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 (November 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 vector machine and neural network classification. Through experiment, we compared these methods over 4 classification regimes and finally summarize the most suitable machine learning algorithms for different aspects of health diagnosis in traction motors area.
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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 (March 26, 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 learning and another classification algorithm cannot handle these variants of EEG data directly. For the process of better classification of motor imagery, EEG needs transformation and extraction. The transform-based feature extraction process such as DCT, DWT, SFTF and some other harmonic frequency-based applied. In this paper presents the details analysis of feature extraction and classification algorithms for motor imagery EEG classification. The machine learning provides three types of an algorithm for classification, supervised, unsupervised and semi-supervised. This paper mainly focuses on the supervised machine learning algorithm. For the analysis of machine learning algorithm use BC competition-IV dataset. MATLAB software is used as a tool for the code of algorithms and measures standard parameters such as accuracy, sensitivity and specificity.
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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 (November 29, 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 pattern in subjects learning novel kinematics (visuomotor adaptation) and dynamics (force-field adaptation). Interestingly, we also observed differences in the redundancy space between the dominant hand and nondominant hand that explained differences in the learning of dynamics. Taken together, these results provide support for the hypothesis that redundancy aids in motor learning and that the redundant component of motor variability is not noise.
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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 (November 1, 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 obtain pseudo-labels and then trains the GSS-BLS classifier together with other labeled samples. Three BCI competition datasets are used to assess the GSS-BLS approach and five comparison algorithms: BLS, ELM, HELM, LapSVM and SMIR. The experimental results show that GSS-BLS achieves satisfying Cohen’s kappa values in three datasets. GSS-BLS achieves the better results of each subject in the 2-class and 4-class datasets and has significant improvements compared with original BLS except subject C6. Therefore, the proposed GSS-BLS is an effective semi-supervised algorithm for classifying EEG signals.
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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 (July 8, 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) linear input–output computations, ( d) sophisticated instructive signals that can be regulated and are predictive, ( e) adaptive mechanisms of plasticity with multiple timescales, and ( f) task-specific hardware specializations. The principles emerging from studies of the cerebellum have striking parallels with those in other brain areas and in artificial neural networks, as well as some notable differences, which can inform future research on supervised learning and inspire next-generation machine-based algorithms.
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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 (July 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 (July 13, 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 supervised learning, makes full use of unlabeled data to learn fault features, and further improves the feature recognition rate by transforming the data into three-channel vibration images. The validity of the method was verified using two typical data sets from a motor bearing. Experimental results show that this method has higher diagnostic accuracy for small quantities of labeled data and is superior to the existing methods.
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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 (May 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 the deep stacking network, and a restricted Boltzmann machine is trained by a semisupervised method in which the adjusting scope of the coefficient in learning rate is determined by performance analysis. Several EEG data sets are carried out to evaluate the performance of the proposed method. The results show that the recognition accuracy of SADSN is advanced with a more significant convergence rate and successfully classifies motor imagery.
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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 (July 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 dopamine-modulated plasticity in the basal ganglia that trains parallel cortical pathways through unsupervised plasticity as a motor task becomes well learned. We developed a novel learning algorithm for reservoir computing that models the interaction between reinforcement and unsupervised learning observed in experiments. This novel learning algorithm converges on simulated motor tasks on which previous reservoir computing algorithms fail and reproduces experimental findings that relate Parkinson's disease and its treatments to motor learning. Hence, incorporating biological theories of motor learning improves the effectiveness and biological relevance of reservoir computing models.
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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 (April 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 unsupervised machine learning algorithms, namely Knn, logistic regression, XGBooting, naive Bayes, Decision tree, Random Forest, Support vector machine, multilayer perceptron , and K-means clustering, respectively. Random Forest scored 0.98 percent accuracy among all these algorithms and can identify and differentiate PD, SWEDD, and Healthy controls patients by motor and non-motor biomarkers.
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Wang, Chiao-Sheng, I.-Hsi Kao, and Jau-Woei Perng. "Fault Diagnosis and Fault Frequency Determination of Permanent Magnet Synchronous Motor Based on Deep Learning." Sensors 21, no. 11 (May 22, 2021): 3608. http://dx.doi.org/10.3390/s21113608.
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The early diagnosis of a motor is important. Many researchers have used deep learning to diagnose motor applications. This paper proposes a one-dimensional convolutional neural network for the diagnosis of permanent magnet synchronous motors. The one-dimensional convolutional neural network model is weakly supervised and consists of multiple convolutional feature-extraction modules. Through the analysis of the torque and current signals of the motors, the motors can be diagnosed under a wide range of speeds, variable loads, and eccentricity effects. The advantage of the proposed method is that the feature-extraction modules can extract multiscale features from complex conditions. The number of training parameters was reduced so as to solve the overfitting problem. Furthermore, the class feature map was proposed to automatically determine the frequency component that contributes to the classification using the weak learning method. The experimental results reveal that the proposed model can effectively diagnose three different motor states—healthy state, demagnetization fault state, and bearing fault state. In addition, the model can detect eccentric effects. By combining the current and torque features, the classification accuracy of the proposed model is up to 98.85%, which is higher than that of classical machine-learning methods such as the k-nearest neighbor and support vector machine.
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Ou, Yanghan, Siqin Sun, Haitao Gan, Ran Zhou, and Zhi Yang. "An improved self-supervised learning for EEG classification." Mathematical Biosciences and Engineering 19, no. 7 (2022): 6907–22. http://dx.doi.org/10.3934/mbe.2022325.
