Relevant bibliographies by topics / Supervised Motor Learning

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Supervised Motor Learning'

Author: Grafiati
Published: 6 September 2023

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Journal articles on the topic "Supervised Motor Learning"

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Xian, Xiaoyu, Haichuan Tang, Yin Tian, Qi Liu, and Yuming Fan. "Performance Analysis of Different Machine Learning Algorithms for Identifying and Classifying the Failures of Traction Motors." Journal of Physics: Conference Series 2095, no. 1 (2021): 012058. http://dx.doi.org/10.1088/1742-6596/2095/1/012058.

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Abstract This paper addresses electric motor fault diagnosis using supervised machine learning classification. A total of 15 distinct fault types are classified and multilabel strategies are used to classify concurrent faults. we explored, developed, and compared the performance of different types of binary (fault/non-fault), multi-class (fault type) and multi-label (single fault versus combination fault) classifiers. To evaluate the effectiveness of fault identification and classification, we used different supervised machine learning methods, including Random forest classification, support v
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Et. al., Rameshwar D. Chintamani,. "Analysis of Motor Imagery EEG Classification Based on Feature Extraction and Machine Learning Algorithm." INFORMATION TECHNOLOGY IN INDUSTRY 9, no. 2 (2021): 541–53. http://dx.doi.org/10.17762/itii.v9i2.381.

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The brain-computer interface provides the excellent potential to address nervous system-related activity. The function of the nervous system work between internal brain control and external human body physical structure. Some parts of the human body cannot generate the signal for the processing of the human brain, cannot recognize and identify human body parts' activity—the motor imagery EEG classification approach helps resolve such types of critical illness cause of death. The dimension and structure of motor imagery-based EEG data are very high and unsupported behaviors. The machine learnin
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Singh, Puneet, Sumitash Jana, Ashitava Ghosal, and Aditya Murthy. "Exploration of joint redundancy but not task space variability facilitates supervised motor learning." Proceedings of the National Academy of Sciences 113, no. 50 (2016): 14414–19. http://dx.doi.org/10.1073/pnas.1613383113.

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The number of joints and muscles in a human arm is more than what is required for reaching to a desired point in 3D space. Although previous studies have emphasized how such redundancy and the associated flexibility may play an important role in path planning, control of noise, and optimization of motion, whether and how redundancy might promote motor learning has not been investigated. In this work, we quantify redundancy space and investigate its significance and effect on motor learning. We propose that a larger redundancy space leads to faster learning across subjects. We observed this pat
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She, Zhou, Gan, Ma, and Luo. "Decoding EEG in Motor Imagery Tasks with Graph Semi-Supervised Broad Learning." Electronics 8, no. 11 (2019): 1273. http://dx.doi.org/10.3390/electronics8111273.

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In recent years, the accurate and real-time classification of electroencephalogram (EEG) signals has drawn increasing attention in the application of brain-computer interface technology (BCI). Supervised methods used to classify EEG signals have gotten satisfactory results. However, unlabeled samples are more frequent than labeled samples, so how to simultaneously utilize limited labeled samples and many unlabeled samples becomes a research hotspot. In this paper, we propose a new graph-based semi-supervised broad learning system (GSS-BLS), which combines the graph label propagation method to
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Raymond, Jennifer L., and Javier F. Medina. "Computational Principles of Supervised Learning in the Cerebellum." Annual Review of Neuroscience 41, no. 1 (2018): 233–53. http://dx.doi.org/10.1146/annurev-neuro-080317-061948.

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Supervised learning plays a key role in the operation of many biological and artificial neural networks. Analysis of the computations underlying supervised learning is facilitated by the relatively simple and uniform architecture of the cerebellum, a brain area that supports numerous motor, sensory, and cognitive functions. We highlight recent discoveries indicating that the cerebellum implements supervised learning using the following organizational principles: ( a) extensive preprocessing of input representations (i.e., feature engineering), ( b) massively recurrent circuit architecture, ( c
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Jigyasu, R., V. Shrivastava, and S. Singh. "Prognostics and health management of induction motor by supervised learning classifiers." IOP Conference Series: Materials Science and Engineering 1168, no. 1 (2021): 012006. http://dx.doi.org/10.1088/1757-899x/1168/1/012006.

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Zhang, Weiwei, Deji Chen, and Yang Kong. "Self-Supervised Joint Learning Fault Diagnosis Method Based on Three-Channel Vibration Images." Sensors 21, no. 14 (2021): 4774. http://dx.doi.org/10.3390/s21144774.

