Effect of a mixed reality-based intervention on arm, hand, and finger function on chronic stroke

[EN] Background: Virtual and mixed reality systems have been suggested to promote motor recovery after stroke. Basing on the existing evidence on motor learning, we have developed a portable and low-cost mixed reality tabletop system that transforms a conventional table in a virtual environment for upper limb rehabilitation. The system allows intensive and customized training of a wide range of arm, hand, and finger movements and enables interaction with tangible objects, while providing audiovisual feedback of the participants' performance in gamified tasks. This study evaluates the clinical effectiveness and the acceptance of an experimental intervention with the system in chronic stroke survivors. Methods: Thirty individuals with stroke were included in a reversal (A-B-A) study. Phase A consisted of 30 sessions of conventional physical therapy. Phase B consisted of 30 training sessions with the experimental system. Both interventions involved flexion and extension of the elbow, wrist, and fingers, and grasping of different objects. Sessions were 45-min long and were administered three to five days a week. The body structures (Modified Ashworth Scale), functions (Motricity Index, Fugl-Meyer Assessment Scale), activities (Manual Function Test, Wolf Motor Function Test, Box and Blocks Test, Nine Hole Peg Test), and participation (Motor Activity Log) were assessed before and after each phase. Acceptance of the system was also assessed after phase B (System Usability Scale, Intrinsic Motivation Inventory). Results: Significant improvement was detected after the intervention with the system in the activity, both in arm function measured by the Wolf Motor Function Test (p < 0.01) and finger dexterity measured by the Box and Blocks Test (p < 0.01) and the Nine Hole Peg Test (p < 0.01); and participation (p < 0.01), which was maintained to the end of the study. The experimental system was reported as highly usable, enjoyable, and motivating. Conclusions: Our results support the clinical effectiveness of mixed reality interventions that satisfy the motor learning principles for upper limb rehabilitation in chronic stroke survivors. This characteristic, together with the low cost of the system, its portability, and its acceptance could promote the integration of these systems in the clinical practice as an alternative to more expensive systems, such as robotic instruments.The authors wish to thank the staff and patients of the Servicio de Neurorrehabilitación y Daño Cerebral de los Hospitales NISA for their involvement in the study. The authors also wish to thank the staff of LabHuman for their support in this project, especially Francisco Toledo and José Roda for their assistance. This study was funded in part by the Project TEREHA (IDI-20110844) and Project NeuroVR (TIN2013-44741-R) of the Ministerio de Economia y Competitividad of Spain, the Project Consolider-C (SEJ2006-14301/PSIC) of the Ministerio de Educacion y Ciencia of Spain, the "CIBER of Physiopathology of Obesity and Nutrition, an initiative of ISCIII", and the Excellence Research Program PROMETEO of the Conselleria de Educacion of Generalitat Valenciana (2008-157).Colomer Font, C.; Llorens Rodríguez, R.; Noé Sebastián, E.; Alcañiz Raya, ML. (2016). Effect of a mixed reality-based intervention on arm, hand, and finger function on chronic stroke. Journal of NeuroEngineering and Rehabilitation. 13:1-10. https://doi.org/10.1186/s12984-016-0153-6S11013Fregni F, Pascual-Leone A. Hand motor recovery after stroke: tuning the orchestra to improve hand motor function. Cogn Behav Neurol. 2006;19(1):21–33.Patten C, Condliffe EG, Dairaghi CA, Lum PS. Concurrent neuromechanical and functional gains following upper-extremity power training post-stroke. J Neuroeng Rehabil. 2013;10:1.Turolla A, Dam M, Ventura L, Tonin P, Agostini M, Zucconi C, et al. Virtual reality for the rehabilitation of the upper limb motor function after stroke: a prospective controlled trial. J Neuroeng Rehabil. 