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<abstract><p>Motor Imagery EEG (MI-EEG) classification plays an important role in different Brain-Computer Interface (BCI) systems. Recently, deep learning has been widely used in the MI-EEG classification tasks, however this technology requires a large number of labeled training samples which are difficult to obtain, and insufficient labeled training samples will result in a degradation of the classification performance. To address the degradation problem, we investigate a Self-Supervised Learning (SSL) based MI-EEG classification method to reduce the dependence on a large number of labeled training samples. The proposed method includes a pretext task and a downstream classification one. In the pretext task, each MI-EEG is rearranged according to the temporal characteristic. A network is pre-trained using the original and rearranged MI-EEGs. In the downstream task, a MI-EEG classification network is firstly initialized by the network learned in the pretext task and then trained using a small number of the labeled training samples. A series of experiments are conducted on Data sets 1 and 2b of BCI competition IV and IVa of BCI competition III. In the case of one third of the labeled training samples, the proposed method can obtain an obvious improvement compared to the baseline network without using SSL. In the experiments under different percentages of the labeled training samples, the results show that the designed SSL strategy is effective and beneficial to improving the classification performance.</p></abstract>
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Rovini, Erika, Carlo Maremmani, Alessandra Moschetti, Dario Esposito, and Filippo Cavallo. "Comparative Motor Pre-clinical Assessment in Parkinson’s Disease Using Supervised Machine Learning Approaches." Annals of Biomedical Engineering 46, no. 12 (July 20, 2018): 2057–68. http://dx.doi.org/10.1007/s10439-018-2104-9.
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Sharrar, Labib. "Anomaly Detection System for Stepper Motors." International Journal of Engineering Research in Electronics and Communication Engineering 9, no. 6 (June 30, 2022): 26–35. http://dx.doi.org/10.36647/ijerece/09.06.a005.
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Predictive maintenance (PdM) systems have the potential to autonomously detect underlying motor issues at early stages. Although many such systems have been proposed up to data, they have yet to be implemented. Most of these methods, which are based on supervised learning, require hours of manual data collection and annotation. Furthermore, they are mostly made to tackle a single instead of the multiple motor issues that may occur and are unable to adapt to varying motor speed and load conditions. Thus, they are not viable for industrial implementation. Therefore, this paper presents an unsupervised LSTM autoencoder-based anomaly detection system for electric motors. It analyzes the vibration and current consumption data from motors to detect anomalies, which is sufficient to account for the various motor defects. The system comes with a variety of features that allows users to autonomously collect data, train models and deploy models. In addition to that, users can remotely keep track of the motor’s conditions. To test the system, a hardware test bench using a stepper motor is made to simulate defective conditions. The LSTM Autoencoder-based anomaly detection system is described step-by-step in this paper.
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Devlaminck, Dieter, Bart Wyns, Moritz Grosse-Wentrup, Georges Otte, and Patrick Santens. "Multisubject Learning for Common Spatial Patterns in Motor-Imagery BCI." Computational Intelligence and Neuroscience 2011 (2011): 1–9. http://dx.doi.org/10.1155/2011/217987.
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Motor-imagery-based brain-computer interfaces (BCIs) commonly use the common spatial pattern filter (CSP) as preprocessing step before feature extraction and classification. The CSP method is a supervised algorithm and therefore needs subject-specific training data for calibration, which is very time consuming to collect. In order to reduce the amount of calibration data that is needed for a new subject, one can apply multitask (from now on called multisubject) machine learning techniques to the preprocessing phase. Here, the goal of multisubject learning is to learn a spatial filter for a new subject based on its own data and that of other subjects. This paper outlines the details of the multitask CSP algorithm and shows results on two data sets. In certain subjects a clear improvement can be seen, especially when the number of training trials is relatively low.
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Das, Arun, Jeffrey Mock, Yufei Huang, Edward Golob, and Peyman Najafirad. "Interpretable Self-Supervised Facial Micro-Expression Learning to Predict Cognitive State and Neurological Disorders." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 1 (May 18, 2021): 818–26. http://dx.doi.org/10.1609/aaai.v35i1.16164.
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Human behavior is the confluence of output from voluntary and involuntary motor systems. The neural activities that mediate behavior, from individual cells to distributed networks, are in a state of constant flux. Artificial intelligence (AI) research over the past decade shows that behavior, in the form of facial muscle activity, can reveal information about fleeting voluntary and involuntary motor system activity related to emotion, pain, and deception. However, the AI algorithms often lack an explanation for their decisions, and learning meaningful representations requires large datasets labeled by a subject-matter expert. Motivated by the success of using facial muscle movements to classify brain states and the importance of learning from small amounts of data, we propose an explainable self-supervised representation-learning paradigm that learns meaningful temporal facial muscle movement patterns from limited samples. We validate our methodology by carrying out comprehensive empirical study to predict future speech behavior in a real-world dataset of adults who stutter (AWS). Our explainability study found facial muscle movements around the eyes (p
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Shifat, Tanvir Alam, and Jang-Wook Hur. "EEMD assisted supervised learning for the fault diagnosis of BLDC motor using vibration signal." Journal of Mechanical Science and Technology 34, no. 10 (July 24, 2020): 3981–90. http://dx.doi.org/10.1007/s12206-020-2208-7.
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Zaki Zadeh, Mohammad, Ashwin Ramesh Babu, Ashish Jaiswal, and Fillia Makedon. "Self-Supervised Human Activity Representation for Embodied Cognition Assessment." Technologies 10, no. 1 (February 17, 2022): 33. http://dx.doi.org/10.3390/technologies10010033.
