration (60-min, 3 times a week, for 8 weeks/24 sessions) and cardiorespiratory rehabilitation (60-min, 3 times a week, for 12 weeks/36 sessions). CONCLUSION This review provides evidence-based best practice recommendations for overground exoskeleton training among individuals with spinal cord injury/disease based on individual therapeutic goals - functional restoration or cardiorespiratory rehabilitation. There is a need for structured exoskeleton clinical translation studies based on standardized methods and common therapeutic outcomes.
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Affiliation(s)
- Patrik Nepomuceno
- KITE Research Institute, University Health Network, Toronto, ON, Canada
- Graduate Program in Health Promotion, Department of Health Sciences, University of Santa Cruz do Sul, Santa Cruz do Sul, RS, Brazil
- Institute of Health Policy Management and Evaluation, University of Toronto, Toronto, Canada
| | - Wagner H Souza
- KITE Research Institute, University Health Network, Toronto, ON, Canada
| | - Maureen Pakosh
- KITE Research Institute, University Health Network, Toronto, ON, Canada
| | - Kristin E Musselman
- KITE Research Institute, University Health Network, Toronto, ON, Canada
- Department of Physical Therapy, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Rehabilitation Sciences Institute, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
| | - B Catharine Craven
- KITE Research Institute, University Health Network, Toronto, ON, Canada.
- Department of Medicine, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada.
- Institute of Health Policy Management and Evaluation, University of Toronto, Toronto, Canada.
- Rehabilitation Sciences Institute, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada.
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Rätz R, Ratschat AL, Cividanes-Garcia N, Ribbers GM, Marchal-Crespo L. Designing for usability: development and evaluation of a portable minimally-actuated haptic hand and forearm trainer for unsupervised stroke rehabilitation. Front Neurorobot 2024; 18:1351700. [PMID: 38638360 PMCID: PMC11024237 DOI: 10.3389/fnbot.2024.1351700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Accepted: 03/20/2024] [Indexed: 04/20/2024] Open
Abstract
In stroke rehabilitation, simple robotic devices hold the potential to increase the training dosage in group therapies and to enable continued therapy at home after hospital discharge. However, we identified a lack of portable and cost-effective devices that not only focus on improving motor functions but also address sensory deficits. Thus, we designed a minimally-actuated hand training device that incorporates active grasping movements and passive pronosupination, complemented by a rehabilitative game with meaningful haptic feedback. Following a human-centered design approach, we conducted a usability study with 13 healthy participants, including three therapists. In a simulated unsupervised environment, the naive participants had to set up and use the device based on written instructions. Our mixed-methods approach included quantitative data from performance metrics, standardized questionnaires, and eye tracking, alongside qualitative feedback from semi-structured interviews. The study results highlighted the device's overall ease of setup and use, as well as its realistic haptic feedback. The eye-tracking analysis further suggested that participants felt safe during usage. Moreover, the study provided crucial insights for future improvements such as a more intuitive and comfortable wrist fixation, more natural pronosupination movements, and easier-to-follow instructions. Our research underscores the importance of continuous testing in the development process and offers significant contributions to the design of user-friendly, unsupervised neurorehabilitation technologies to improve sensorimotor stroke rehabilitation.
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Affiliation(s)
- Raphael Rätz
- Motor Learning and Neurorehabilitation Laboratory, ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
- Department of Cognitive Robotics, Delft University of Technology, Delft, Netherlands
| | - Alexandre L. Ratschat
- Department of Cognitive Robotics, Delft University of Technology, Delft, Netherlands
- Department of Rehabilitation Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, Netherlands
| | | | - Gerard M. Ribbers
- Department of Rehabilitation Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, Netherlands
- Rijndam Rehabilitation Center, Rotterdam, Netherlands
| | - Laura Marchal-Crespo
- Motor Learning and Neurorehabilitation Laboratory, ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
- Department of Cognitive Robotics, Delft University of Technology, Delft, Netherlands
- Department of Rehabilitation Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, Netherlands
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Radhakrishnan U, Kuang L, Koumaditis K, Chinello F, Pacchierotti C. Haptic Feedback, Performance and Arousal: A Comparison Study in an Immersive VR Motor Skill Training Task. IEEE TRANSACTIONS ON HAPTICS 2024; 17:249-262. [PMID: 37747855 DOI: 10.1109/toh.2023.3319034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/27/2023]
Abstract
This article investigates the relationship between fine motor skill training in VR, haptic feedback, and physiological arousal. To do so, we present the design and development of a motor skill task (buzzwire), along with a custom vibrotactile feedback attachment for the Geomagic Touch haptic device. A controlled experiment following a between-subjects design was conducted with 73 participants, studying the role of three feedback conditions - visual/kinesthetic, visual/vibrotactile and visual only - on the learning and performance of the considered task and the arousal levels of the participants. Results indicate that performance improved in all three feedback conditions after the considered training session. However, participants reported no change in self-efficacy and in terms of presence and task load (NASA-TLX). All three feedback conditions also showed similar arousal levels. Further analysis revealed that positive changes in performance were linked to higher arousal levels. These results suggest the potential of haptic feedback to affect arousal levels and encourage further research into using this relationship to improve motor skill training in VR.