2013;10:85.Dancause N, Nudo RJ. Shaping plasticity to enhance recovery after injury. Prog Brain Res. 2011;192:273–95.Kwakkel G, Kollen B, Lindeman E. Understanding the pattern of functional recovery after stroke: facts and theories. Restor Neurol Neurosci. 2004;22(3–5):281–99.Nielsen JB, Willerslev-Olsen M, Christiansen L, Lundbye-Jensen J, Lorentzen J. Science-based neurorehabilitation: recommendations for neurorehabilitation from basic science. J Mot Behav. 2015;47(1):7–17.Shaughnessy M, Resnick BM. Using theory to develop an exercise intervention for patients post stroke. Top Stroke Rehabil. 2009;16(2):140–6.Subramanian SK, Massie CL, Malcolm MP, Levin MF. Does provision of extrinsic feedback result in improved motor learning in the upper limb poststroke? A systematic review of the evidence. Neurorehabil Neural Repair. 2010;24(2):113–24.Arya KN, Verma R, Garg RK, Sharma VP, Agarwal M, Aggarwal GG. Meaningful task-specific training (MTST) for stroke rehabilitation: a randomized controlled trial. Top Stroke Rehabil. 2012;19(3):193–211.Levin MF, Weiss PL, Keshner EA. Emergence of Virtual Reality as a Tool for Upper Limb Rehabilitation: Incorporation of Motor Control and Motor Learning Principles. Phys Ther. 2015;95(3):415–25.Laver K, George S, Thomas S, Deutsch JE, Crotty M. Cochrane review: virtual reality for stroke rehabilitation. Eur J Phys Rehabil Med. 2012;48(3):523–30.Cameirao MS, Badia SB, Duarte E, Frisoli A, Verschure PF. The combined impact of virtual reality neurorehabilitation and its interfaces on upper extremity functional recovery in patients with chronic stroke. Stroke. 2012;43(10):2720–8.Saposnik G, Levin M, G. Outcome Research Canada Working. Virtual reality in stroke rehabilitation: a meta-analysis and implications for clinicians. Stroke. 2011;42(5):1380–6.Viau A, Feldman AG, McFadyen BJ, Levin MF. Reaching in reality and virtual reality: a comparison of movement kinematics in healthy subjects and in adults with hemiparesis. J Neuroeng Rehabil. 2004;1(1):11.Thornton M, Marshall S, McComas J, Finestone H, McCormick A, Sveistrup H. Benefits of activity and virtual reality based balance exercise programmes for adults with traumatic brain injury: perceptions of participants and their caregivers. Brain Inj. 2005;19(12):989–1000.Mazzoleni S, Puzzolante L, Zollo L, Dario P, Posteraro F. Mechanisms of motor recovery in chronic and subacute stroke patients following a robot-aided training. IEEE Trans Haptics. 2014;7(2):175–80.Duff M, Chen Y, Cheng L, Liu SM, Blake P, Wolf SL, et al. Adaptive mixed reality rehabilitation improves quality of reaching movements more than traditional reaching therapy following stroke. Neurorehabil Neural Repair. 2013;27(4):306–15.Mousavi Hondori, H., M. Khademi, L. Dodakian, A. McKenzie, C.V. Lopes, and S.C. Cramer, Choice of Human-Computer Interaction Mode in Stroke Rehabilitation. Neurorehabil Neural Repair, 2015.Bohannon RW, Smith MB. Interrater reliability of a modified Ashworth scale of muscle spasticity. Phys Ther. 1987;67(2):206–7.Paternostro-Sluga T, Grim-Stieger M, Posch M, Schuhfried O, Vacariu G, Mittermaier C, et al. Reliability and validity of the Medical Research Council (MRC) scale and a modified scale for testing muscle strength in patients with radial palsy. J Rehabil Med. 2008;40(8):665–71.Kopp B, Kunkel A, Flor H, Platz T, Rose U, Mauritz KH, et al. The Arm Motor Ability Test: reliability, validity, and sensitivity to change of an instrument for assessing disabilities in activities of daily living. Arch Phys Med Rehabil. 1997;78(6):615–20.Folstein MF, Folstein SE, McHugh PR. “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res. 1975;12(3):189–98.Romero M, Sanchez A, Marin C, Navarro MD, Ferri J, Noe E. Clinical usefulness of the Spanish version of the Mississippi Aphasia Screening Test (MASTsp): validation in stroke patients. Neurologia. 2012;27(4):216–24.Llorens R, Marín C, Ortega M, Alcaniz M, Colomer C, Navarro MD, et al. Upper limb tracking using depth information for rehabilitative tangible tabletop systems, in 9th International Conference on Disability, Virtual Reality & Associated Technologies. Laval, France: The University of Reading; 2012. p. 463–466.Alt Murphy M, Resteghini C, Feys P, Lamers I. An overview of systematic reviews on upper extremity outcome measures after stroke. BMC Neurol. 