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Physical activities, according to the embodied cognition theory, are an important manifestation of cognitive functions. As a result, in this paper, the Activate Test of Embodied Cognition (ATEC) system is proposed to assess various cognitive measures. It consists of physical exercises with different variations and difficulty levels designed to provide assessment of executive and motor functions. This work focuses on obtaining human activity representation from recorded videos of ATEC tasks in order to automatically assess embodied cognition performance. A self-supervised approach is employed in this work that can exploit a small set of annotated data to obtain an effective human activity representation. The performance of different self-supervised approaches along with a supervised method are investigated for automated cognitive assessment of children performing ATEC tasks. The results show that the supervised learning approach performance decreases as the training set becomes smaller, whereas the self-supervised methods maintain their performance by taking advantage of unlabeled data.
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Qi, Yugang, Sijie Tan, Mingyang Sui, and Jianxiong Wang. "SUPERVISED PHYSICAL TRAINING IMPROVES FINE MOTOR SKILLS OF 5-YEAR-OLD CHILDREN." Revista Brasileira de Medicina do Esporte 24, no. 1 (January 2018): 9–12. http://dx.doi.org/10.1590/1517-869220182401177117.
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ABSTRACT Introduction: Fine motor skills are important for children not only in the activities of daily living, but also for learning activities. In the present study, the effects of supervised physical training were investigated in normal children. Objective: To evaluate the effects of supervised training by combining full-body exercise and the eye-hand coordination activities to improve fine motor skills in a group of five-year-old normal children. Methods: Fifty-two children were selected and randomized in exercise and control groups. The exercise group participated in three 30-minute training sessions per week for 24 weeks. Results: The fine motor skills and hand grip strength of the exercise group were significantly increased, while there was no significant change in the control group during the experimental period. Conclusion: The results indicate that the current exercise training program is effective and can be applied to 5-year-old normal children to improve their fine motor skills. In addition, this program has simple physical activities that are appropriate to the physical and mental level of child development. The 30-minute training session would be easily implemented in the kindergarten program. Level of Evidence I; High quality randomized trial with statistically significant difference or no statistically significant difference but narrow confidence intervals.
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Xu, Yilu, Hua Yin, Wenlong Yi, Xin Huang, Wenjuan Jian, Canhua Wang, and Ronghua Hu. "Supervised and Semisupervised Manifold Embedded Knowledge Transfer in Motor Imagery-Based BCI." Computational Intelligence and Neuroscience 2022 (October 17, 2022): 1–19. http://dx.doi.org/10.1155/2022/1603104.
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A long calibration procedure limits the use in practice for a motor imagery (MI)-based brain-computer interface (BCI) system. To tackle this problem, we consider supervised and semisupervised transfer learning. However, it is a challenge for them to cope with high intersession/subject variability in the MI electroencephalographic (EEG) signals. Based on the framework of unsupervised manifold embedded knowledge transfer (MEKT), we propose a supervised MEKT algorithm (sMEKT) and a semisupervised MEKT algorithm (ssMEKT), respectively. sMEKT only has limited labelled samples from a target subject and abundant labelled samples from multiple source subjects. Compared to sMEKT, ssMEKT adds comparably more unlabelled samples from the target subject. After performing Riemannian alignment (RA) and tangent space mapping (TSM), both sMEKT and ssMEKT execute domain adaptation to shorten the differences among subjects. During domain adaptation, to make use of the available samples, two algorithms preserve the source domain discriminability, and ssMEKT preserves the geometric structure embedded in the labelled and unlabelled target domains. Moreover, to obtain a subject-specific classifier, sMEKT minimizes the joint probability distribution shift between the labelled target and source domains, whereas ssMEKT performs the joint probability distribution shift minimization between the unlabelled target domain and all labelled domains. Experimental results on two publicly available MI datasets demonstrate that our algorithms outperform the six competing algorithms, where the sizes of labelled and unlabelled target domains are variable. Especially for the target subjects with 10 labelled samples and 270/190 unlabelled samples, ssMEKT shows 5.27% and 2.69% increase in average accuracy on the two abovementioned datasets compared to the previous best semisupervised transfer learning algorithm (RA-regularized common spatial patterns-weighted adaptation regularization, RA-RCSP-wAR), respectively. Therefore, our algorithms can effectively reduce the need of labelled samples for the target subject, which is of importance for the MI-based BCI application.
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Fujita, Masahiko. "New Supervised Learning Theory Applied to Cerebellar Modeling for Suppression of Variability of Saccade End Points." Neural Computation 25, no. 6 (June 2013): 1440–71. http://dx.doi.org/10.1162/neco_a_00448.
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A new supervised learning theory is proposed for a hierarchical neural network with a single hidden layer of threshold units, which can approximate any continuous transformation, and applied to a cerebellar function to suppress the end-point variability of saccades. In motor systems, feedback control can reduce noise effects if the noise is added in a pathway from a motor center to a peripheral effector; however, it cannot reduce noise effects if the noise is generated in the motor center itself: a new control scheme is necessary for such noise. The cerebellar cortex is well known as a supervised learning system, and a novel theory of cerebellar cortical function developed in this study can explain the capability of the cerebellum to feedforwardly reduce noise effects, such as end-point variability of saccades. This theory assumes that a Golgi-granule cell system can encode the strength of a mossy fiber input as the state of neuronal activity of parallel fibers. By combining these parallel fiber signals with appropriate connection weights to produce a Purkinje cell output, an arbitrary continuous input-output relationship can be obtained. By incorporating such flexible computation and learning ability in a process of saccadic gain adaptation, a new control scheme in which the cerebellar cortex feedforwardly suppresses the end-point variability when it detects a variation in saccadic commands can be devised. Computer simulation confirmed the efficiency of such learning and showed a reduction in the variability of saccadic end points, similar to results obtained from experimental data.