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The accuracy of bearing fault diagnosis is of great significance for the reliable operation of rotating machinery. In recent years, increasing attention has been paid to intelligent fault diagnosis techniques based on deep learning. However, most of these methods are based on supervised learning with a large amount of labeled data, which is a challenge for industrial applications. To reduce the dependence on labeled data, a self-supervised joint learning (SSJL) fault diagnosis method based on three-channel vibration images is proposed. The method combines self-supervised learning with supervis
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Tang, Xian-Lun, Wei-Chang Ma, De-Song Kong, and Wei Li. "Semisupervised Deep Stacking Network with Adaptive Learning Rate Strategy for Motor Imagery EEG Recognition." Neural Computation 31, no. 5 (2019): 919–42. http://dx.doi.org/10.1162/neco_a_01183.

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Practical motor imagery electroencephalogram (EEG) data-based applications are limited by the waste of unlabeled samples in supervised learning and excessive time consumption in the pretraining period. A semisupervised deep stacking network with an adaptive learning rate strategy (SADSN) is proposed to solve the sample loss caused by supervised learning of EEG data and the extraction of manual features. The SADSN adopts the idea of an adaptive learning rate into a contrastive divergence (CD) algorithm to accelerate its convergence. Prior knowledge is introduced into the intermediary layer of t
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Pyle, Ryan, and Robert Rosenbaum. "A Reservoir Computing Model of Reward-Modulated Motor Learning and Automaticity." Neural Computation 31, no. 7 (2019): 1430–61. http://dx.doi.org/10.1162/neco_a_01198.

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Reservoir computing is a biologically inspired class of learning algorithms in which the intrinsic dynamics of a recurrent neural network are mined to produce target time series. Most existing reservoir computing algorithms rely on fully supervised learning rules, which require access to an exact copy of the target response, greatly reducing the utility of the system. Reinforcement learning rules have been developed for reservoir computing, but we find that they fail to converge on complex motor tasks. Current theories of biological motor learning pose that early learning is controlled by dopa
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Cingireddy, Anirudh Reddy, Robin Ghosh, Venkata Kiran Melapu, Sravanthi Joginipelli, and Tor A. Kwembe. "Classification of Parkinson's Disease Using Motor and Non-Motor Biomarkers Through Machine Learning Techniques." International Journal of Quantitative Structure-Property Relationships 7, no. 2 (2022): 1–21. http://dx.doi.org/10.4018/ijqspr.290011.

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Parkinson's disease (PD) is the second most neurodegenerative disease in the United States of America after Alzheimer's disease. The Parkinson's disease patients and scans without evidence for dopaminergic deficit (SWEDD) patients will share the same symptoms, and It's hard to differentiate the PD, SWEDD patients, and healthy controls in the progression of PD. In this research, we classified PD patients, SWEDD patients, and healthy controls by considering motor and non-motor biomarkers, namely MDS-UPDRS part 1, SCOPA score, and QUIP-RS from the PPMI database by using supervised and unsupervise
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Dissertations / Theses on the topic "Supervised Motor Learning"

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Coen, Michael Harlan. "Multimodal dynamics : self-supervised learning in perceptual and motor systems." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/34022.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Includes bibliographical references (leaves 178-192).<br>This thesis presents a self-supervised framework for perceptual and motor learning based upon correlations in different sensory modalities. The brain and cognitive sciences have gathered an enormous body of neurological and phenomenological evidence in the past half century
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De, La Bourdonnaye François. "Learning sensori-motor mappings using little knowledge : application to manipulation robotics." Thesis, Université Clermont Auvergne‎ (2017-2020), 2018. http://www.theses.fr/2018CLFAC037/document.

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La thèse consiste en l'apprentissage d'une tâche complexe de robotique de manipulation en utilisant très peu d'aprioris. Plus précisément, la tâche apprise consiste à atteindre un objet avec un robot série. L'objectif est de réaliser cet apprentissage sans paramètres de calibrage des caméras, modèles géométriques directs, descripteurs faits à la main ou des démonstrations d'expert. L'apprentissage par renforcement profond est une classe d'algorithmes particulièrement intéressante dans cette optique. En effet, l'apprentissage par renforcement permet d’apprendre une compétence sensori-motrice en
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Ahnesjö, Henrik. "Fault detection of planetary gearboxes in BLDC-motors using vibration and acoustic noise analysis." Thesis, Uppsala universitet, Institutionen för elektroteknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-425966.