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Kunavar T, Jamšek M, Avila-Mireles EJ, Rueckert E, Peternel L, Babič J. The Effects of Different Motor Teaching Strategies on Learning a Complex Motor Task. SENSORS (BASEL, SWITZERLAND) 2024; 24:1231. [PMID: 38400387 PMCID: PMC10892071 DOI: 10.3390/s24041231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Revised: 02/07/2024] [Accepted: 02/08/2024] [Indexed: 02/25/2024]
Abstract
During the learning of a new sensorimotor task, individuals are usually provided with instructional stimuli and relevant information about the target task. The inclusion of haptic devices in the study of this kind of learning has greatly helped in the understanding of how an individual can improve or acquire new skills. However, the way in which the information and stimuli are delivered has not been extensively explored. We have designed a challenging task with nonintuitive visuomotor perturbation that allows us to apply and compare different motor strategies to study the teaching process and to avoid the interference of previous knowledge present in the naïve subjects. Three subject groups participated in our experiment, where the learning by repetition without assistance, learning by repetition with assistance, and task Segmentation Learning techniques were performed with a haptic robot. Our results show that all the groups were able to successfully complete the task and that the subjects' performance during training and evaluation was not affected by modifying the teaching strategy. Nevertheless, our results indicate that the presented task design is useful for the study of sensorimotor teaching and that the presented metrics are suitable for exploring the evolution of the accuracy and precision during learning.
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Affiliation(s)
- Tjasa Kunavar
- Laboratory for Neromechanics and Biorobotics, Department of Automatics and Biocybernetics, Jožef Stefan Institute, 1000 Ljubljana, Slovenia
- Jožef Stefan International Postgraduate School, Jamova cesta 39, 1000 Ljubljana, Slovenia
| | - Marko Jamšek
- Laboratory for Neromechanics and Biorobotics, Department of Automatics and Biocybernetics, Jožef Stefan Institute, 1000 Ljubljana, Slovenia
| | - Edwin Johnatan Avila-Mireles
- Laboratory for Neromechanics and Biorobotics, Department of Automatics and Biocybernetics, Jožef Stefan Institute, 1000 Ljubljana, Slovenia
| | - Elmar Rueckert
- Chair of Cyber-Physical-Systems, Montauniversität Leoben, 8700 Leoben, Austria
| | - Luka Peternel
- Department of Cognitive Robotics, Delft University of Technology, 2628 CD Delft, The Netherlands
| | - Jan Babič
- Laboratory for Neromechanics and Biorobotics, Department of Automatics and Biocybernetics, Jožef Stefan Institute, 1000 Ljubljana, Slovenia
- Faculty of Electrical Engineering, University of Ljubljana, 1000 Ljubljana, Slovenia
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Tivadar RI, Franceschiello B, Minier A, Murray MM. Learning and navigating digitally rendered haptic spatial layouts. NPJ SCIENCE OF LEARNING 2023; 8:61. [PMID: 38102127 PMCID: PMC10724186 DOI: 10.1038/s41539-023-00208-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Accepted: 11/28/2023] [Indexed: 12/17/2023]
Abstract
Learning spatial layouts and navigating through them rely not simply on sight but rather on multisensory processes, including touch. Digital haptics based on ultrasounds are effective for creating and manipulating mental images of individual objects in sighted and visually impaired participants. Here, we tested if this extends to scenes and navigation within them. Using only tactile stimuli conveyed via ultrasonic feedback on a digital touchscreen (i.e., a digital interactive map), 25 sighted, blindfolded participants first learned the basic layout of an apartment based on digital haptics only and then one of two trajectories through it. While still blindfolded, participants successfully reconstructed the haptically learned 2D spaces and navigated these spaces. Digital haptics were thus an effective means to learn and translate, on the one hand, 2D images into 3D reconstructions of layouts and, on the other hand, navigate actions within real spaces. Digital haptics based on ultrasounds represent an alternative learning tool for complex scenes as well as for successful navigation in previously unfamiliar layouts, which can likely be further applied in the rehabilitation of spatial functions and mitigation of visual impairments.
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Affiliation(s)
- Ruxandra I Tivadar
- The Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
- Department of Ophthalmology, Fondation Asile des Aveugles, Lausanne, Switzerland.
- Centre for Integrative and Complementary Medicine, Department of Anesthesiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
- Cognitive Computational Neuroscience Group, Institute for Computer Science, University of Bern, Bern, Switzerland.
- The Sense Innovation and Research Center, Lausanne and Sion, Switzerland.
| | - Benedetta Franceschiello
- The Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- The Sense Innovation and Research Center, Lausanne and Sion, Switzerland
- Institute of Systems Engineering, School of Engineering, University of Applied Sciences Western Switzerland (HES-SO Valais), Sion, Switzerland
| | - Astrid Minier
- The Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- Department of Ophthalmology, Fondation Asile des Aveugles, Lausanne, Switzerland
| | - Micah M Murray
- The Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
- Department of Ophthalmology, Fondation Asile des Aveugles, Lausanne, Switzerland.
- The Sense Innovation and Research Center, Lausanne and Sion, Switzerland.
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Gasperina SD, Ratschat AL, Marchal-Crespo L. Quantitative and Qualitative Evaluation of Exoskeleton Transparency Controllers for Upper-Limb Neurorehabilitation. IEEE Int Conf Rehabil Robot 2023; 2023:1-6. [PMID: 37941246 DOI: 10.1109/icorr58425.2023.10304703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2023]
Abstract
High transparency is a fundamental requirement for upper-limb exoskeletons to promote active patient participation. Although various control strategies have been suggested to improve the transparency of these robots, there are still some limitations, such as the need for precise dynamic models and potential safety issues when external forces are applied to the robot. This study presents a novel hybrid controller designed to tackle these limitations by combining a traditional zero-torque controller with an interaction torque observer that compensates for residual undesired disturbances. The transparency of the proposed controller was evaluated using both quantitative-e.g., residual joint torques and movement smoothness-and qualitative measures-e.g., comfort, agency, and perceived resistance-in a pilot study with six healthy participants. The performance of the new controller was compared to that of two conventional controllers: a zero-torque closed-loop controller and a velocity-based disturbance observer. Our preliminary results show that the proposed hybrid controller may be a good alternative to state-of-the-art controllers as it allows participants to perform precise and smooth movements with low interaction joint torques. Importantly, participants rated the new controller higher in comfort and agency, and lower in perceived resistance. This study highlights the importance of incorporating both quantitative and qualitative assessments in evaluating control strategies developed to enhance the transparency of rehabilitation robots.