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Abu Al-Haija, Qasem, and Moez Krichen. "A Lightweight In-Vehicle Alcohol Detection Using Smart Sensing and Supervised Learning." Computers 11, no. 8 (August 3, 2022): 121. http://dx.doi.org/10.3390/computers11080121.
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According to the risk investigations of being involved in an accident, alcohol-impaired driving is one of the major causes of motor vehicle accidents. Preventing highly intoxicated persons from driving could potentially save many lives. This paper proposes a lightweight in-vehicle alcohol detection that processes the data generated from six alcohol sensors (MQ-3 alcohol sensors) using an optimizable shallow neural network (O-SNN). The experimental evaluation results exhibit a high-performance detection system, scoring a 99.8% detection accuracy with a very short inferencing delay of 2.22 μs. Hence, the proposed model can be efficiently deployed and used to discover in-vehicle alcohol with high accuracy and low inference overhead as a part of the driver alcohol detection system for safety (DADSS) system aiming at the massive deployment of alcohol-sensing systems that could potentially save thousands of lives annually.
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Rodrigues, Luis Guilherme Silva, Diego Roberto Colombo Dias, Marcelo De Paiva Guimarães, Alexandre Fonseca Brandão, Leonardo C. Rocha, Rogério Luiz Iope, and José Remo Ferreira Brega. "Supervised Classification of Motor-Rehabilitation Body Movements with RGB Cameras and Pose Tracking Data." Journal on Interactive Systems 13, no. 1 (September 6, 2022): 221–31. http://dx.doi.org/10.5753/jis.2022.2409.
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The technological evolution allowed the use of a single camera for precise and effective body tracking, reducing the cost and increasing the accessibility of applications in places where depth cameras and wearable sensors are not available. This paper describes and implements a supervised machine learning process consisting of a mobile application used as a motion capture device which also transforms the data into an input for a machine learning model that classifies upper and lower limbs movements (24 types of human movements). The user performs movements in front of the camera, and the trained model classifies them. We designed the system to work in a motor-rehabilitation context to assist the professional while the patient does physical exercises. The implementation can summarize the movements made during the rehabilitation sessions by counting the repetitions and classifying them when done completely or reached a specific range of motion.
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Li, Hailong, Zhiyuan Li, Kevin Du, Yu Zhu, Nehal A. Parikh, and Lili He. "A Semi-Supervised Graph Convolutional Network for Early Prediction of Motor Abnormalities in Very Preterm Infants." Diagnostics 13, no. 8 (April 21, 2023): 1508. http://dx.doi.org/10.3390/diagnostics13081508.
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Approximately 32−42% of very preterm infants develop minor motor abnormalities. Earlier diagnosis soon after birth is urgently needed because the first two years of life represent a critical window of opportunity for early neuroplasticity in infants. In this study, we developed a semi-supervised graph convolutional network (GCN) model that is able to simultaneously learn the neuroimaging features of subjects and consider the pairwise similarity between them. The semi-supervised GCN model also allows us to combine labeled data with additional unlabeled data to facilitate model training. We conducted our experiments on a multisite regional cohort of 224 preterm infants (119 labeled subjects and 105 unlabeled subjects) who were born at 32 weeks or earlier from the Cincinnati Infant Neurodevelopment Early Prediction Study. A weighted loss function was applied to mitigate the impact of an imbalanced positive:negative (~1:2) subject ratio in our cohort. With only labeled data, our GCN model achieved an accuracy of 66.4% and an AUC of 0.67 in the early prediction of motor abnormalities, outperforming prior supervised learning models. By taking advantage of additional unlabeled data, the GCN model had significantly better accuracy (68.0%, p = 0.016) and a higher AUC (0.69, p = 0.029). This pilot work suggests that the semi-supervised GCN model can be utilized to aid early prediction of neurodevelopmental deficits in preterm infants.
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Sadouk, Lamyaa, Taoufiq Gadi, and El Hassan Essoufi. "A Novel Deep Learning Approach for Recognizing Stereotypical Motor Movements within and across Subjects on the Autism Spectrum Disorder." Computational Intelligence and Neuroscience 2018 (July 10, 2018): 1–16. http://dx.doi.org/10.1155/2018/7186762.
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Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by persistent difficulties including repetitive patterns of behavior known as stereotypical motor movements (SMM). So far, several techniques have been implemented to track and identify SMMs. In this context, we propose a deep learning approach for SMM recognition, namely, convolutional neural networks (CNN) in time and frequency-domains. To solve the intrasubject SMM variability, we propose a robust CNN model for SMM detection within subjects, whose parameters are set according to a proper analysis of SMM signals, thereby outperforming state-of-the-art SMM classification works. And, to solve the intersubject variability, we propose a global, fast, and light-weight framework for SMM detection across subjects which combines a knowledge transfer technique with an SVM classifier, therefore resolving the “real-life” medical issue associated with the lack of supervised SMMs per testing subject in particular. We further show that applying transfer learning across domains instead of transfer learning within the same domain also generalizes to the SMM target domain, thus alleviating the problem of the lack of supervised SMMs in general.
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Schwarz, Andreas, Julia Brandstetter, Joana Pereira, and Gernot R. Müller-Putz. "Direct comparison of supervised and semi-supervised retraining approaches for co-adaptive BCIs." Medical & Biological Engineering & Computing 57, no. 11 (September 14, 2019): 2347–57. http://dx.doi.org/10.1007/s11517-019-02047-1.
