Wearable Robots in Rehabilitation Engineering Tremor Suppression

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The phase difference between neural drives to antagonist muscles in essential tremor is associated with the relative strength of supraspinal and afferent input

The pathophysiology of essential tremor (ET), the most common movement disorder, is not fully understood. We investigated which factors determine the variability in the phase difference between neural drives to antagonist muscles, a long-standing observation yet unexplained. We used a computational model to simulate the effects of different levels of voluntary and tremulous synaptic input to antagonistic motoneuron pools on the tremor. We compared these simulations to data from 11 human ET patients. In both analyses, the neural drive to muscle was represented as the pooled spike trains of several motor units, which provides an accurate representation of the common synaptic input to motoneurons. The simulations showed that, for each voluntary input level, the phase difference between neural drives to antagonist muscles is determined by the relative strength of the supraspinal tremor input to the motoneuron pools. In addition, when the supraspinal tremor input to one muscle was weak or absent, Ia afferents provided significant common tremor input due to passive stretch. The simulations predicted that without a voluntary drive (rest tremor) the neural drives would be more likely in phase, while a concurrent voluntary input (postural tremor) would lead more frequently to an out-of-phase pattern. The experimental results matched these predictions, showing a significant change in phase difference between postural and rest tremor. They also indicated that the common tremor input is always shared by the antagonistic motoneuron pools, in agreement with the simulations. Our results highlight that the interplay between supraspinal input and spinal afferents is relevant for tremor generation

Neurophysiologic Assessment of Motor Imagery Training by Using Virtual Reality for Pediatric Population with Cerebral Palsy

