Academic literature on the topic 'Motor learning and execution'
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Journal articles on the topic "Motor learning and execution"
Diedrichsen, Jörn, and Katja Kornysheva. "Motor skill learning between selection and execution." Trends in Cognitive Sciences 19, no. 4 (April 2015): 227–33. http://dx.doi.org/10.1016/j.tics.2015.02.003.
Full textTorriero, Sara, Massimiliano Oliveri, Giacomo Koch, Emanuele Lo Gerfo, Silvia Salerno, Fabio Ferlazzo, Carlo Caltagirone, and Laura Petrosini. "Changes in Cerebello-motor Connectivity during Procedural Learning by Actual Execution and Observation." Journal of Cognitive Neuroscience 23, no. 2 (February 2011): 338–48. http://dx.doi.org/10.1162/jocn.2010.21471.
Full textJäger, Anna-Thekla P., Julia M. Huntenburg, Stefanie A. Tremblay, Uta Schneider, Sophia Grahl, Julia Huck, Christine L. Tardif, et al. "Motor sequences; separating the sequence from the motor. A longitudinal rsfMRI study." Brain Structure and Function 227, no. 3 (October 27, 2021): 793–807. http://dx.doi.org/10.1007/s00429-021-02412-7.
Full textTorriani-Pasin, Camila, Gisele Carla dos Santos Palma, Cristiane Matsumoto Jakabi, Cinthya Walter, Andrea Michele Freudenheim, and Umberto César Correa. "Motor Learning of a cognitive-motor task after stroke." Revista Brasileira de Educação Física e Esporte 34, no. 1 (June 4, 2020): 1–9. http://dx.doi.org/10.11606/1807-5509202000010001.
Full textTorriani-Pasin, Camila, Gisele Carla dos Santos Palma, Cristiane Matsumoto Jakabi, Cinthya Walter, Andrea Michele Freudenheim, and Umberto César Correa. "Motor Learning of a cognitive-motor task after stroke." Revista Brasileira de Educação Física e Esporte 34, no. 1 (June 4, 2020): 1–9. http://dx.doi.org/10.11606/issn.1981-4690.v34i1p1-9.
Full textDomingues, Clayton Amaral, Sergio Machado, Emerson Garcia Cavaleiro, Vernon Furtado, Mauricio Cagy, Pedro Ribeiro, and Roberto Piedade. "Alpha absolute power: motor learning of practical pistol shooting." Arquivos de Neuro-Psiquiatria 66, no. 2b (June 2008): 336–40. http://dx.doi.org/10.1590/s0004-282x2008000300010.
Full textSobierajewicz, Jagna, Sylwia Szarkiewicz, Anna Przekoracka-Krawczyk, Wojciech Jaśkowski, and Rob H. J. van der Lubbe. "To What Extent Can Motor Imagery Replace Motor Execution While Learning a Fine Motor Skill?" Advances in Cognitive Psychology 12, no. 4 (December 31, 2016): 178–91. http://dx.doi.org/10.5709/acp-0197-1.
Full textStoter, Arjan J. R., Erik J. A. Scherder, Yvo P. T. Kamsma, and Theo Mulder. "Rehearsal Strategies during Motor-Sequence Learning in Old Age: Execution vs Motor Imagery." Perceptual and Motor Skills 106, no. 3 (June 2008): 967–78. http://dx.doi.org/10.2466/pms.106.3.967-978.
Full textAriani, Giacomo, and Jörn Diedrichsen. "Sequence learning is driven by improvements in motor planning." Journal of Neurophysiology 121, no. 6 (June 1, 2019): 2088–100. http://dx.doi.org/10.1152/jn.00041.2019.
Full textCho, Nam Jun, Sang Hyoung Lee, Jong Bok Kim, and Il Hong Suh. "Learning, Improving, and Generalizing Motor Skills for the Peg-in-Hole Tasks Based on Imitation Learning and Self-Learning." Applied Sciences 10, no. 8 (April 15, 2020): 2719. http://dx.doi.org/10.3390/app10082719.
Full textDissertations / Theses on the topic "Motor learning and execution"
Marchant, David Christopher. "The effects of internally and externally directed attention during motor skill execution and learning." Thesis, University of Hull, 2005. http://hydra.hull.ac.uk/resources/hull:11168.