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Abstract For Brain-Computer interfaces (BCIs), system calibration is a lengthy but necessary process for successful operation. Co-adaptive BCIs aim to shorten training and imply positive motivation to users by presenting feedback already at early stages: After just 5 min of gathering calibration data, the systems are able to provide feedback and engage users in a mutual learning process. In this work, we investigate whether the retraining stage of co-adaptive BCIs can be adapted to a semi-supervised concept, where only a small amount of labeled data is available and all additional data needs to be labeled by the BCI itself. The aim of the current work was to evaluate whether a semi-supervised co-adaptive BCI could successfully compete with a supervised co-adaptive BCI model. In a supporting two-class (190 trials per condition) BCI study based on motor imagery tasks, we evaluated both approaches in two separate groups of 10 participants online, while we simulated the other approach in each group offline. Our results indicate that despite the lack of true labeled data, the semi-supervised driven BCI did not perform significantly worse (p > 0.05) than the supervised counterpart. We believe that these findings contribute to developing BCIs for long-term use, where continuous adaptation becomes imperative for maintaining meaningful BCI performance.
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Lakshmi Praveena, T., and N. V. Muthu Lakshmi. "Prediction of Autism Spectrum Disorder Using Supervised Machine Learning Algorithms." Asian Journal of Computer Science and Technology 8, no. 3 (November 15, 2019): 15–18. http://dx.doi.org/10.51983/ajcst-2019.8.3.2734.
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Autism appears to be a neuro developmental disorder that is visible in the early years. It is a wide-spectrum disorder that indicates that the severity and symptoms can vary from person to person. The Centre for Disease Control found that one in 68 was diagnosed with autism spectrum disorder with increasing numbers in every year. Detection of autism in adults is a cumbersome procedure because in adults, many symptoms can blend with some other mental health, motor impairment disorders so misinterpretation of actual diseases can in turn lead to a terrible life without proper diagnosis and effective treatment mechanisms. Machine learning is a powerful computer tool that supports different application domains Learning complex relationships or patterns from large datasets to draw accurate conclusions. Disease assessment can be done with predictive health data analysis and more appropriate treatment mechanisms that are now a hot area of research. Supervised learning is an important step of Machine learning which uses a rule-based approach by examining empirical data sets to build accurate predictive models. In this paper, decision tree, random forest, SVM, neural networks algorithms are applied on autism spectrum data which have been collected from UCI repository. The results of decision tree, random forest, SVM, neural networks algorithms on autism dataset are presented in this paper in an efficient manner. Analysis performed over these accurate results which will be useful to make right decisions in predicting autism spectrum disorder (ASD) at early stages. Thus, early autism intervention using machine learning techniques opens up a new way for autistic individuals to develop the potential to lead a better life by improving their behavioural and emotional skills.
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Chen, Junjian, Zhuliang Yu, and Zhenghui Gu. "Semi-supervised Deep Learning in Motor Imagery-Based Brain-Computer Interfaces with Stacked Variational Autoencoder." Journal of Physics: Conference Series 1631 (September 2020): 012007. http://dx.doi.org/10.1088/1742-6596/1631/1/012007.
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She, Qingshan, Jie Zou, Zhizeng Luo, Thinh Nguyen, Rihui Li, and Yingchun Zhang. "Multi-class motor imagery EEG classification using collaborative representation-based semi-supervised extreme learning machine." Medical & Biological Engineering & Computing 58, no. 9 (July 16, 2020): 2119–30. http://dx.doi.org/10.1007/s11517-020-02227-4.
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Ling, Xufeng, Yapeng Wu, Rahman Ali, and Huaizhong Zhu. "Magnetic Tile Surface Defect Detection Methodology Based on Self-Attention and Self-Supervised Learning." Computational Intelligence and Neuroscience 2022 (August 3, 2022): 1–10. http://dx.doi.org/10.1155/2022/3003810.
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As the core component of permanent magnet motor, the magnetic tile defects seriously affect the quality of industrial motor. Automatic recognition of the surface defects of the magnetic tile is a difficult job since the patterns of the defects are complex and diverse. The existing defect recognition methods result in difficulty in practical application due to the complicated system structure and the low accuracy of the image segmentation and the target detection for the diversity of the defect patterns. A self-supervised learning (SSL) method, which benefits from its nonlinear feature extraction performance, is proposed in this study to improve the existing approaches. We proposed an efficient multihead self-attention method, which can automatically locate single or multiple defect areas of magnetic tile and extract features of the magnetic tile defects. We also designed an accurate full-connection classifier, which can accurately classify different defects of magnetic tile defects. A knowledge distillation process without labeling is proposed, which simplifies the self-supervised training process. The process of our method is as follows. A feature extraction model consists of standard vision transformer (ViT) backbone, which is trained by contrast learning without labeled dataset that is used to extract global and local features from the input magnetic tile images. Then, we use a full-connection neural network, which is trained by using labeled dataset to classify the known defect types. Finally, we combined the feature extraction model and defect classification model together to form a relatively simple integrated system. The public magnetic tile surface defect dataset, which holds 5 defect categories and 1 nondefect category, is used in the process of training, validating, and testing. We also use online data augmentation techs to increase training samples to make the model converge and achieve high classification accuracy. The experimental results show that the features extracted by the SSL method can get richer and more detailed features than the supervised learning model gets. The composite model reaches to a high testing accuracy of 98.3%, and gains relatively strong robustness and good generalization ability.