[ES] Existen diversas evidencias que indican que los déficits motores en los pacientes de parálisis cerebral se asocian con problemas en la planificación motora que, a su vez, apuntan a una mermada capacidad para imaginar movimientos. La imaginación motora se ha revelado como una herramienta efectiva en el aprendizaje y la adquisición de habilidades motoras ya que comparte estructuras neuronales similares con la ejecución motora. En este trabajo se presenta un paradigma basado en un juego de realidad virtual para guiar la actividad mental del paciente que sirve a dos fines: estudiar su capacidad de imaginar movimientos e implicar y motivar al paciente en el entrenamiento de dicha capacidad. El estudio ha involucrado cuatro niños con parálisis cerebral espástica (edad media = 13.25 años, DS = 1.5) con lesión cerebral bilateral. Los resultados obtenidos del análisis de su actividad electroencefalográfica muestran que estos pacientes son capaces de emplear la imaginación motora en una tarea de marcha, indicada por la presencia del fenómeno ERD (Event Related Desynchronization) en zonas corticales motoras, independientemente de su nivel funcional y de los miembros afectados.[EN] There are several evidences showing that motor disorders in patients with cerebral palsy are associated with problems in motor planning, which, in turn, denote a diminished capability to imagine movements. Motor imagery appears like an effective means in learning and  acquiring motor skills since it shares similar neural structures to those ones used in motor execution. In this paper, a paradigm based on a virtual reality game that drives the patient’s electroencephalographic signal is presented. This study aims, on the one hand, to analyze the patients’ ability of imagining movements and, on the other hand, to involve and motivate them in order to implement this ability. The research work has engaged four children with spastic cerebral palsy (mean age = 13.25, SD = 1.5) with bilateral brain damage. After analyzing their electroencephalographic signal, the results show that these patients are able of using motor imagery in a walking task, as indicated by the presence of ERD (event related desynchronization) in cortical motor areas, regardless their functional impairment and affected body extremities.Este trabajo ha sido financiado parcialmente por los proyectos CP-WALKER (DPI2012-39133-C03-01), MD (PIE201650E055) y NeuroMOD (DPI2015-68664-C4-1-R)Del Castillo, M.; Serrano, J.; Lerma, S.; Martínez, I.; Rocon, E. (2018). Evaluación Neurofisiológica del Entrenamiento de la Imaginación Motora con Realidad Virtual en Pacientes Pediátricos con Parálisis Cerebral. Revista Iberoamericana de Automática e Informática industrial. 15(2):174-179. https://doi.org/10.4995/riai.2017.8819OJS174179152Bayón, C., Ramírez, O., Serrano, J.I., del Castillo, M.D., Pérez-Somarriba, A., Belda-Lois, J.M., Martínez-Caballero, I., Lerma-Lara, S., Cifuentes, C., Frizera, A., Rocon, E., 2017. Development and evaluation of a novel robotic platform for gait rehabilitation in patients with cerebral palsy: CPWalker. Robotics and Autonomous Systems, 91, 101-114. https://doi.org/10.1016/j.robot.2016.12.015Blair, E., 2010. Epidemiology of the cerebral palsies. Orthopedic Clinics of North America, 41, 441-55. https://doi.org/10.1016/j.ocl.2010.06.004Chang, M.C., Kim, D.Y., Park, D.H., 2015. Enhancement of cortical excitability and lower limb motor function in patients with stroke by transcranial direct current stimulation. Brain Stimulation, 8(3), 561-566. https://doi.org/10.1016/j.brs.2015.01.411Crajé, C., van Elk, M., Beeren, M., van Schie, H.T., Bekkering, H., Steenbergen, B., 2010. Compromised motor planning and motor imagery in right hemiparetic cerebral palsy. Research in Developmental Disabilities, 3186, 1313-1322. https://doi.org/10.1016/j.ridd.2010.07.010Iosa, M., Zocolillo, L., Montesi, M., Morelli, D., Paolucci, S., Fusco, A., 2014. The brain's sense of walking: a study on the intertwine between locomotor imagery and internal locomotor models in healthy adults, typically developing children and children with cerebral palsy. Frontiers in Human Neuroscience, 8(359), 1-9. https://doi.org/10.3389/fnhum.2014.00859Labruyère, R., Gerber, C.N., Birrer‐Brütsch, K., Meyer‐Heim, A., van Hedel, H., 2013. Requirements for and impact of a serious game for neuro‐pediatric robot‐assisted gait training. Research in Developmental Disabilities, 34, 3906-3915. https://doi.org/10.1016/j.ridd.2013.07.031Laver, K., George, S., Thomas, S., Deutsch, JE., Crotty, M., 2012. Cochrane review: virtual reality for stroke rehabilitation. European Journal of Physical and Rehabilitation Medicine, 48(3), 523-530.Lerma, S., del Castillo, M.D., Serrano, J.I., Rocon, E., Raya, R., Martínez, I., 2015. EEG control of gait in children with cerebral palsy. Preliminary data for the construction of a brain computer interface. Gait & Posture 42, S42. https://doi.org/10.1016/j.gaitpost.2015.06.082Meyer-Heim, A., van Hedel, HJA., 2013. Robot-assisted and computer-enhanced therapies for children with cerebral palsy: current state and slinical implementation. Seminars in Pediatric Neurology, 02, 139-145. https://doi.org/10.1016/j.spen.2013.06.006Mullen, T., Kothe, C., Chi, Y.M., Ojeda, A., Kerth, T., Makeig, S., Cauwenberghs, G., Jung, T.-P., 2013. Real-time modeling and 3d visualization of source dynamics and connectivity using wearable EEG. In Procceedings of IEEE EMBS, 2013, pp. 2184-2187.Mutsaarts, M., Steenbergen, B., Bekkering, H., 2007. Impaired motor imagery in right hemiparetic cerebral palsy. Experimental Brain Research, 172, 151-162. https://doi.org/10.1007/s00221-005-0327-0Niazi, I.K., Mrachacz-Kersting, N., Jiang, N., Dremstrup, K., Farina, D., 2012. Peripheral electrical stimulation triggered by self-paced detection of motor intention enhances motor evoked potentials. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 20(4), 595-604. https://doi.org/10.1109/TNSRE.2012.2194309Pfurtscheller, G., da Silva, F. H. L. 1999. Event-related EEG/EMG synchronization and desynchronization: basic principles, Clinical Neurophysiology, 110, 1842-1857. https://doi.org/10.1016/S1388-2457(99)00141-8Ridderinkhof, KR., Brass, M., 2015. How kinesthetic motor Imagery works: a predictive-processing theory of visualization in sports and motor expertise. Journal of Physiology, 109, 35-63. https://doi.org/10.1016/j.jphysparis.2015.02.003Rose, FD., Brooks, BM., Rizzo A., 2005. Virtual reality in brain damage rehabilitation: review. Cyberpsychology Behavior, 8(3), 241-62. https://doi.org/10.1089/cpb.2005.8.241Sharma, N., Baron, JC., 2013. Does motor imagery share neural networks with executed movement: a multivariate fMRI analysis. Frontiers in Human Neuroscience, 7:564. https://doi.org/10.3389/fnhum.2013.00564Shin, Y.K., Lee, D.R., Hwang, H.J., You, S.J., Im, C.H., 2012. A novel EEG-based brain mapping to determine cortical activation patterns in normal children and children with cerebral palsy during motor imagery tasks. Neurorehabilitation, 31(4), 349-355. DOI: 10.3233/NRE-2012-00803Spruijt, S., ven der Kamp, J., Steenbergen, B., 2015. Current insights in the development of children's motor imagery ability. Research in Developmental Disabilities, 34, 4154-60. https://doi.org/10.1016/j.ridd.2013.08.044Weiss, P.L., Keshner, EA., Levin, M.F. (eds.), 2014. Virtual Reality for Physical and Motor Rehabilitation, Springer. https://doi.org/10.1007/978-1-4939-0968-1Winkler, I., Haufe, S., Tangermann, M., 2011. Automatic classification of artifactual ICA-Components for artifact removal in EEG signals. Behavioral and Brain Functions, 7(30), 1-15. https://doi.org/10.1186/1744-9081-7-30You, S.H., Jang, S.H., Kim, Y.H., Hallett, M., Ahn, S.H., Kwon, Y.H., Kim, J.H, Lee, M.Y., 2005. Virtual reality-induced cortical reorganization and associated locomotor recovery in chronic stroke: an experimenter-blind randomized study. Stroke, 36(6), 1166-1171. https://doi.org/10.1161/01.STR.0000162715.43417.9