2015;15:29.Sloan RL, Sinclair E, Thompson J, Taylor S, Pentland B. Inter-rater reliability of the modified Ashworth Scale for spasticity in hemiplegic patients. Int J Rehabil Res. 1992;15(2):158–61.van der Ploeg RJ, Fidler V, Oosterhuis HJ. Hand-held myometry: reference values. J Neurol Neurosurg Psychiatry. 1991;54(3):244–7.Duncan PW, Propst M, Nelson SG. Reliability of the Fugl-Meyer assessment of sensorimotor recovery following cerebrovascular accident. Phys Ther. 1983;63(10):1606–10.Miyamoto S, Kondo T, Suzukamo Y, Michimata A, Izumi S. Reliability and validity of the Manual Function Test in patients with stroke. Am J Phys Med Rehabil. 2009;88(3):247–55.Woodbury M, Velozo CA, Thompson PA, Light K, Uswatte G, Taub E, et al. Measurement structure of the Wolf Motor Function Test: implications for motor control theory. Neurorehabil Neural Repair. 2010;24(9):791–801.Mathiowetz V, Volland G, Kashman N, Weber K. Adult norms for the Box and Block Test of manual dexterity. Am J Occup Ther. 1985;39(6):386–91.Oxford Grice K, Vogel KA, Le V, Mitchell A, Muniz S, Vollmer MA. Adult norms for a commercially available Nine Hole Peg Test for finger dexterity. Am J Occup Ther. 2003;57(5):570–3.Hammer AM, Lindmark B. Responsiveness and validity of the Motor Activity Log in patients during the subacute phase after stroke. Disabil Rehabil. 2010;32(14):1184–93.Bullinger HJ, F.-I.f.A.u. Organisation, and U.S.I.f.A.u. Technologiemanagement. Human Aspects in Computing: Design and use of interactive systems and work with terminals. Elsevier; 1991.McAuley E, Duncan T, Tammen VV. Psychometric properties of the Intrinsic Motivation Inventory in a competitive sport setting: a confirmatory factor analysis. Res Q Exerc Sport. 1989;60(1):48–58.Mazzoleni S, Sale P, Tiboni M, Franceschini M, Carrozza MC, Posteraro F. Upper limb robot-assisted therapy in chronic and subacute stroke patients: a kinematic analysis. Am J Phys Med Rehabil. 2013;92(10 Suppl 2):e26–37.Lin KC, Hsieh YW, Wu CY, Chen CL, Jang Y, Liu JS. Minimal detectable change and clinically important difference of the Wolf Motor Function Test in stroke patients. Neurorehabil Neural Repair. 2009;23(5):429–34.Fu TS, Wu CY, Lin KC, Hsieh CJ, Liu JS, Wang TN, et al. Psychometric comparison of the shortened Fugl-Meyer Assessment and the streamlined Wolf Motor Function Test in stroke rehabilitation. Clin Rehabil. 2012;26(11):1043–7.Hsieh YW, Wu CY, Lin KC, Chang YF, Chen CL, Liu JS. Responsiveness and validity of three outcome measures of motor function after stroke rehabilitation. Stroke. 2009;40(4):1386–91.van der Lee JH, Beckerman H, Lankhorst GJ, Bouter LM. The responsiveness of the Action Research Arm test and the Fugl-Meyer Assessment scale in chronic stroke patients. J Rehabil Med. 2001;33(3):110–3.Wolf SL, Catlin PA, Ellis M, Archer AL, Morgan B, Piacentino A. Assessing Wolf Motor Function Test as Outcome Measure for Research in Patients After Stroke. Stroke. 2001;32(7):1635–9.Reinkensmeyer DJ, Wolbrecht ET, Chan V, Chou C, Cramer SC, Bobrow JE. Comparison of three-dimensional, assist-as-needed robotic arm/hand movement training provided with Pneu-WREX to conventional tabletop therapy after chronic stroke. Am J Phys Med Rehabil. 2012;91(11 Suppl 3):S232–41.Takahashi CD, Der-Yeghiaian L, Le V, Motiwala RR, Cramer SC. Robot-based hand motor therapy after stroke. Brain. 2008;131(Pt 2):425–37.Sale P, Mazzoleni S, Lombardi V, Galafate D, Massimiani MP, Posteraro F, et al. Recovery of hand function with robot-assisted therapy in acute stroke patients: a randomized-controlled trial. Int J Rehabil Res. 2014;37(3):236–42.Hwang CH, Seong JW, Son DS. Individual finger synchronized robot-assisted hand rehabilitation in subacute to chronic stroke: a prospective randomized clinical trial of efficacy. Clin Rehabil. 2012;26(8):696–704.Timmermans AA, Seelen HA, Willmann RD, Kingma H. Technology-assisted training of arm-hand skills in stroke: concepts on reacquisition of motor control and therapist guidelines for rehabilitation technology design. J Neuroeng Rehabil. 