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Saxena, Abhinav, Rajat Kumar, Arun Kumar Rawat, Mohd Majid, Jay Singh, S. Devakirubakaran, and Gyanendra Kumar Singh. "Abnormal Health Monitoring and Assessment of a Three-Phase Induction Motor Using a Supervised CNN-RNN-Based Machine Learning Algorithm." Mathematical Problems in Engineering 2023 (January 30, 2023): 1–8. http://dx.doi.org/10.1155/2023/1264345.
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This paper shows the health monitoring and assessment of a three-phase induction motor in abnormal conditions using a machine learning algorithm. The convolutional neural network (CNN) and recurrent neural network (RNN) algorithms are the prominent methods used in machine learning algorithms, and the combined method is known as the CRNN method. The abnormal conditions of a three phase-induction motor are represented by three-phase faults, line-to-ground faults, etc. The pattern of fault current is traced, and key features are extracted by the CRNN algorithm. The performance parameters like THD (%), accuracy, and reliability of abnormal conditions are measured with the CRNN algorithm. The assessment of abnormal conditions is being realized at the terminals of a three-phase induction motor. A fuzzy logic controller (FLC) is also used to assess such abnormalities. It is observed that performance parameters are found to be better with the CRNN method in comparison to CNN, RNN, ANN, and other methods. Such a realization makes the system more compatible with abnormality recognition.
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Zhao, Xianghong, Jieyu Zhao, Weiming Cai, and Shuangqing Wu. "Transferring Common Spatial Filters With Semi-Supervised Learning for Zero-Training Motor Imagery Brain-Computer Interface." IEEE Access 7 (2019): 58120–30. http://dx.doi.org/10.1109/access.2019.2913154.
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Wang, Fang, Kai Xu, Qiao Sheng Zhang, Yi Wen Wang, and Xiao Xiang Zheng. "A Multi-Step Neural Control for Motor Brain-Machine Interface by Reinforcement Learning." Applied Mechanics and Materials 461 (November 2013): 565–69. http://dx.doi.org/10.4028/www.scientific.net/amm.461.565.
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Brain-machine interfaces (BMIs) decode cortical neural spikes of paralyzed patients to control external devices for the purpose of movement restoration. Neuroplasticity induced by conducting a relatively complex task within multistep, is helpful to performance improvements of BMI system. Reinforcement learning (RL) allows the BMI system to interact with the environment to learn the task adaptively without a teacher signal, which is more appropriate to the case for paralyzed patients. In this work, we proposed to apply Q(λ)-learning to multistep goal-directed tasks using users neural activity. Neural data were recorded from M1 of a monkey manipulating a joystick in a center-out task. Compared with a supervised learning approach, significant BMI control was achieved with correct directional decoding in 84.2% and 81% of the trials from naïve states. The results demonstrate that the BMI system was able to complete a task by interacting with the environment, indicating that RL-based methods have the potential to develop more natural BMI systems.
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Ghorbani, Saeed, Amir Dana, and Zynalabedin Fallah. "The effects of external and internal focus of attention on motor learning and promoting learner’s focus." Biomedical Human Kinetics 11, no. 1 (January 1, 2019): 175–80. http://dx.doi.org/10.2478/bhk-2019-0024.
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SummaryStudy aim: External focus of attention is considered as a critical factor in the OPTIMAL theory of motor learning. This theory proposes that external focus of attention facilitates motor performance and learning because it promotes focusing on the task goal. However, the effects of external focus of attention on focusing on the task goal are not well understood. The aim of this study was, therefore, to investigate the effects of an external focus of attention versus an internal focus of attention on motor learning and promoting focus of the learner on the task goal.Material and methods: Thirty-six right-handed male students (mean age 21.16 ± 1.85 years old) with no prior experiences with the motor task were randomly assigned to three groups: external focus, internal focus, and control groups. Participants were asked to throw darts at a target during an acquisition phase (10 blocks of six trials each) and during subsequent retention and transfer tests. Throwing accuracy and focus on the task goal were measured as dependent variables. Analysis of variance (ANOVA) with repeated measures as well as a one-way ANOVA was used to analyze the differences in accuracy scores between groups during the acquisition phase as well as retention and transfer tests, respectively. The significance level was set at p < .05. The author supervised all phases of the experiment.Results: The results showed that adopting an external focus promoted a focus on the task goal and resulted in significantly better motor learning than adopting an internal focus and control conditions (p < 0.05).Conclusions: The findings of the present study provided support for the propositions of the OPTIMAL theory and showed that adopting an external focus of attention promotes focus of the learner on the task goal. The results are discussed in terms of benefits of external focus instructions for facilitating motor learning and goal-action coupling.
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Lee, Seyoung, Jiye Lee, and Jehee Lee. "Learning Virtual Chimeras by Dynamic Motion Reassembly." ACM Transactions on Graphics 41, no. 6 (November 30, 2022): 1–13. http://dx.doi.org/10.1145/3550454.3555489.
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The Chimera is a mythological hybrid creature composed of different animal parts. The chimera's movements are highly dependent on the spatial and temporal alignments of its composing parts. In this paper, we present a novel algorithm that creates and animates chimeras by dynamically reassembling source characters and their movements. Our algorithm exploits a two-network architecture: part assembler and dynamic controller. The part assembler is a supervised learning layer that searches for the spatial alignment among body parts, assuming that the temporal alignment is provided. The dynamic controller is a reinforcement learning layer that learns robust control policy for a wide variety of potential temporal alignments. These two layers are tightly intertwined and learned simultaneously. The chimera animation generated by our algorithm is energy efficient and expressive in terms of describing weight shifting, balancing, and full-body coordination. We demonstrate the versatility of our algorithm by generating the motor skills of a large variety of chimeras from limited source characters.