Full textKo, Raymond. "The Role of the Basal Ganglia in Executing and Learning Complex Motor Sequences." Thesis, Harvard University, 2016. http://nrs.harvard.edu/urn-3:HUL.InstRepos:33493272.
Full textBiology, Organismic and Evolutionary
Parziale, Antonio. "A neurocomputational model of reaching movements." Doctoral thesis, Universita degli studi di Salerno, 2016. http://hdl.handle.net/10556/2341.
Full textHow the brain controls movement is a question that has fascinated researchers from different areas as neuroscience, robotics and psychology. To understand how we move is not only an intellectual challenge, but it is important for finding new strategies for nursing people with movement diseases, for rehabilitation and to develop new robotic technology. While there is an agreement about the role of the primary motor cortex (M1) in the execution of voluntary movements, it is still debated what (and how) is encoded by the neural activity of the motor cortex. To unveil the "code" used for executing voluntary movements we investigated the interaction between the motor cortex and the spinal cord, the main recipient of the descending signals departing from M1 neurons. In particular, the research presented in this thesis aims at understanding how primary motor cortex and spinal cord cooperate to execute a reaching movement, and whether a modular organization of the spinal cord can be exploited for controlling the movement. On the basis of physiological studies about the primary motor cortex organization, we have hypothesized that this brain area encodes both movement's parameters and patterns of muscle activation. We argue that the execution of voluntary movements results from the cooperation of different clusters of neurons distributed in the rostral and caudal regions of primary motor cortex, each of which represents different aspects of the ongoing movement. In particular, kinetic aspects of movement are directly represented by the caudal part of primary motor cortex as activations of alpha motoneurons, while kinematic aspects of the movement are encoded by the rostral region and are translated by spinal cord interneurons into alpha motoneurons activation. The population of corticomotoneuron (CM) cells in the caudal part of M1 creates muscle synergies for a direct control of muscle activity, useful to execute highly novel skills that require a direct control of multijoint and single joint movements by the central nervous system (CNS). On the other side, clusters of neurons in the rostral M1 are devoted to the activation of different subpopulations of interneurons in the spinal cord organized in functional modules. Each spinal module implements hardwired muscle synergies regulating the activity of a subset of muscles working around one or more joints. The way a module regulates the muscles activations is related to its structural properties. One area recruits the hard-wired motor primitives hosted in the spinal cord as spatiotemporal synergies, while the other one has direct access to the alpha motoneurons and may build new synergies for the execution of very demanding movements. The existence of these two areas regulating directly and indirectly the muscle activity can explain the controversy about what kind of parameter is encoded by the brain. In order to validate our conjecture about the coexistence of an explicit representation of both kinetic and kinematics aspects of the movement, we have developed and implemented the computational model of the spinal cord and its connections with supraspinal brain. The model incorporates the key anatomical and physiological features of the neurons in the spinal cord (interneurons Ia, Ib and PN and Renshaw cells, and their interconnections). The model envisages descending inputs coming from both rostral and caudal M1 motor cortex and cerebellum (through the rubro- and reticulo-spinal tracts), local inputs from both Golgi tendon organs and spindles, and its output is directed towards alfa motoneurons, which also receive descending inputs from the cortex and local inputs from spindles. The musculoskeletal model used in this study is a one degree-of-freedom arm whose motion is restricted to the extension/flexion of the elbow. The musculoskeletal model includes three muscles: Biceps Short, Brachialis and Triceps Lateral. Our simulations show that the CNS may produce elbow flexion movements with different properties by adopting different strategies for the recruitment and the modulation of interneurons and motoneurons. The results obtained using our computational model confirm what has been hypothesized in literature: modularity may be the organizational principle that the central nervous system exploits in motor control. In humans, the central nervous system can execute motor tasks by recruiting the motor primitives in the spinal cord or by learning new collections of synergies essential for executing novel skills typical of our society. To get more insights about how brain encodes movements and to unveil the role played by the different areas of the brain we verified if the movement generated by our model satisfied the trade-off between speed and accuracy predicted by the Fitts’ law. An interesting result is that the speed-accuracy tradeoff does not follow from the structure of the system, that is capable of performing fast and precise movements, but arises from the strategy adopted to produce faster movements, by starting from a prelearned set of motor commands useful to reach the target position and by modifying only the activations of alfa motoneurons. These results suggest that the brain may use the clusters of neurons in the rostral M1 for encoding the direction of the movement and the clusters of CM cells in the caudal M1 for regulating the tradeoff between speed and accuracy. The simulation performed with our computational model have shown that the activation of an area cannot exclude the activation of the other one but, on the contrary, both the activations are needed to have a simulated behaviour that fits the real behavior. [edited by Author]
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Pompéu, José Eduardo. "Melhora funcional de pacientes com doença de Parkinson após treinamento em ambientes real e virtual." Universidade de São Paulo, 2012. http://www.teses.usp.br/teses/disponiveis/47/47135/tde-05102012-113814/.