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This thesis aims to use vibration and acoustic noise analysis to help a production line of a certain motor type to ensure good quality. Noise from the gearbox is sometimes present and the way it is detected is with a human listening to it. This type of error detection is subjective, and it is possible for human error to be present. Therefore, an automatic test that pass or fail the produced Brush Less Direct Current (BLDC)-motors is wanted. Two measurement setups were used. One was based on an accelerometer which was used for vibration measurements, and the other based on a microphone for acou
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Singh, Puneet. "The Role of Basal Ganglia and Redundancy in Supervised Motor Learning." Thesis, 2017. http://etd.iisc.ac.in/handle/2005/4176.

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Human sensorimotor control can achieve highly reliable movements under circumstances of noise, redundancy, uncertainty, and sensory delays. Our ability to achieve reliable and accurate movements is in the fact we have a nervous system that learns these limitations and continuously compensates for them. The purpose of the thesis is to understand brain mechanisms and computations underlying supervised motor learning, its interaction with reinforcement learning and study its relation to motor variability. To address these issues, we have investigated factors influencing supervised motor learning
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Books on the topic "Supervised Motor Learning"

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Herreros, Ivan. Learning and control. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199674923.003.0026.

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This chapter discusses basic concepts from control theory and machine learning to facilitate a formal understanding of animal learning and motor control. It first distinguishes between feedback and feed-forward control strategies, and later introduces the classification of machine learning applications into supervised, unsupervised, and reinforcement learning problems. Next, it links these concepts with their counterparts in the domain of the psychology of animal learning, highlighting the analogies between supervised learning and classical conditioning, reinforcement learning and operant cond
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Book chapters on the topic "Supervised Motor Learning"

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Wazlawick, Raul Sidnei, and Antonio Carlos da Rocha Costa. "Non-Supervised Sensory-Motor Agents Learning." In Artificial Neural Nets and Genetic Algorithms. Springer Vienna, 1995. http://dx.doi.org/10.1007/978-3-7091-7535-4_15.

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Smrkovsky, Eric, and Hubert Cecotti. "Graph-Based Semi-supervised Learning Using Riemannian Geometry Distance for Motor Imagery Classification." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-33783-3_30.

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Jana, Gopal Chandra, Shivam Shukla, Divyansh Srivastava, and Anupam Agrawal. "Performance Estimation and Analysis Over the Supervised Learning Approaches for Motor Imagery EEG Signals Classification." In Intelligent Computing and Applications. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5566-4_12.

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Premkumar, K., and B. V. Manikandan. "Online Fuzzy Supervised Learning of Radial Basis Function Neural Network Based Speed Controller for Brushless DC Motor." In Lecture Notes in Electrical Engineering. Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-2119-7_136.

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Toscano, David S., and Enrique V. Carrera. "Failure Detection in Induction Motors Using Non-supervised Machine Learning Algorithms." In Systems and Information Sciences. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59194-6_5.

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Kawato, Mitsuo. "Feedback-Error-Learning Neural Network for Supervised Motor Learning." In Advanced Neural Computers. Elsevier, 1990. http://dx.doi.org/10.1016/b978-0-444-88400-8.50047-9.

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Trappenberg, Thomas P. "Motor Control and Reinforcement Learning." In Fundamentals of Computational Neuroscience, 3rd ed. Oxford University PressOxford, 2022. http://dx.doi.org/10.1093/oso/9780192869364.003.0011.

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Abstract This chapter discusses control and decision systems that learn from general guidance in form of environmental feedback. It starts with a short overview of classical control systems that describe well basic motor control in the brain. The chapter then continues to adaptive control systems that can learn optimal actions from reward feedback. This is somewhat different to the basic supervised learning discussed previously where the teacher would specify precisely the desired response of an agent. It is also different to unsupervised learning as there is still some feedback from the environment in form of reward. The goal of a reinforcement learning agent is to figure out which actions to take in order to achieve optimal returns, often in noisy settings.
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Benarroch, Eduardo E. "Cerebellar Circuits." In Neuroscience for Clinicians, edited by Eduardo E. Benarroch. Oxford University Press, 2021. http://dx.doi.org/10.1093/med/9780190948894.003.0033.

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The cerebellum has a critical role in control of timing and coordination of movement, acquisition of skills, and cognitive and affective functions. It participates in motor control via both immediate online adjustments of motor performance and long-term adaptive motor learning, referred to as supervised or error-based learning. Most of the cerebellum is interconnected with association areas of the cerebral cortex. The cerebellum is a major target of genetic, degenerative, metabolic, and immune disorders. Experimental evidence indicates that disrupted Purkinje cell pacemaking activity and synaptic plasticity in the cerebellum have a major role in the pathophysiology of ataxia. The cerebellar circuits also have a major role in the pathophysiology of different types of tremor.
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Arbib, Michael A. "From empathy to mirror neurons and back to aesthetics." In When Brains Meet Buildings. Oxford University Press, 2021. http://dx.doi.org/10.1093/med/9780190060954.003.0005.