2009;6:1.Levin MF, Kleim JA, Wolf SL. What do motor “recovery” and “compensation” mean in patients following stroke? Neurorehabil Neural Repair. 2009;23(4):313–9.Rosati G, Oscari F, Spagnol S, Avanzini F, Masiero S. Effect of task-related continuous auditory feedback during learning of tracking motion exercises. J Neuroeng Rehabil. 2012;9:79.Imam B, Jarus T. Virtual reality rehabilitation from social cognitive and motor learning theoretical perspectives in stroke population. Rehabil Res Pract. 2014;2014:594540.Schuster-Amft C, Henneke A, Hartog-Keisker B, Holper L, Siekierka E, Chevrier E, et al. Intensive virtual reality-based training for upper limb motor function in chronic stroke: a feasibility study using a single case experimental design and fMRI. Disabil Rehabil Assist Technol. 2015;10(5):385–92.Llorens R, Noe E, Colomer C, Alcaniz M. Effectiveness, usability, and cost-benefit of a virtual reality-based telerehabilitation program for balance recovery after stroke: a randomized controlled trial. Arch Phys Med Rehabil. 2015;96(3):418–25. e2.Llorens R, Gil-Gomez JA, Alcaniz M, Colomer C, Noe E. Improvement in balance using a virtual reality-based stepping exercise: a randomized controlled trial involving individuals with chronic stroke. Clin Rehabil. 2015;29(3):7

Feasibility of a walking virtual reality system for rehabilitation: objective and subjective parameters

[EN] Background: Even though virtual reality (VR) is increasingly used in rehabilitation, the implementation of walking navigation in VR still poses a technological challenge for current motion tracking systems. Different metaphors simulate locomotion without involving real gait kinematics, which can affect presence, orientation, spatial memory and cognition, and even performance. All these factors can dissuade their use in rehabilitation. We hypothesize that a marker-based head tracking solution would allow walking in VR with high sense of presence and without causing sickness. The objectives of this study were to determine the accuracy, the jitter, and the lag of the tracking system and its elicited sickness and presence in comparison of a CAVE system. Methods: The accuracy and the jitter around the working area at three different heights and the lag of the head tracking system were analyzed. In addition, 47 healthy subjects completed a search task that involved navigation in the walking VR system and in the CAVE system. Navigation was enabled by natural locomotion in the walking VR system and through a specific device in the CAVE system. An HMD was used as display in the walking VR system. After interacting with each system, subjects rated their sickness in a seven-point scale and their presence in the Slater-Usoh-Steed Questionnaire and a modified version of the Presence Questionnaire. Results: Better performance was registered at higher heights, where accuracy was less than 0.6 cm and the jitter was about 6 mm. The lag of the system was 120 ms. Participants reported that both systems caused similar low levels of sickness (about 2.4 over 7). However, ratings showed that the walking VR system elicited higher sense of presence than the CAVE system in both the Slater-Usoh-Steed Questionnaire (17.6 +/- 0.3 vs 14.6 +/- 0.6 over 21, respectively) and the modified Presence Questionnaire (107.4 +/- 2.0 vs 93.5 +/- 3.2 over 147, respectively). Conclusions: The marker-based solution provided accurate, robust, and fast head tracking to allow navigation in the VR system by walking without causing relevant sickness and promoting higher sense of presence than CAVE systems, thus enabling natural walking in full-scale environments, which can enhance the ecological validity of VR-based rehabilitation applications.The authors wish to thank the staff of LabHuman for their support in this project, especially José Miguel Martínez and José Roda for their assistance. This study was funded in part by Ministerio de Economia y Competitividad of Spain (Project NeuroVR, TIN2013-44741-R and Project REACT, TIN2014-61975-EXP), by Ministerio de Educacion y Ciencia of Spain (Project Consolider-C, SEJ2006-14301/PSIC), and by Universitat Politecnica de Valencia (Grant PAID-10-14).Borrego, A.; Latorre Grau, J.; Llorens Rodríguez, R.; Alcañiz Raya, ML.; Noé, E. (2016). Feasibility of a walking virtual reality system for rehabilitation: objective and subjective parameters. Journal of NeuroEngineering and Rehabilitation. 