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Kucuktabak EB, Wen Y, Short M, Demirbas E, Lynch K, Pons J. Virtual Physical Coupling of Two Lower-Limb Exoskeletons. IEEE Int Conf Rehabil Robot 2023; 2023:1-6. [PMID: 37941279 DOI: 10.1109/icorr58425.2023.10304601] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2023]
Abstract
Physical interaction between individuals plays an important role in human motor learning and performance during shared tasks. Using robotic devices, researchers have studied the effects of dyadic haptic interaction mostly focusing on the upper-limb. Developing infrastructure that enables physical interactions between multiple individuals' lower limbs can extend the previous work and facilitate investigation of new dyadic lower-limb rehabilitation schemes. We designed a system to render haptic interactions between two users while they walk in multi-joint lower-limb exoskeletons. Specifically, we developed an infrastructure where desired interaction torques are commanded to the individual lower-limb exoskeletons based on the users' kinematics and the properties of the virtual coupling. In this pilot study, we demonstrated the capacity of the platform to render different haptic properties (e.g., soft and hard), different haptic connection types (e.g., bidirectional and unidirectional), and connections expressed in joint space and in task space. With haptic connection, dyads generated synchronized movement, and the difference between joint angles decreased as the virtual stiffness increased. This is the first study where multi-joint dyadic haptic interactions are created between lower-limb exoskeletons. This platform will be used to investigate effects of haptic interaction on motor learning and task performance during walking, a complex and meaningful task for gait rehabilitation.
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van Dellen F, Aurich-Schuler T, Labruyère R. Within- and between-therapist agreement on personalized parameters for robot-assisted gait therapy: the challenge of adjusting robotic assistance. J Neuroeng Rehabil 2023; 20:81. [PMID: 37340308 DOI: 10.1186/s12984-023-01176-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Accepted: 04/19/2023] [Indexed: 06/22/2023] Open
Abstract
BACKGROUND Stationary robotic gait trainers usually allow for adjustment of training parameters, including gait speed, body weight support and robotic assistance, to personalize therapy. Consequently, therapists personalize parameter settings to pursue a relevant therapy goal for each patient. Previous work has shown that the choice of parameters influences the behavior of patients. At the same time, randomized clinical trials usually do not report the applied settings and do not consider them in the interpretation of their results. The choice of adequate parameter settings therefore remains one of the major challenges that therapists face in everyday clinical practice. For therapy to be most effective, personalization should ideally result in repeatable parameter settings for repeatable therapy situations, irrespective of the therapist who adjusts the parameters. This has not yet been investigated. Therefore, the aim of the present study was to investigate the agreement of parameter settings from session to session within a therapist and between two different therapists in children and adolescents undergoing robot-assisted gait training. METHODS AND RESULTS Fourteen patients walked in the robotic gait trainer Lokomat on 2 days. Two therapists from a pool of 5 therapists independently personalized gait speed, bodyweight support and robotic assistance for a moderately and a vigorously intensive therapy task. There was a very high agreement within and between therapists for the parameters gait speed and bodyweight support, but a substantially lower agreement for robotic assistance. CONCLUSION These findings imply that therapists perform consistently at setting parameters that have a very clear and visible clinical effect (e.g. walking speed and bodyweight support). However, they have more difficulties with robotic assistance, which has a more ambiguous effect because patients may respond differently to changes. Future work should therefore focus on better understanding patient reactions to changes in robotic assistance and especially on how instructions can be employed to steer these reactions. To improve the agreement, we propose that therapists link their choice of robotic assistance to the individual therapy goals of the patients and closely guide the patients during walking with instructions.
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Affiliation(s)
- Florian van Dellen
- Sensory-Motor Systems Lab, Department of Health Sciences and Technology, ETH Zurich, Tannenstrasse 1, 8092, Zurich, Switzerland.
- Swiss Children's Rehab, University Children's Hospital Zurich, Mühlebergstrasse 104, 8910, Affoltern Am Albis, Switzerland.
- Children's Research Center, University Children's Hospital Zurich, Steinwiesstrasse 75, CH-8032, Zurich, Switzerland.