Full textThe objective of this work was to compare the effects of two balance training programs, one Nintendo Wii Fit-based and the other traditionally-based without the use of a gaming system, on the balance, functionality and cognition of patients with Parkinson´s disease. It was a prospective, single blinded, randomized clinical trial performed at Brazil Parkinson Association and Center of Research of the courses of Speech Therapy, Physical Therapy and Occupational Therapy of São Paulo University. 32 patients with Parkinson´s disease on stages 1 and 2,5 of Hoehn e Yahr participated of this work. Patients were randomized in control and experimental group, 16 each one. Both groups performed 14 training sessions, twice a week, for seven weeks. Each session was composed of a 30 minute-global-exercise series including stretching, muscle strengthen and axial mobility exercises. After this, both groups performed more 30 minutes of balance training: the control group performed balance exercises without external cues, visual or auditory feedbacks or cognitive stimulations; the experimental group performed the balance training with 10 Wii Fit games which stimulated motor and cognitive functions. The main outcome measures were: (1) Unified Parkinson´s Disease Rating Scale (UPDRS); (2) Berg Balance Scale (BBS); (3) Unipedal Stance Test (UST) and (4) Montreal Cognitive Assessment (MoCA). The statistical analysis was done by repeated measures ANOVA in order to assess the possible differences among the analyzed variables. Both groups showed improvement in the section II of UPDRS, BBS, UST and MoCA. Patients with Parkinson´s disease showed balance and cognitive improvement with positive repercussion on daily living activities after 14 sessions of balance training without additional advantages to the virtual training
Weinberg, Isobel Claire. "Expectation in motor planning and execution." Thesis, University College London (University of London), 2018. http://discovery.ucl.ac.uk/10049246/.
Full textKadlec, Daniel. "Motor capacity and sidestepping execution strategies in female athletes." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2022. https://ro.ecu.edu.au/theses/2536.
Full textDahlén, Olle, and Axel Rantil. "Optimized Trade Execution with Reinforcement Learning." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-150186.
Full textMorris, Nicole K. "Perception, Cognition, and Action in the Execution of a Motor Skill." Miami University Honors Theses / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=muhonors1303917744.
Full textShalabi, Kholood Matouq. "Motor learning and inter-manual transfer of motor learning after a stroke." Thesis, University of Newcastle upon Tyne, 2017. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.768491.
Full text侯江濤 and Kong-to William Hau. "Artificial neural networks, motor programs and motor learning." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1999. http://hub.hku.hk/bib/B31240227.
Full textBooks on the topic "Motor learning and execution"
Rothstein, Anne L. Motor learning. Reston, Va: American Alliance for Health, Physical Education, Recreation, and Dance, 1987.
Find full textRothstein, Anne L. Motor learning. Reston, Va: American Alliance for Health, Physical Education, Recreation, and Dance, 1987.
Find full textKober, Jens, and Jan Peters. Learning Motor Skills. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-03194-1.
Full textA, Crutchfield Carolyn, and Barnes Marylou R, eds. Motor control and motor learning in rehabilitation. Atlanta, Ga: Stokesville Pub. Co., 1993.
Find full textLatash, Mark L., and Francis Lestienne, eds. Motor Control and Learning. Boston, MA: Springer US, 2006. http://dx.doi.org/10.1007/0-387-28287-4.
Full textShea, Charles H. Motor learning and control. Boston, Mass: Allyn and Bacon, 1993.
Find full text1948-, Reid Greg, and Collier Douglas Holden 1953-, eds. Motor learning and development. Champaign, IL: Human Kinetics, 2011.
Find full textA, Wrisberg Craig, ed. Motor learning and performance. 3rd ed. Champaign, IL: Human Kinetics, 2004.
Find full textWayne, Shebilske, and Worchel Stephen, eds. Motor learning and control. Englewood Cliffs, N.J: Prentice Hall, 1993.