13:1-9. https://doi.org/10.1186/s12984-016-0174-1S1913Lee KM. Presence. Explicated Communication Theory. 2004;14(1):27–50.Riva G. Is presence a technology issue? Some insights from cognitive sciences. Virtual Reality. 2009;13(3):159–69.Banos RM, et al. Immersion and emotion: their impact on the sense of presence. Cyberpsychol Behav. 2004;7(6):734–41.Llorens R, et al. Tracking systems for virtual rehabilitation: objective performance vs. subjective experience. A practical scenario. Sensors (Basel). 2015;15(3):6586–606.Navarro MD, et al. Validation of a low-cost virtual reality system for training street-crossing. A comparative study in healthy, neglected and non-neglected stroke individuals. Neuropsychol Rehabil. 2013;23(4):597–618.Parsons TD. Virtual reality for enhanced ecological validity and experimental control in the clinical, affective and social neurosciences. Front Hum Neurosci. 2015;9:660.Cameirao MS, et al. Neurorehabilitation using the virtual reality based Rehabilitation Gaming System: methodology, design, psychometrics, usability and validation. J Neuroeng Rehabil. 2010;7:48.Llorens R, et al. Improvement in balance using a virtual reality-based stepping exercise: a randomized controlled trial involving individuals with chronic stroke. Clin Rehabil. 2015;29(3):261–8.Llorens R, et al. Videogame-based group therapy to improve self-awareness and social skills after traumatic brain injury. J Neuroeng Rehabil. 2015;12:37.Fong KN, et al. Usability of a virtual reality environment simulating an automated teller machine for assessing and training persons with acquired brain injury. J Neuroeng Rehabil. 2010;7:19.Levin MF, Weiss PL, Keshner EA. Emergence of virtual reality as a tool for upper limb rehabilitation: incorporation of motor control and motor learning principles. Phys Ther. 2015;95(3):415–25.Llorens R, et al. Effectiveness, usability, and cost-benefit of a virtual reality-based telerehabilitation program for balance recovery after stroke: a randomized controlled trial. Arch Phys Med Rehabil. 2015;96(3):418–25. e2.Cruz-Neira C, et al. Scientists in wonderland: A report on visualization applications in the CAVE virtual reality environment. In: 1993. Proceedings IEEE 1993 Symposium on Research Frontiers in Virtual Reality. 1993.Juan MC, Perez D. Comparison of the levels of presence and anxiety in an acrophobic environment viewed via HMD or CAVE. Presence. 2009;18(3):232–48.Yang YR, et al. Virtual reality-based training improves community ambulation in individuals with stroke: a randomized controlled trial. Gait Posture. 2008;28(2):201–6.Cho KH, Lee WH. Virtual walking training program using a real-world video recording for patients with chronic stroke: a pilot study. Am J Phys Med Rehabil. 2013;92(5):371–84.Darter BJ, Wilken JM. Gait training with virtual reality-based real-time feedback: improving gait performance following transfemoral amputation. Phys Ther. 2011;91(9):1385–94.Yang S, et al. Improving balance skills in patients who had stroke through virtual reality treadmill training. Am J Phys Med Rehabil. 2011;90(12):969–78.Walker ML, et al. Virtual reality-enhanced partial body weight-supported treadmill training poststroke: feasibility and effectiveness in 6 subjects. Arch Phys Med Rehabil. 2010;91(1):115–22.Riley PO, et al. A kinematic and kinetic comparison of overground and treadmill walking in healthy subjects. Gait Posture. 2007;26(1):17–24.Alton F, et al. A kinematic comparison of overground and treadmill walking. Clin Biomech. 1998;13(6):434–40.Lee SJ, Hidler J. Biomechanics of overground vs. treadmill walking in healthy individuals. J Appl Physiol. 2008;104(3).Slater M. Measuring presence: a response to the witmer and Singer presence questionnaire. Presence. 1999;8(5):560–5.Viau A, et al. Reaching in reality and virtual reality: a comparison of movement kinematics in healthy subjects and in adults with hemiparesis. J Neuroeng Rehabil. 