| | - T Aurich-Schuler
- Swiss Children's Rehab, University Children's Hospital Zurich, Mühlebergstrasse 104, 8910, Affoltern Am Albis, Switzerland
- Children's Research Center, University Children's Hospital Zurich, Steinwiesstrasse 75, CH-8032, Zurich, Switzerland
| | - Rob Labruyère
- Swiss Children's Rehab, University Children's Hospital Zurich, Mühlebergstrasse 104, 8910, Affoltern Am Albis, Switzerland
- Children's Research Center, University Children's Hospital Zurich, Steinwiesstrasse 75, CH-8032, Zurich, Switzerland
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de Miguel-Fernández J, Lobo-Prat J, Prinsen E, Font-Llagunes JM, Marchal-Crespo L. Control strategies used in lower limb exoskeletons for gait rehabilitation after brain injury: a systematic review and analysis of clinical effectiveness. J Neuroeng Rehabil 2023; 20:23. [PMID: 36805777 PMCID: PMC9938998 DOI: 10.1186/s12984-023-01144-5] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 01/07/2023] [Indexed: 02/21/2023] Open
Abstract
BACKGROUND In the past decade, there has been substantial progress in the development of robotic controllers that specify how lower-limb exoskeletons should interact with brain-injured patients. However, it is still an open question which exoskeleton control strategies can more effectively stimulate motor function recovery. In this review, we aim to complement previous literature surveys on the topic of exoskeleton control for gait rehabilitation by: (1) providing an updated structured framework of current control strategies, (2) analyzing the methodology of clinical validations used in the robotic interventions, and (3) reporting the potential relation between control strategies and clinical outcomes. METHODS Four databases were searched using database-specific search terms from January 2000 to September 2020. We identified 1648 articles, of which 159 were included and evaluated in full-text. We included studies that clinically evaluated the effectiveness of the exoskeleton on impaired participants, and which clearly explained or referenced the implemented control strategy. RESULTS (1) We found that assistive control (100% of exoskeletons) that followed rule-based algorithms (72%) based on ground reaction force thresholds (63%) in conjunction with trajectory-tracking control (97%) were the most implemented control strategies. Only 14% of the exoskeletons implemented adaptive control strategies. (2) Regarding the clinical validations used in the robotic interventions, we found high variability on the experimental protocols and outcome metrics selected. (3) With high grade of evidence and a moderate number of participants (N = 19), assistive control strategies that implemented a combination of trajectory-tracking and compliant control showed the highest clinical effectiveness for acute stroke. However, they also required the longest training time. With high grade of evidence and low number of participants (N = 8), assistive control strategies that followed a threshold-based algorithm with EMG as gait detection metric and control signal provided the highest improvements with the lowest training intensities for subacute stroke. Finally, with high grade of evidence and a moderate number of participants (N = 19), assistive control strategies that implemented adaptive oscillator algorithms together with trajectory-tracking control resulted in the highest improvements with reduced training intensities for individuals with chronic stroke. CONCLUSIONS Despite the efforts to develop novel and more effective controllers for exoskeleton-based gait neurorehabilitation, the current level of evidence on the effectiveness of the different control strategies on clinical outcomes is still low. There is a clear lack of standardization in the experimental protocols leading to high levels of heterogeneity. Standardized comparisons among control strategies analyzing the relation between control parameters and biomechanical metrics will fill this gap to better guide future technical developments. It is still an open question whether controllers that provide an on-line adaptation of the control parameters based on key biomechanical descriptors associated to the patients' specific pathology outperform current control strategies.
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Affiliation(s)
- Jesús de Miguel-Fernández
- Biomechanical Engineering Lab, Department of Mechanical Engineering and Research Centre for Biomedical Engineering, Universitat Politècnica de Catalunya, Diagonal 647, 08028 Barcelona, Spain
- Institut de Recerca Sant Joan de Déu, Santa Rosa 39-57, 08950 Esplugues de Llobregat, Spain
| | | | - Erik Prinsen
- Roessingh Research and Development, Roessinghsbleekweg 33b, 7522AH Enschede, Netherlands
| | - Josep M. Font-Llagunes
- Biomechanical Engineering Lab, Department of Mechanical Engineering and Research Centre for Biomedical Engineering, Universitat Politècnica de Catalunya, Diagonal 647, 08028 Barcelona, Spain
- Institut de Recerca Sant Joan de Déu, Santa Rosa 39-57, 08950 Esplugues de Llobregat, Spain
| | - Laura Marchal-Crespo
- Cognitive Robotics Department, Delft University of Technology, Mekelweg 2, 2628 Delft, Netherlands
- Motor Learning and Neurorehabilitation Lab, ARTORG Center for Biomedical Engineering Research, University of Bern, Freiburgstrasse 3, 3010 Bern, Switzerland
- Department of Rehabilitation Medicine, Erasmus MC University Medical Center, Doctor Molewaterplein 40, 3015 GD Rotterdam, The Netherlands
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Rodrigues-Carvalho C, Fernández-García M, Pinto-Fernández D, Sanz-Morere C, Barroso FO, Borromeo S, Rodríguez-Sánchez C, Moreno JC, del-Ama AJ. Benchmarking the Effects on Human-Exoskeleton Interaction of Trajectory, Admittance and EMG-Triggered Exoskeleton Movement Control. SENSORS (BASEL, SWITZERLAND) 2023; 23:791. [PMID: 36679587 PMCID: PMC9867281 DOI: 10.3390/s23020791] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 12/12/2022] [Accepted: 01/04/2023] [Indexed: 06/17/2023]
Abstract
Nowadays, robotic technology for gait training is becoming a common tool in rehabilitation hospitals. However, its effectiveness is still controversial. Traditional control strategies do not adequately integrate human intention and interaction and little is known regarding the impact of exoskeleton control strategies on muscle coordination, physical effort, and user acceptance. In this article, we benchmarked three types of exoskeleton control strategies in a sample of seven healthy volunteers: trajectory assistance (TC), compliant assistance (AC), and compliant assistance with EMG-Onset stepping control (OC), which allows the user to decide when to take a step during the walking cycle. This exploratory study was conducted within the EUROBENCH project facility. Experimental procedures and data analysis were conducted following EUROBENCH's protocols. Specifically, exoskeleton kinematics, muscle activation, heart and breathing rates, skin conductance, as well as user-perceived effort were analyzed. Our results show that the OC controller showed robust performance in detecting stepping intention even using a corrupt EMG acquisition channel. The AC and OC controllers resulted in similar kinematic alterations compared to the TC controller. Muscle synergies remained similar to the synergies found in the literature, although some changes in muscle contribution were found, as well as an overall increase in agonist-antagonist co-contraction. The OC condition led to the decreased mean duration of activation of synergies. These differences were not reflected in the overall physiological impact of walking or subjective perception. We conclude that, although the AC and OC walking conditions allowed the users to modulate their walking pattern, the application of these two controllers did not translate into significant changes in the overall physiological cost of walking nor the perceived experience of use. Nonetheless, results suggest that both AC and OC controllers are potentially interesting approaches that can be explored as gait rehabilitation tools. Furthermore, the INTENTION project is, to our knowledge, the first study to benchmark the effects on human-exoskeleton interaction of three different exoskeleton controllers, including a new EMG-based controller designed by us and never tested in previous studies, which has made it possible to provide valuable third-party feedback on the use of the EUROBENCH facility and testbed, enriching the apprenticeship of the project consortium and contributing to the scientific community.