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Liu, Minjie, Mingming Zhou, Tao Zhang, and Naixue Xiong. "Semi-supervised learning quantization algorithm with deep features for motor imagery EEG Recognition in smart healthcare application." Applied Soft Computing 89 (April 2020): 106071. http://dx.doi.org/10.1016/j.asoc.2020.106071.
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Altaf, Saud, Muhammad Waseem Soomro, and Mirza Sajid Mehmood. "Fault Diagnosis and Detection in Industrial Motor Network Environment Using Knowledge-Level Modelling Technique." Modelling and Simulation in Engineering 2017 (2017): 1–10. http://dx.doi.org/10.1155/2017/1292190.
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In this paper, broken rotor bar (BRB) fault is investigated by utilizing the Motor Current Signature Analysis (MCSA) method. In industrial environment, induction motor is very symmetrical, and it may have obvious electrical signal components at different fault frequencies due to their manufacturing errors, inappropriate motor installation, and other influencing factors. The misalignment experiments revealed that improper motor installation could lead to an unexpected frequency peak, which will affect the motor fault diagnosis process. Furthermore, manufacturing and operating noisy environment could also disturb the motor fault diagnosis process. This paper presents efficient supervised Artificial Neural Network (ANN) learning technique that is able to identify fault type when situation of diagnosis is uncertain. Significant features are taken out from the electric current which are based on the different frequency points and associated amplitude values with fault type. The simulation results showed that the proposed technique was able to diagnose the target fault type. The ANN architecture worked well with selecting of significant number of feature data sets. It seemed that, to the results, accuracy in fault detection with features vector has been achieved through classification performance and confusion error percentage is acceptable between healthy and faulty condition of motor.
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Redkar, Sangram. "Using Deep Learning for Human Computer Interface via Electroencephalography." IAES International Journal of Robotics and Automation (IJRA) 4, no. 4 (December 1, 2015): 292. http://dx.doi.org/10.11591/ijra.v4i4.pp292-310.
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<table class="Heading1Char" width="593" border="0" cellspacing="0" cellpadding="0"><tbody><tr><td valign="top" width="387"><p>In this paper, several techniques used to perform EEG signal pre-processing, feature extraction and signal classification have been discussed, implemented, validated and verified; efficient supervised and unsupervised machine learning models, for the EEG motor imagery classification are identified. Brain Computer Interfaces are becoming the next generation controllers not only in the medical devices for disabled individuals but also in the gaming and entertainment industries. In order to build an effective Brain Computer Interface, it is important to have robust signal processing and machine learning modules which operate on the EEG signals and estimate the current thought or intent of the user. Motor Imagery (imaginary hand and leg movements) signals are acquired using the Emotiv EEG headset. The signal have been extracted and supplied to the machine learning (ML) stage, wherein, several ML techniques are applied and validated. The performances of various ML techniques are compared and some important observations are reported. Further, Deep Learning techniques like autoencoding have been used to perform unsupervised feature learning. The reliability of the features is presented and analyzed by performing classification by using the ML techniques. It is shown that hand engineered ‘ad-hoc’ feature extraction techniques are less reliable than the automated (‘Deep Learning’) feature learning techniques. All the findings in this research, can be used by the BCI research community for building motor imagery based BCI applications such as Gaming, Robot control and autonomous vehicles.</p></td></tr></tbody></table>
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Cardenas, Javier A., Uriel E. Carrero, Edgar C. Camacho, and Juan M. Calderon. "Intelligent Position Controller for Unmanned Aerial Vehicles (UAV) Based on Supervised Deep Learning." Machines 11, no. 6 (June 2, 2023): 606. http://dx.doi.org/10.3390/machines11060606.
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In recent years, multi-rotor UAVs have become valuable tools in several productive fields, from entertainment to agriculture and security. However, during their flight trajectory, they sometimes do not accurately perform a specific set of tasks, and the implementation of flight controllers in these vehicles is required to achieve a successful performance. Therefore, this research describes the design of a flight position controller based on Deep Neural Networks and subsequent implementation for a multi-rotor UAV. Five promising Neural Network architectures are developed based on a thorough literature review, incorporating LSTM, 1-D convolutional, pooling, and fully-connected layers. A dataset is then constructed using the performance data of a PID flight controller, encompassing diverse trajectories with transient and steady-state information such as position, speed, acceleration, and motor output signals. The tuning of hyperparameters for each type of architecture is performed by applying the Hyperband algorithm. The best model obtained (LSTMCNN) consists of a combination of LSTM and CNN layers in one dimension. This architecture is compared with the PID flight controller in different scenarios employing evaluation metrics such as rise time, overshoot, steady-state error, and control effort. The findings reveal that our best models demonstrate the successful generalization of flight control tasks. While our best model is able to work with a wider operational range than the PID controller and offers step responses in the Y and X axis with 97% and 98% similarity, respectively, within the PID’s operational range. This outcome opens up possibilities for efficient online training of flight controllers based on Neural Networks, enabling the development of adaptable controllers tailored to specific application domains.
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Aich, Satyabrata, Jinyoung Youn, Sabyasachi Chakraborty, Pyari Mohan Pradhan, Jin-han Park, Seongho Park, and Jinse Park. "A Supervised Machine Learning Approach to Detect the On/Off State in Parkinson’s Disease Using Wearable Based Gait Signals." Diagnostics 10, no. 6 (June 20, 2020): 421. http://dx.doi.org/10.3390/diagnostics10060421.