2004;1(1):11.Parsons TD, et al. The potential of function-led virtual environments for ecologically valid measures of executive function in experimental and clinical neuropsychology. Neuropsychol Rehabil. 2015;11:1–31. doi: 10.1080/09602011.2015.1109524 .Aravind G, Lamontagne A. Perceptual and locomotor factors affect obstacle avoidance in persons with visuospatial neglect. J Neuroeng Rehabil. 2014;11:38.Darekar A, Lamontagne A, Fung J. Dynamic clearance measure to evaluate locomotor and perceptuo-motor strategies used for obstacle circumvention in a virtual environment. Hum Mov Sci. 2015;40:359–71.Whittle MW. Chapter 4 - Methods of gait analysis. In: Whittle MW, editor. Gait analysis. Edinburgh: Butterworth-Heinemann; 2007. p. 137–75.Hodgson E, et al. WeaVR: a self-contained and wearable immersive virtual environment simulation system. Behav Res Methods. 2015;47(1):296–307.Akizuki H, et al. Effects of immersion in virtual reality on postural control. Neurosci Lett. 2005;379(1):23–6.Thies SB, et al. Comparison of linear accelerations from three measurement systems during "reach & grasp". Med Eng Phys. 2007;29(9):967–72.Fiala M. Designing highly reliable fiducial markers. IEEE Trans Pattern Anal Mach Intell. 2010;32(7):1317–24.Garrido-Jurado S, et al. Automatic generation and detection of highly reliable fiducial markers under occlusion. Pattern Recognition. 2014;47(6):2280–92.Kim K, et al. Effects of virtual environment platforms on emotional responses. Comput Methods Programs Biomed. 2014;113(3):882–93.Slater M, Steed A. A virtual presence counter. Presence. 2000;9(5):413–34.Witmer BG, Singer MJ. Measuring presence in virtual environments: a presence questionnaire. Presence Teleop Virt. 1998;7(3):225–40.Martín-Gutiérrez J, et al. Design and validation of an augmented book for spatial abilities development in engineering students. Comput Graph. 2010;34(1):77–91.Lopez-Mir F, et al. Design and validation of an augmented reality system for laparoscopic surgery in a real environment. Biomed Res Int. 2013;2013:758491.Abawi DF, Bienwald J, Dorner R. Accuracy in optical tracking with fiducial markers: an accuracy function for ARToolKit. In: Third IEEE and ACM International symposium on mixed and augmented reality, ISMAR 2004. 2004.Malbezin P, Piekarski W, Thomas BH. Measuring ARTootKit accuracy in long distance tracking experiments. In: The first IEEE International workshop augmented reality toolkit. 2002.Paquette C, Paquet N, Fung J. Aging affects coordination of rapid head motions with trunk and pelvis movements during standing and walking. Gait Posture. 2006;24(1):62–9.Graham JE, et al. Walking speed threshold for classifying walking independence in hospitalized older adults. Phys Ther. 2010;90(11):1591–7.Gorea A. A refresher of the original Bloch’s Law paper (bloch, july 1885). i-Perception. 2015;6:4.Moss JD, Muth ER. Characteristics of head-mounted displays and their effects on Simulator sickness. Hum Factors. 2011;53(3):308–19.Draper MH, et al. Effects of image scale and system time delay on Simulator sickness within head-coupled virtual environments. Hum Factors. 2001;43(1):129–46.Fujisaki W. Effects of delayed visual feedback on grooved pegboard test performance. Front Psychol. 2012;3:61.Keshner EA, et al. Augmenting sensory-motor conflict promotes adaptation of postural behaviors in a virtual environment. Conf Proc IEEE Eng Med Biol Soc. 2011;2011:1379–82.Slaboda JC, Keshner EA. Reorientation to vertical modulated by combined support surface tilt and virtual visual flow in healthy elders and adults with stroke. J Neurol. 2012;259(12):2664–72.Tossavainen T. Comparison of CAVE and HMD for visual stimulation in postural control research. Stud Health Technol Inform. 2004;98:385–7.Akiduki H, et al. Visual-vestibular conflict induced by virtual reality in humans. Neurosci Lett. 2003;340(3):197–200.Duh HBL, et al. Effects of field of view on balance in an immersive environment. In: Virtual Reality, 2001. Proceedings. IEEE. 2001.Krijn M, et al. Treatment of acrophobia in virtual reality: the role of immersion and presence. Behav Res Ther. 2004;42(2):229–39.Mania K, Chalmers A. The effects of levels of immersion on memory and presence in virtual environments: a reality centered approach. Cyberpsychol Behav. 