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Affiliation(s)
- Camila Rodrigues-Carvalho
- Neural Rehabilitation Group, Cajal Institute, Spanish National Research Council (CSIC), 28002 Madrid, Spain
- Systems Engineering and Automation Department, Carlos III University of Madrid, 28903 Madrid, Spain
| | | | - David Pinto-Fernández
- Neural Rehabilitation Group, Cajal Institute, Spanish National Research Council (CSIC), 28002 Madrid, Spain
- CAR-UPM Associated Unit, Universidad Politécnica de Madrid, 28040 Madrid, Spain
| | - Clara Sanz-Morere
- Center for Clinical Neuroscience, Hospital Los Madroños, 28690 Madrid, Spain
| | - Filipe Oliveira Barroso
- Neural Rehabilitation Group, Cajal Institute, Spanish National Research Council (CSIC), 28002 Madrid, Spain
| | - Susana Borromeo
- Electronic Technology Department, Rey Juan Carlos University, 28933 Móstoles, Spain
| | | | - Juan C. Moreno
- Neural Rehabilitation Group, Cajal Institute, Spanish National Research Council (CSIC), 28002 Madrid, Spain
| | - Antonio J. del-Ama
- Electronic Technology Department, Rey Juan Carlos University, 28933 Móstoles, Spain
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12
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Li X, Lu Q, Chen P, Gong S, Yu X, He H, Li K. Assistance level quantification-based human-robot interaction space reshaping for rehabilitation training. Front Neurorobot 2023; 17:1161007. [PMID: 37205055 PMCID: PMC10185799 DOI: 10.3389/fnbot.2023.1161007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 04/10/2023] [Indexed: 05/21/2023] Open
Abstract
Stroke has become a major disease that seriously threatens human health due to its high incidence and disability rates. Most patients undergo upper limb motor dysfunction after stroke, which significantly impairs the ability of stroke survivors in their activities of daily living (ADL). Robots provide an optional solution for stroke rehabilitation by attending therapy in the hospital and the community, however, the rehabilitation robot still has difficulty in providing needed assistance interactively like human clinicians in conventional therapy. For safe and rehabilitation training, a human-robot interaction space reshaping method was proposed based on the recovery states of patients. According to different recovery states, we designed seven experimental protocols suitable for distinguishing rehabilitation training sessions. To achieve assist-as-needed (AAN) control, a PSO-SVM classification model and an LSTM-KF regression model were introduced to recognize the motor ability of patients with electromyography (EMG) and kinematic data, and a region controller for interaction space shaping was studied. Ten groups of offline and online experiments and corresponding data processing were conducted, and the machine learning and AAN control results were presented, which ensured the effective and the safe upper limb rehabilitation training. To discuss the human-robot interaction in different training stages and sessions, we defined a quantified assistance level index that characterizes the rehabilitation needs by considering the engagement of the patients and had the potential to apply in clinical upper limb rehabilitation training.
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Affiliation(s)
- Xiangyun Li
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
- Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Qi Lu
- Sichuan University-Pittsburgh Institute, Sichuan University, Chengdu, China
| | - Peng Chen
- School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, China
- *Correspondence: Peng Chen
| | - Shan Gong
- Sichuan University-Pittsburgh Institute, Sichuan University, Chengdu, China
| | - Xi Yu
- Department of Orthopedic Surgery and Orthopedic Research Institute, West China Hospital, Sichuan University, Chengdu, China
- Department of Rehabilitation Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Hongchen He
- Department of Rehabilitation Medicine, West China Hospital, Sichuan University, Chengdu, China
- School of Rehabilitation Sciences, West China School of Medicine, Sichuan University, Chengdu, China
- Key Laboratory of Rehabilitation Medicine in Sichuan Province, West China Hospital, Sichuan University, Chengdu, China
- Hongchen He
| | - Kang Li
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
- Med-X Center for Informatics, Sichuan University, Chengdu, China
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13
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Agarwal R, Hussain A, SKM V, Campolo D. Let the force guide you: a performance-based adaptive algorithm for postural training using haptic feedback. Front Hum Neurosci 2022; 16:968669. [PMID: 36504631 PMCID: PMC9729548 DOI: 10.3389/fnhum.2022.968669] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Accepted: 11/07/2022] [Indexed: 11/27/2022] Open
Abstract
Motor learning is an essential component of human behavior. Many different factors can influence the process of motor learning, such as the amount of practice and type of feedback. Changes in task difficulty during training can also considerably impact motor learning. Typical motor learning studies include a sequential variation of task difficulty, i.e., easy to challenging, irrespective of user performance. However, many studies have reported the importance of performance-based task difficulty variation for effective motor learning and skill transfer. A performance-based adaptive algorithm for task difficulty variation based on the challenge-point framework is proposed in this study. The algorithm is described for postural adaptation during simultaneous upper-limb training. Ten healthy participants (28 ± 2.44 years) were recruited to validate the algorithm. Participants adapted to a postural target of 20° in the anterior direction from the initial upright posture while performing a unimanual reaching task using a robotic device. Results suggest a significant decrease in postural error after training. The algorithm successfully adapted the task difficulty based on the performance of the user. The proposed algorithm could be modified for different motor skills and can be further evaluated for different applications in order to maximize the potential benefits of rehabilitation sessions.