Find full textA, Wrisberg Craig, ed. Motor learning and performance. 2nd ed. Champaign, IL: Human Kinetics, 2000.
Find full textBook chapters on the topic "Motor learning and execution"
Delgado-García, J. M., A. Gruart, J. A. Domingo, and J. A. Trigo. "Quantal neural mechanisms underlying movement execution and motor learning." In Biological and Artificial Computation: From Neuroscience to Technology, 124–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/bfb0032470.
Full textWulf, Gabriele. "Motor Learning." In Encyclopedia of the Sciences of Learning, 2348–50. Boston, MA: Springer US, 2012. http://dx.doi.org/10.1007/978-1-4419-1428-6_869.
Full textMorgan, Michael M., MacDonald J. Christie, Thomas Steckler, Ben J. Harrison, Christos Pantelis, Christof Baltes, Thomas Mueggler, et al. "Motor Learning." In Encyclopedia of Psychopharmacology, 805. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-540-68706-1_3411.
Full textGoertzel, Ben. "Motor Learning." In The Structure of Intelligence, 141–47. New York, NY: Springer New York, 1993. http://dx.doi.org/10.1007/978-1-4612-4336-6_11.
Full textCarrière, Beate. "Motor Learning." In The Swiss Ball, 36–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-58864-8_3.
Full textBoehm, Matthias, Arun Kumar, and Jun Yang. "Execution Strategies." In Data Management in Machine Learning Systems, 53–71. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-031-01869-5_5.
Full textKokai, Yuki, Isao Nambu, and Yasuhiro Wada. "Identifying Motor Imagery-Related Electroencephalogram Features During Motor Execution." In Neural Information Processing, 90–97. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-63836-8_8.
Full textSherwood, David E. "Motor Control and Motor Learning." In Introduction to Exercise Science, 241–62. Fifth edition. | Milton Park, Abingdon, Oxon ; New York, NY :: Routledge, 2017. http://dx.doi.org/10.4324/9781315177670-10.
Full textSheridan, Martin R. "Initiation and Execution of Movement: A Unified Approach." In Tutorials in Motor Neuroscience, 313–32. Dordrecht: Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-011-3626-6_26.
Full textAnnett, John. "Motor Learning: A Review." In Motor Behavior, 189–212. Berlin, Heidelberg: Springer Berlin Heidelberg, 1985. http://dx.doi.org/10.1007/978-3-642-69749-4_6.
Full textConference papers on the topic "Motor learning and execution"
Aslan, Oğuzhan, Kurt Kağan Kurtoğlu, Kutay Yeşilalan, and Sinem Burcu Erdoğan. "Machine Learning Based Prediction of Motor Imagery and Motor Execution Tasks from Functional Near Infrared Spectroscopy Signals." In Optics and the Brain. Washington, D.C.: OSA, 2020. http://dx.doi.org/10.1364/brain.2020.bm4c.2.
Full textCorlaci, Ionut, and Mihaela Puiu. "THE WAYS FOR IMPROVING THE USE OF VISUAL FEEDBACK BY E-PROGRAMMES IN MEN'S ARTISTIC GYMNASTICS." In eLSE 2016. Carol I National Defence University Publishing House, 2016. http://dx.doi.org/10.12753/2066-026x-16-225.
Full textCasellato, Claudia, Marta Gandolla, Alessandro Crippa, and Alessandra Pedrocchi. "Robotic set-up to quantify hand-eye behavior in motor execution and learning of children with autism spectrum disorder." In 2017 International Conference on Rehabilitation Robotics (ICORR). IEEE, 2017. http://dx.doi.org/10.1109/icorr.2017.8009372.
Full textBelov, Dmitry, Samba BA, Ji Tang Liu, Anton Kolyshkin, and Sergio Daniel Rocchio. "Data-Driven PHM Solution for Health Monitoring of Mud Motor Power Sections While Drilling." In SPE Europec featured at 82nd EAGE Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/205219-ms.
Full textYadav, Singh Anurag, and Imran Muhammad Chohan. "Offset Data Analysis and Seam Less Execution Through Real Time Monitoring Results in Step Change in Drilling Performance." In SPE Europec featured at 82nd EAGE Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/205152-ms.
Full textGong, J. Q., and Bin Yao. "Indirect Neural Network Adaptive Robust Control of Linear Motor Drive System." In ASME 2002 International Mechanical Engineering Congress and Exposition. ASMEDC, 2002. http://dx.doi.org/10.1115/imece2002-33420.