2001;4(2):247–64.Gorini A, et al. The role of immersion and narrative in mediated presence: the virtual hospital experience. Cyberpsychol Behav Soc Netw. 2011;14(3):99–105.Fromberger P, et al. Virtual viewing time: the relationship between presence and sexual interest in androphilic and gynephilic Men. PLoS One. 2015;10(5), e0127156.Slater M, et al. Visual realism enhances realistic response in an immersive virtual environment. IEEE Comput Graph Appl. 2009;29(3):76–84.Nir-Hadad SY, et al. A virtual shopping task for the assessment of executive functions: Validity for people with stroke. Neuropsychol Rehabil. 2015;11:1–26. doi: 10.1080/09602011.2015.1109523 .Vasilyeva M, Lourenco SF. Development of spatial cognition. Wiley Interdiscip Rev Cogn Sci. 2012;3(3):349–62.Banakou D, Groten R, Slater M. Illusory ownership of a virtual child body causes overestimation of object sizes and implicit attitude changes. Proc Natl Acad Sci U S A. 2013;110(31):12846–51.Yee N, Bailenson JN, Ducheneaut N. The proteus effect: implications of transformed digital self-representation on online and offline behavior. Commun Res. 2009;36(2):285–312.Baylor AL. Promoting motivation with virtual agents and avatars: role of visual presence and appearance. Philos Trans R Soc Lond B Biol Sci. 2009;364(1535):3559–65.Clemente M, et al. Assessment of the influence of navigation control and screen size on the sense of presence in virtual reality using EEG. Expert Sys App. 2014;41(4, Part 2):1584–92.Clemente M, et al. An fMRI study to analyze neural correlates of presence during virtual reality experiences. 2013. Interacting with Computers

The combined impact of virtual reality neurorehabilitation and its interfaces on upper extremity functional recovery in patients with chronic stroke

Background and Purpose—Although there is strong evidence on the beneficial effects of virtual reality (VR)-based rehabilitation, it is not yet well understood how the different aspects of these systems affect recovery. Consequently, we do not exactly know what features of VR neurorehabilitation systems are decisive in conveying their beneficial effects. Methods—To specifically address this issue, we developed 3 different configurations of the same VR-based rehabilitation system, the Rehabilitation Gaming System, using 3 different interface technologies: vision-based tracking, haptics, and a passive exoskeleton. Forty-four patients with chronic stroke were randomly allocated to one of the configurations and used the system for 35 minutes a day for 5 days a week during 4 weeks. Results—Our results revealed significant within-subject improvements at most of the standard clinical evaluation scales for all groups. Specifically we observe that the beneficial effects of VR-based training are modulated by the use/nonuse of compensatory movement strategies and the specific sensorimotor contingencies presented to the user, that is, visual feedback versus combined visual haptic feedback. Conclusions—Our findings suggest that the beneficial effects of VR-based neurorehabilitation systems such as the Rehabilitation Gaming System for the treatment of chronic stroke depend on the specific interface systems used. These results have strong implications for the design of future VR rehabilitation strategies that aim at maximizing functional outcomes and their retention. Clinical Trial Registration—This trial was not registered because it is a small clinical study that evaluates the feasibility of prototype devices.info:eu-repo/semantics/publishedVersio

Optimizing performance of non-expert users in brain-computer interaction by means of an adaptive performance engine