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This chapter approaches aesthetics anew by considering empathy and Einfühlung, “feeling ourselves into” a work of art or architecture. The key neuroscience is the discovery of mirror neurons in monkeys that inspired the discovery of mirror systems in humans. Unsupervised, supervised, and reinforcement learning, each based on a different rule for synaptic plasticity, are presented as background for a computational model of how mirror neuron wiring is learned. Mirror neurons may serve social interaction, but they also self-monitor in acquiring new behaviors. This is exemplified in modeling how adaptive sequences of behavior may be mastered through learning the desirability and executability of actions. Such opportunistic scheduling complements the role of scripts. Empathy is linked to mirror systems but also depends on systems beyond the mirror. Returning to Einfühlung, we explore how a motor component may enrich our aesthetic appreciation by recognizing the actions and emotions of protagonists in a representational painting, or by gaining some feeling for the actions of the artist, sculptor, or architect in creating the work. Finally, case studies are sampled, including those in neuroaesthetics seeking neural correlates for aesthetic appreciation, that contribute to a tool kit for assessing the experience of buildings to enrich future design.
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Conference papers on the topic "Supervised Motor Learning"

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Zaman, Shafi Md Kawsar, Xiaodong Liang, and Lihong Zhang. "Induction Motor Fault Diagnosis Using Graph-Based Semi-Supervised Learning." In 2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE). IEEE, 2020. http://dx.doi.org/10.1109/ccece47787.2020.9255706.

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Burfoot, D., and Y. Kuniyoshi. "Semi-supervised learning in a complex arm motor control task." In 2008 IEEE International Conference on Robotics and Biomimetics. IEEE, 2009. http://dx.doi.org/10.1109/robio.2009.4913257.

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Han, Jinpei, Xiao Gu, and Benny Lo. "Semi-Supervised Contrastive Learning for Generalizable Motor Imagery EEG Classification." In 2021 IEEE 17th International Conference on Wearable and Implantable Body Sensor Networks (BSN). IEEE, 2021. http://dx.doi.org/10.1109/bsn51625.2021.9507038.

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Lotey, Taveena, Prateek Keserwani, Gaurav Wasnik, and Partha Pratim Roy. "Cross-Session Motor Imagery EEG Classification using Self-Supervised Contrastive Learning." In 2022 26th International Conference on Pattern Recognition (ICPR). IEEE, 2022. http://dx.doi.org/10.1109/icpr56361.2022.9956291.

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Rovini, E., A. Moschetti, L. Fiorini, D. Esposito, C. Maremmani, and F. Cavallo. "Wearable Sensors for Prodromal Motor Assessment of Parkinson’s Disease using Supervised Learning*." In 2019 41st Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, 2019. http://dx.doi.org/10.1109/embc.2019.8856804.

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Dharani, M., and M. Sivachitra. "Motor imagery signal classification using semi supervised and unsupervised extreme learning machines." In 2017 4th International Conference on Innovations in Information, Embedded and Communication Systems. IEEE, 2017. http://dx.doi.org/10.1109/iciiecs.2017.8276131.

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Bamdadian, A., Cuntai Guan, Kai Keng Ang, and Jianxin Xu. "Online semi-supervised learning with KL distance weighting for Motor Imagery-based BCI." In 2012 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2012. http://dx.doi.org/10.1109/embc.2012.6346529.

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Kewalramani, Rohit, and A. Ram. "Estimation of Remaining Useful Life of Electric Motor using supervised deep learning methods." In 2019 IEEE Transportation Electrification Conference (ITEC-India). IEEE, 2019. http://dx.doi.org/10.1109/itec-india48457.2019.itecindia2019-197.

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Papageorgiou, T. D., W. A. Curtis, M. McHenry, and S. M. LaConte. "Neurofeedback of two motor functions using supervised learning-based real-time functional magnetic resonance imaging." In 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2009. http://dx.doi.org/10.1109/iembs.2009.5333703.

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Rajapaksha, Nipuna, Shantha Jayasinghe, Hossein Enshaei, and Nirman Jayarathne. "Supervised Machine Learning Algorithm Selection for Condition Monitoring of Induction Motors." In 2021 IEEE Southern Power Electronics Conference (SPEC). IEEE, 2021. http://dx.doi.org/10.1109/spec52827.2021.9709436.

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