Full textNguyen, Van-Hanh, Fre´de´ric Me´rienne, Jean-Luc Martinez, and Thierry Pozzo. "An Approach for Measuring the Human Gesture Learning Ability in Third-Person View Virtual Environment for Motor Rehabilitation." In ASME 2010 World Conference on Innovative Virtual Reality. ASMEDC, 2010. http://dx.doi.org/10.1115/winvr2010-3736.
Full textThomas Philip, Titto, and Sergey Ziatdinov. "Learnings from Building a Vendor Agnostic Automated Directional Drilling System." In SPE Annual Technical Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/205864-ms.
Full textGrosu, Vlad teodor, Tatiana Dobrescu, and Emilia Grosu. "GENERAL AND HAND-EYES COORDINATION IN MENTAL TRAINING OF ALPINE SKIERS." In eLSE 2016. Carol I National Defence University Publishing House, 2016. http://dx.doi.org/10.12753/2066-026x-16-231.
Full textTeodoru, Marian daniel, and Razvanliviu Petre. "THE EFFICIENT LEARNING OF STRIKES IN RELATION TO THE KARATE-DO STANCES BY MEANS OF THE PEDAR-X PLANTAR PRESSURE MEASUREMENT SYSTEM." In eLSE 2013. Carol I National Defence University Publishing House, 2013. http://dx.doi.org/10.12753/2066-026x-13-240.
Full textReports on the topic "Motor learning and execution"
Hudson, Kesha N., and Michael T. Willoughby. The Multiple Benefits of Motor Competence Skills in Early Childhood. RTI Press, August 2021. http://dx.doi.org/10.3768/rtipress.2021.rb.0027.2108.
Full textji, yuqin, hao tian, qiang ye, zhuoyan ye, and zeyu zheng. Effectiveness of exercise intervention on improving fundamental motor skills in children with autism spectrum disorder: A systematic review and Meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, December 2022. http://dx.doi.org/10.37766/inplasy2022.12.0013.
Full textThrun, Sebastian. MAPLE: Multi-Agent Planning, Learning, and Execution. Fort Belvoir, VA: Defense Technical Information Center, February 2004. http://dx.doi.org/10.21236/ada421529.
Full textZhu, Song-Chun. MATHEMATICS OF SENSING, EXPLOITATION, AND EXECUTION (MSEE) Sensing, Exploitation, and Execution (SEE) on a Foundation for Representation, Inference, and Learning. Fort Belvoir, VA: Defense Technical Information Center, July 2016. http://dx.doi.org/10.21236/ad1011558.
Full textThompson, Richard F. A Biological Neural Network Analysis of Learning and Memory: The Cerebellum and Sensory Motor Conditioning. Fort Belvoir, VA: Defense Technical Information Center, November 1995. http://dx.doi.org/10.21236/ada304568.
Full textNickerson, Jeffrey, Kalle Lyytinen, and John L. King. Automated Vehicles: A Human/Machine Co-learning Perspective. SAE International, April 2022. http://dx.doi.org/10.4271/epr2022009.
Full textJelsma, Dorothee, Reza Abdollahipour, Farhad Ghadiri, Fatemeh Alaei, Miriam Paloma Nieto, Zdenek Svoboda, Miguel Villa de Gregorio, Paola Violasdotter Nilsson, Dido Green, and Kamila Banatova. Evidence-based practice interventions for children and young people with Developmental Coordination Disorder - A scoping review. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, February 2023. http://dx.doi.org/10.37766/inplasy2023.2.0028.
Full textSAINI, RAVINDER, AbdulKhaliq Alshadid, and Lujain Aldosari. Investigation on the application of artificial intelligence in prosthodontics. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, December 2022. http://dx.doi.org/10.37766/inplasy2022.12.0096.
Full textKulhandjian, Hovannes. AI-based Pedestrian Detection and Avoidance at Night using an IR Camera, Radar, and a Video Camera. Mineta Transportation Institute, November 2022. http://dx.doi.org/10.31979/mti.2022.2127.
Full textAvellán, Leopoldo, Zulima Leal Calderon, and Giulia Lotti. Why do some Development Projects Disburse Funds Faster than Others. Inter-American Development Bank, November 2021. http://dx.doi.org/10.18235/0003839.
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