Brain–Computer Interfaces (BCIs) are become increasingly more available at reduced costs and are being incorporated into immersive virtual environments and video games for serious applications. Most research in BCIs focused on signal processing techniques and has neglected the interaction aspect of BCIs. This has created an imbalance between BCI classification performance and online control quality of the BCI interaction. This results in user fatigue and loss of interest over time. In the health domain, BCIs provide a new way to overcome motor-related disabilities, promoting functional and structural plasticity in the brain. In order to exploit the advantages of BCIs in neurorehabilitation we need to maximize not only the classification performance of such systems but also engagement and the sense of competence of the user. Therefore, we argue that the primary goal should not be for users to be trained to successfully use a BCI system but to adapt the BCI interaction to each user in order to maximize the level of control on their actions, whatever their performance level is. To achieve this, we developed the Adaptive Performance Engine (APE) and tested with data from 20 naïve BCI users. APE can provide user specific performance improvements up to approx. 20% and we compare it with previous methods. Finally, we contribute with an open motor-imagery datasets with 2400 trials from naïve users.info:eu-repo/semantics/publishedVersio

Counteracting learned non-use in chronic stroke patients with reinforcement-induced movement therapy

Background: After stroke, patients who suffer from hemiparesis tend to suppress the use of the affected extremity, a condition called learned non-use. Consequently, the lack of training may lead to the progressive deterioration of motor function. Although Constraint-Induced Movement Therapies (CIMT) have shown to be effective in treating this condition, the method presents several limitations, and the high intensity of its protocols severely compromises its adherence. We propose a novel rehabilitation approach called Reinforcement-Induced Movement Therapy (RIMT), which proposes to restore motor function through maximizing arm use. This is achieved by exposing the patient to amplified goal-oriented movements in VR that match the intended actions of the patient. We hypothesize that through this method we can increase the patients self-efficacy, reverse learned non-use, and induce long-term motor improvements. Methods: We conducted a randomized, double-blind, longitudinal clinical study with 18 chronic stroke patients. Patients performed 30 minutes of daily VR-based training during six weeks. During training, the experimental group experienced goal-oriented movement amplification in VR. The control group followed the same training protocol but without movement amplification. Evaluators blinded to group designation performed clinical measurements at the beginning, at the end of the training and at 12-weeks follow-up. We used the Fugl-Meyer Assessment for the upper extremities (UE-FM) (Sanford et al., Phys Ther 73:447–454, 1993) as a primary outcome measurement of motor recovery. Secondary outcome measurements included the Chedoke Arm and Hand Activity Inventory (CAHAI-7) (Barreca et al., Arch Phys Med Rehabil 6:1616–1622, 2005) for measuring functional motor gains in the performance of Activities of Daily Living (ADLs), the Barthel Index (BI) for the evaluation of the patient’s perceived independence (Collin et al., Int Disabil Stud 10:61–63, 1988), and the Hamilton scale (Knesevich et al., Br J Psychiatr J Mental Sci 131:49–52, 1977) for the identification of improvements in mood disorders that could be induced by the reinforcement-based intervention. In order to study and predict the effects of this intervention we implemented a computational model of recovery after stroke. Results: While both groups showed significant motor gains at 6-weeks post-treatment, only the experimental group continued to exhibit further gains in UE-FM at 12-weeks follow-up (p<.05). This improvement was accompanied by a significant increase in arm-use during training in the experimental group. Conclusions: Implicitly reinforcing arm-use by augmenting visuomotor feedback as proposed by RIMT seems beneficial for inducing significant improvement in chronic stroke patients. By challenging the patients’ self-limiting believe system and perceived low self-efficacy this approach might counteract learned non-use.This project was supported through ERC project cDAC (FP7-IDEAS-ERC 341196), EC H2020 project socSMCs (H2020-EU.1.2.2. 641321) and MINECO project SANAR (Gobierno de España)