Inertial Sensing to Determine Movement Disorder Motion Present before and after Treatment

There has been a lot of interest in recent years in using inertial sensors (accelerometers and gyroscopes) to monitor movement disorder motion and monitor the efficacy of treatment options. Two of the most prominent movement disorders, which are under evaluation in this research paper, are essential tremor (ET) and Parkinson’s disease (PD). These movement disorders are first evaluated to show that ET and PD motion often depict more (tremor) motion content in the 3–12 Hz frequency band of interest than control data and that such tremor motion can be characterized using inertial sensors. As well, coherence analysis is used to compare between pairs of many of the six degrees-of-freedom of motions under evaluation, to determine the similarity in tremor motion for the various degrees-of-freedom at different frequency bands of interest. It was quite surprising that this coherence analysis depicts that there is a statistically significant relationship using coherence analysis when differentiating between control and effectively medicated PD motion. The statistical analysis uncovers the novel finding that PD medication induced dyskinesia is depicted within coherence data from inertial signals. Dyskinesia is involuntary motion or the absence of intended motion, and it is a common side effect among medicated PD patients. The results show that inertial sensors can be used to differentiate between effectively medicated PD motion and control motion; such a differentiation can often be difficult to perform with the human eye because effectively medicated PD patients tend to not produce much tremor. As well, the finding that PD motion, when well medicated, does still differ significantly from control motion allows for researchers to quantify potential deficiencies in the use of medication. By using inertial sensors to spot such deficiencies, as outlined in this research paper, it is hoped that medications with even a larger degree of efficacy can be created in the future

Can robotic-based top-down rehabilitation therapies improve motor control in children with cerebral palsy? A perspective on the CPWalker project

[EN] Cerebral Palsy (CP) is one of the most severe disabilities in childhood, and it demands important costs in health, education, and social services. CP is caused by damage to or abnormalities inside the developing brain that disrupt the brain's ability to control movement and maintain posture. Furthermore, CP is often associated with sensory deficits, cognition impairments, communication and motor disabilities, behavior issues, seizure disorder, pain, and secondary musculoskeletal problems. According to the literature, motor modules are peripheral measurements related to automatic motor control. There is a lack of evidence of change in motor modules in children with CP when different treatment approaches have been evaluated. Thus, new strategies are needed to improve motor control in this population. Robotic-based therapies are emerging as an effective intervention for gait rehabilitation in motor disorders such as stroke, spinal cord injury, and CP. There is vast clinical evidence that neural plasticity is the central core of motor recovery and development, and on-going studies suggest that robot-mediated intensive therapy could be beneficial for improved functional recovery. However, current robotic strategies are focused on the peripheral neural system (PNS) facilitating the performance of repetitive movements (a bottom-up approach). Since CP affects primarily brain structures, both the PNS and the central nervous system (CNS) should to be integrated in a physical and cognitive rehabilitation therapy (a top-down approach). This paper discusses perspectives of the top-down approach based on a novel robot-assisted rehabilitative system. Accordingly, the CPWalker robotic platform was developed to support novel therapies for CP rehabilitation. This robotic platform (Smart Walker + exoskeleton) is controlled by a multimodal interface enabling the interaction of CP infants with robot-based therapies. The aim of these therapies is to improve the physical skills of infants with CP using a top-down approach, in which motor related brain activity is used to drive robotic physical rehabilitation therapies. Our hypothesis is that the CPWalker concept will promote motor learning and this improvement will lead to significant improvements in automatic motor control.Lerma Lara, S.; Martínez Caballero, I.; Bayón, C.; Del Castillo, M.; Serrano, I.; Raya, R.; Belda Lois, JM.... (2016). Can robotic-based top-down rehabilitation therapies improve motor control in children with cerebral palsy? A perspective on the CPWalker project. Biomedical Research and Clinical Practice. 22-26. doi:10.15761/BRCP.1000106S222

A soft wearable robot for tremor assessment and suppression

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