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Affiliation(s)
- Rakhi Agarwal
- Department of Applied Mechanics, Indian Institute of Technology Madras, Chennai, India,School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore, Singapore
| | | | - Varadhan SKM
- Department of Applied Mechanics, Indian Institute of Technology Madras, Chennai, India
| | - Domenico Campolo
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore, Singapore,*Correspondence: Domenico Campolo
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14
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Interaction with a reactive partner improves learning in contrast to passive guidance. Sci Rep 2022; 12:15821. [PMID: 36138031 PMCID: PMC9499977 DOI: 10.1038/s41598-022-18617-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Accepted: 08/16/2022] [Indexed: 11/08/2022] Open
Abstract
Many tasks such as physical rehabilitation, vehicle co-piloting or surgical training, rely on physical assistance from a partner. While this assistance may be provided by a robotic interface, how to implement the necessary haptic support to help improve performance without impeding learning is unclear. In this paper, we study the influence of haptic interaction on the performance and learning of a shared tracking task. We compare in a tracking task the interaction with a human partner, the trajectory guidance traditionally used in training robots, and a robot partner yielding human-like interaction. While trajectory guidance resulted in the best performance during training, it dramatically reduced error variability and hindered learning. In contrast, the reactive human and robot partners did not impede the adaptation and allowed the subjects to learn without modifying their movement patterns. Moreover, interaction with a human partner was the only condition that demonstrated an improvement in retention and transfer learning compared to a subject training alone. These results reveal distinctly different learning behaviour in training with a human compared to trajectory guidance, and similar learning between the robotic partner and human partner. Therefore, for movement assistance and learning, algorithms that react to the user's motion and change their behaviour accordingly are better suited.
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15
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Villar Ortega E, Aksöz EA, Buetler KA, Marchal-Crespo L. Enhancing touch sensibility by sensory retraining in a sensory discrimination task via haptic rendering. FRONTIERS IN REHABILITATION SCIENCES 2022; 3:929431. [PMID: 36189030 PMCID: PMC9397824 DOI: 10.3389/fresc.2022.929431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 07/07/2022] [Indexed: 11/22/2022]
Abstract
Stroke survivors are commonly affected by somatosensory impairment, hampering their ability to interpret somatosensory information. Somatosensory information has been shown to critically support movement execution in healthy individuals and stroke survivors. Despite the detrimental effect of somatosensory impairments on performing activities of daily living, somatosensory training—in stark contrast to motor training—does not represent standard care in neurorehabilitation. Reasons for the neglected somatosensory treatment are the lack of high-quality research demonstrating the benefits of somatosensory interventions on stroke recovery, the unavailability of reliable quantitative assessments of sensorimotor deficits, and the labor-intensive nature of somatosensory training that relies on therapists guiding the hands of patients with motor impairments. To address this clinical need, we developed a virtual reality-based robotic texture discrimination task to assess and train touch sensibility. Our system incorporates the possibility to robotically guide the participants' hands during texture exploration (i.e., passive touch) and no-guided free texture exploration (i.e., active touch). We ran a 3-day experiment with thirty-six healthy participants who were asked to discriminate the odd texture among three visually identical textures –haptically rendered with the robotic device– following the method of constant stimuli. All participants trained with the passive and active conditions in randomized order on different days. We investigated the reliability of our system using the Intraclass Correlation Coefficient (ICC). We also evaluated the enhancement of participants' touch sensibility via somatosensory retraining and compared whether this enhancement differed between training with active vs. passive conditions. Our results showed that participants significantly improved their task performance after training. Moreover, we found that training effects were not significantly different between active and passive conditions, yet, passive exploration seemed to increase participants' perceived competence. The reliability of our system ranged from poor (in active condition) to moderate and good (in passive condition), probably due to the dependence of the ICC on the between-subject variability, which in a healthy population is usually small. Together, our virtual reality-based robotic haptic system may be a key asset for evaluating and retraining sensory loss with minimal supervision, especially for brain-injured patients who require guidance to move their hands.
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Affiliation(s)
- Eduardo Villar Ortega
- Motor Learning and Neurorehabilitation Laboratory, ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
- *Correspondence: Eduardo Villar Ortega
| | - Efe Anil Aksöz
- Motor Learning and Neurorehabilitation Laboratory, ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
- Division of Mechanical Engineering, Department of Engineering and Information Technology, Institute for Rehabilitation and Performance Technology, Bern University of Applied Sciences, Burgdorf, Switzerland
| | - Karin A. Buetler
- Motor Learning and Neurorehabilitation Laboratory, ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
| | - Laura Marchal-Crespo
- Motor Learning and Neurorehabilitation Laboratory, ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
- Department of Cognitive Robotics, Delft University of Technology, Delft, Netherlands
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16
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Zeng H, Yu W, Chen D, Hu X, Zhang D, Song A. Exploring Biomimetic Stiffness Modulation and Wearable Finger Haptics for Improving Myoelectric Control of Virtual Hand. IEEE Trans Neural Syst Rehabil Eng 2022; 30:1601-1611. [PMID: 35675253 DOI: 10.1109/tnsre.2022.3181284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
The embodiment of virtual hand (VH) by the user is generally deemed to be important for virtual reality (VR) based hand rehabilitation applications, which may help to engage the user and promote motor skill relearning. In particular, it requires that the VH should produce task-dependent interaction behaviors from rigid to soft. While such a capability is inherent to humans via hand stiffness regulation and haptic interactions, yet it have not been successfully imitated by VH in existing studies. In this paper, we present a work which integrates biomimetic stiffness regulation and wearable finger force feedback in VR scenarios involving myoelectric control of VH. On one hand, the biomimetic stiffness modulation intuitively enables VH to imitate the stiffness profile of the user's hand in real time. On the other hand, the wearable finger force-feedback device elicits a natural and realistic sensation of external force on the fingertip, which provides the user a proper understanding of the environment for enhancing his/her stiffness regulation. The benefits of the proposed integrated system were evaluated with eight healthy subjects that performed two tasks with opposite stiffness requirements. The achieved performance is compared with reduced versions of the integrated system, where either biomimetic impedance control or wearable force feedback is excluded. The results suggest that the proposed integrated system enables the stiffness of VH to be adaptively regulated by the user through the perception of interaction torques and vision, resulting in task-dependent behaviors from rigid to soft for VH.
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17
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Buetler KA, Penalver-Andres J, Özen Ö, Ferriroli L, Müri RM, Cazzoli D, Marchal-Crespo L. “Tricking the Brain” Using Immersive Virtual Reality: Modifying the Self-Perception Over Embodied Avatar Influences Motor Cortical Excitability and Action Initiation. Front Hum Neurosci 2022; 15:787487. [PMID: 35221950 PMCID: PMC8863605 DOI: 10.3389/fnhum.2021.787487] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 12/13/2021] [Indexed: 02/02/2023] Open
Abstract
To offer engaging neurorehabilitation training to neurologic patients, motor tasks are often visualized in virtual reality (VR). Recently introduced head-mounted displays (HMDs) allow to realistically mimic the body of the user from a first-person perspective (i.e., avatar) in a highly immersive VR environment. In this immersive environment, users may embody avatars with different body characteristics. Importantly, body characteristics impact how people perform actions. Therefore, alternating body perceptions using immersive VR may be a powerful tool to promote motor activity in neurologic patients. However, the ability of the brain to adapt motor commands based on a perceived modified reality has not yet been fully explored. To fill this gap, we “tricked the brain” using immersive VR and investigated if multisensory feedback modulating the physical properties of an embodied avatar influences motor brain networks and control. Ten healthy participants were immersed in a virtual environment using an HMD, where they saw an avatar from first-person perspective. We slowly transformed the surface of the avatar (i.e., the “skin material”) from human to stone. We enforced this visual change by repetitively touching the real arm of the participant and the arm of the avatar with a (virtual) hammer, while progressively replacing the sound of the hammer against skin with stone hitting sound via loudspeaker. We applied single-pulse transcranial magnetic simulation (TMS) to evaluate changes in motor cortical excitability associated with the illusion. Further, to investigate if the “stone illusion” affected motor control, participants performed a reaching task with the human and stone avatar. Questionnaires assessed the subjectively reported strength of embodiment and illusion. Our results show that participants experienced the “stone arm illusion.” Particularly, they rated their arm as heavier, colder, stiffer, and more insensitive when immersed with the stone than human avatar, without the illusion affecting their experienced feeling of body ownership. Further, the reported illusion strength was associated with enhanced motor cortical excitability and faster movement initiations, indicating that participants may have physically mirrored and compensated for the embodied body characteristics of the stone avatar. Together, immersive VR has the potential to influence motor brain networks by subtly modifying the perception of reality, opening new perspectives for the motor recovery of patients.
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Affiliation(s)
- Karin A. Buetler
- Motor Learning and Neurorehabilitation Laboratory, ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
- *Correspondence: Karin A. Buetler,
| | - Joaquin Penalver-Andres
- Motor Learning and Neurorehabilitation Laboratory, ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
- Psychosomatic Medicine, Department of Neurology, University Hospital of Bern (Inselspital), Bern, Switzerland
| | - Özhan Özen
- Motor Learning and Neurorehabilitation Laboratory, ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
| | - Luca Ferriroli
- Motor Learning and Neurorehabilitation Laboratory, ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
| | - René M. Müri
- Gerontechnology and Rehabilitation Group, ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
- Department of Neurology, University Neurorehabilitation, University Hospital of Bern (Inselspital), University of Bern, Bern, Switzerland
| | - Dario Cazzoli
- Gerontechnology and Rehabilitation Group, ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
- Department of Neurology, University Neurorehabilitation, University Hospital of Bern (Inselspital), University of Bern, Bern, Switzerland
- Neurocenter, Luzerner Kantonsspital, Lucerne, Switzerland
| | - Laura Marchal-Crespo
- Motor Learning and Neurorehabilitation Laboratory, ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
- Department of Cognitive Robotics, Delft University of Technology, Delft, Netherlands
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18
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Wenk N, Jordi MV, Buetler KA, Marchal-Crespo L. Hiding Assistive Robots During Training in Immersive VR Does not Affect Users' Motivation, Presence, Embodiment, Performance, nor Visual Attention. IEEE Trans Neural Syst Rehabil Eng 2022; 30:390-399. [PMID: 35085087 DOI: 10.1109/tnsre.2022.3147260] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Combining immersive virtual reality (VR) using head-mounted displays (HMDs) with assisting robotic devices might be a promising procedure to enhance neurorehabilitation. However, it is still an open question how immersive virtual environments (VE) should be designed when interacting with rehabilitation robots. In conventional training, the robot is usually not visually represented in the VE, resulting in a visuo-haptic sensory conflict between what users see and feel. This study aimed to investigate how motivation, embodiment, and presence are affected by this visuo-haptic sensory conflict. Using an HMD and a rehabilitation robot, 28 healthy participants performed a path-tracing task, while the robot was either visually reproduced in the VE or not and while the robot either assisted the movements or not. Participants' performance and visual attention were measured during the tasks, and after each visibility/assistance condition, they reported their motivation, presence, and embodiment with questionnaires. We found that, independently of the assistance, the robot visibility did not affect participants' motivation, presence, embodiment, nor task performance. We only found a greater effort/importance reported when the robot was visible. The visual attention was also slightly affected by the robot's visibility. Importantly, we found that the robotic assistance hampered presence and embodiment, but improved motivation. Our results indicate no disadvantage of not reproducing robotic devices in VEs when using HMDs. However, caution must be put when developing assisting controllers, as they might hamper users' affect.
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Küçüktabak EB, Kim SJ, Wen Y, Lynch K, Pons JL. Human-machine-human interaction in motor control and rehabilitation: a review. J Neuroeng Rehabil 2021; 18:183. [PMID: 34961530 PMCID: PMC8714449 DOI: 10.1186/s12984-021-00974-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Accepted: 12/07/2021] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND Human-human (HH) interaction mediated by machines (e.g., robots or passive sensorized devices), which we call human-machine-human (HMH) interaction, has been studied with increasing interest in the last decade. The use of machines allows the implementation of different forms of audiovisual and/or physical interaction in dyadic tasks. HMH interaction between two partners can improve the dyad's ability to accomplish a joint motor task (task performance) beyond either partner's ability to perform the task solo. It can also be used to more efficiently train an individual to improve their solo task performance (individual motor learning). We review recent research on the impact of HMH interaction on task performance and individual motor learning in the context of motor control and rehabilitation, and we propose future research directions in this area. METHODS A systematic search was performed on the Scopus, IEEE Xplore, and PubMed databases. The search query was designed to find studies that involve HMH interaction in motor control and rehabilitation settings. Studies that do not investigate the effect of changing the interaction conditions were filtered out. Thirty-one studies met our inclusion criteria and were used in the qualitative synthesis. RESULTS Studies are analyzed based on their results related to the effects of interaction type (e.g., audiovisual communication and/or physical interaction), interaction mode (collaborative, cooperative, co-active, and competitive), and partner characteristics. Visuo-physical interaction generally results in better dyadic task performance than visual interaction alone. In cases where the physical interaction between humans is described by a spring, there are conflicting results as to the effect of the stiffness of the spring. In terms of partner characteristics, having a more skilled partner improves dyadic task performance more than having a less skilled partner. However, conflicting results were observed in terms of individual motor learning. CONCLUSIONS Although it is difficult to draw clear conclusions as to which interaction type, mode, or partner characteristic may lead to optimal task performance or individual motor learning, these results show the possibility for improved outcomes through HMH interaction. Future work that focuses on selecting the optimal personalized interaction conditions and exploring their impact on rehabilitation settings may facilitate the transition of HMH training protocols to clinical implementations.
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Affiliation(s)
- Emek Barış Küçüktabak
- Department of Mechanical Engineering, McCormick School of Engineering, Northwestern University, 60208 Evanston, IL USA
- Legs + Walking Lab, Shirley Ryan Ability Lab, 60611 Chicago, IL USA
| | - Sangjoon J. Kim
- Legs + Walking Lab, Shirley Ryan Ability Lab, 60611 Chicago, IL USA
- Department of Physical Medicine and Rehabilitation, Feinberg School of Medicine, Northwestern University, 60611 Chicago, IL USA
| | - Yue Wen
- Legs + Walking Lab, Shirley Ryan Ability Lab, 60611 Chicago, IL USA
- Department of Physical Medicine and Rehabilitation, Feinberg School of Medicine, Northwestern University, 60611 Chicago, IL USA
| | - Kevin Lynch
- Department of Mechanical Engineering, McCormick School of Engineering, Northwestern University, 60208 Evanston, IL USA
| | - Jose L. Pons
- Department of Mechanical Engineering, McCormick School of Engineering, Northwestern University, 60208 Evanston, IL USA
- Legs + Walking Lab, Shirley Ryan Ability Lab, 60611 Chicago, IL USA
- Department of Physical Medicine and Rehabilitation, Feinberg School of Medicine, Northwestern University, 60611 Chicago, IL USA
- Department of Biomedical Engineering, McCormick School of Engineering, Northwestern University, 60208 Evanston, IL USA
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Penalver-Andres J, Buetler KA, Koenig T, Müri RM, Marchal-Crespo L. Providing Task Instructions During Motor Training Enhances Performance and Modulates Attentional Brain Networks. Front Neurosci 2021; 15:755721. [PMID: 34955719 PMCID: PMC8695982 DOI: 10.3389/fnins.2021.755721] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Accepted: 10/18/2021] [Indexed: 11/21/2022] Open
Abstract
Learning a new motor task is a complex cognitive and motor process. Especially early during motor learning, cognitive functions such as attentional engagement, are essential, e.g., to discover relevant visual stimuli. Drawing participant's attention towards task-relevant stimuli-e.g., with task instructions using visual cues or explicit written information-is a common practice to support cognitive engagement during training and, hence, accelerate motor learning. However, there is little scientific evidence about how visually cued or written task instructions affect attentional brain networks during motor learning. In this experiment, we trained 36 healthy participants in a virtual motor task: surfing waves by steering a boat with a joystick. We measured the participants' motor performance and observed attentional brain networks using alpha-band electroencephalographic (EEG) activity before and after training. Participants received one of the following task instructions during training: (1) No explicit task instructions and letting participants surf freely (implicit training; IMP); (2) Task instructions provided through explicit visual cues (explicit-implicit training; E-IMP); or (3) through explicit written commands (explicit training; E). We found that providing task instructions during training (E and E-IMP) resulted in less post-training motor variability-linked to enhanced performance-compared to training without instructions (IMP). After training, participants trained with visual cues (E-IMP) enhanced the alpha-band strength over parieto-occipital and frontal brain areas at wave onset. In contrast, participants who trained with explicit commands (E) showed decreased fronto-temporal alpha activity. Thus, providing task instructions in written (E) or using visual cues (E-IMP) leads to similar motor performance improvements by enhancing activation on different attentional networks. While training with visual cues (E-IMP) may be associated with visuo-attentional processes, verbal-analytical processes may be more prominent when written explicit commands are provided (E). Together, we suggest that training parameters such as task instructions, modulate the attentional networks observed during motor practice and may support participant's cognitive engagement, compared to training without instructions.
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Affiliation(s)
- Joaquin Penalver-Andres
- Motor Learning and Neurorehabilitation Laboratory, ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
- Psychosomatic Medicine, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Karin A. Buetler
- Motor Learning and Neurorehabilitation Laboratory, ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
| | - Thomas Koenig
- Translational Research Center, University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - René Martin Müri
- Gerontechnology and Rehabilitation Group, ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
- Perception and Eye Movement Laboratory, Department of Neurology and BioMedical Research, University of Bern, Bern, Switzerland
- Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Laura Marchal-Crespo
- Motor Learning and Neurorehabilitation Laboratory, ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
- Department of Cognitive Robotics, Delft University of Technology, Delft, Netherlands
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