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Fluctuations in motor symptoms are mostly observed in Parkinson’s disease (PD) patients. This characteristic is inevitable, and can affect the quality of life of the patients. However, it is difficult to collect precise data on the fluctuation characteristics using self-reported data from PD patients. Therefore, it is necessary to develop a suitable technology that can detect the medication state, also termed the “On”/“Off” state, automatically using wearable devices; at the same time, this could be used in the home environment. Recently, wearable devices, in combination with powerful machine learning techniques, have shown the potential to be effectively used in critical healthcare applications. In this study, an algorithm is proposed that can detect the medication state automatically using wearable gait signals. A combination of features that include statistical features and spatiotemporal gait features are used as inputs to four different classifiers such as random forest, support vector machine, K nearest neighbour, and Naïve Bayes. In total, 20 PD subjects with definite motor fluctuations have been evaluated by comparing the performance of the proposed algorithm in association with the four aforementioned classifiers. It was found that random forest outperformed the other classifiers with an accuracy of 96.72%, a recall of 97.35%, and a precision of 96.92%.
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E., Sica, Savarese G., Criscitiello G., and Grano R. "The “School in the Green”: An Experience of Developing Scholastic Intelligence Through the Enhancement of the Naturalistic and Visual-Spatial Ones." British Journal of Education, Learning and Development Psychology 6, no. 3 (August 14, 2023): 1–6. http://dx.doi.org/10.52589/bjeldp-sqmy0hxc.
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The "Cipolletti" Institute has launched, from the 2020-21 school year to today, in the summer months, the experience of the "School in the Green", located in a wood in the town of Banzano di Montoro (AV- Italy). It is a space equipped for the safety of kindergarten and primary school children, in which motor, artistic, and laboratory activities are carried out in English since the Institute is a bilingual school. The school, which has been using the "Embodied cognition" model for years, has intended to integrate "Embodied" learning, precisely "Embodied", with outdoor teaching, trying to develop, in parallel with scholastic intelligence, also naturalistic intelligence, and visual-spatial. Among the scientific assumptions is that of Waldpädagogik, of experiential pedagogy that values discovery learning. The project of the "School in the Green" was supervised by the Chair of Developmental and Educational Psychology of the Department of Medicine, Surgery, Dentistry "Scuola Medica Salernitana" of the University of Salerno (Italy), and the same Department sponsored the project. The activities carried out were: Study science directly through sensory experience in nature; Music and movement workshop with Orff instruments; Sensory and musical journey with handcrafted instruments; Motor paths and motor coordination to develop naturalistic intelligence and visual-spatial intelligence; Motor-sport activity in the greenery; Immersive learning of the English language; Peer tutoring for learning English; Small group activity in Spanish; Learning of less widespread languages with the 3D construction of morpho-phonemes (for example Korean, Portuguese); Theatrical activities in nature; Garden care; Workshop for flower arrangements; Outdoor cooking workshop¸local field trips. This short article reports the theoretical construct of reference and research to evaluate whether school learning activities benefit from being implemented in a naturalistic context.
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Brons, Annette, Antoine de Schipper, Svetlana Mironcika, Huub Toussaint, Ben Schouten, Sander Bakkes, and Ben Kröse. "Assessing Children’s Fine Motor Skills With Sensor-Augmented Toys: Machine Learning Approach." Journal of Medical Internet Research 23, no. 4 (April 22, 2021): e24237. http://dx.doi.org/10.2196/24237.
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Background Approximately 5%-10% of elementary school children show delayed development of fine motor skills. To address these problems, detection is required. Current assessment tools are time-consuming, require a trained supervisor, and are not motivating for children. Sensor-augmented toys and machine learning have been presented as possible solutions to address this problem. Objective This study examines whether sensor-augmented toys can be used to assess children’s fine motor skills. The objectives were to (1) predict the outcome of the fine motor skill part of the Movement Assessment Battery for Children Second Edition (fine MABC-2) and (2) study the influence of the classification model, game, type of data, and level of difficulty of the game on the prediction. Methods Children in elementary school (n=95, age 7.8 [SD 0.7] years) performed the fine MABC-2 and played 2 games with a sensor-augmented toy called “Futuro Cube.” The game “roadrunner” focused on speed while the game “maze” focused on precision. Each game had several levels of difficulty. While playing, both sensor and game data were collected. Four supervised machine learning classifiers were trained with these data to predict the fine MABC-2 outcome: k-nearest neighbor (KNN), logistic regression (LR), decision tree (DT), and support vector machine (SVM). First, we compared the performances of the games and classifiers. Subsequently, we compared the levels of difficulty and types of data for the classifier and game that performed best on accuracy and F1 score. For all statistical tests, we used α=.05. Results The highest achieved mean accuracy (0.76) was achieved with the DT classifier that was trained on both sensor and game data obtained from playing the easiest and the hardest level of the roadrunner game. Significant differences in performance were found in the accuracy scores between data obtained from the roadrunner and maze games (DT, P=.03; KNN, P=.01; LR, P=.02; SVM, P=.04). No significant differences in performance were found in the accuracy scores between the best performing classifier and the other 3 classifiers for both the roadrunner game (DT vs KNN, P=.42; DT vs LR, P=.35; DT vs SVM, P=.08) and the maze game (DT vs KNN, P=.15; DT vs LR, P=.62; DT vs SVM, P=.26). The accuracy of only the best performing level of difficulty (combination of the easiest and hardest level) achieved with the DT classifier trained with sensor and game data obtained from the roadrunner game was significantly better than the combination of the easiest and middle level (P=.046). Conclusions The results of our study show that sensor-augmented toys can efficiently predict the fine MABC-2 scores for children in elementary school. Selecting the game type (focusing on speed or precision) and data type (sensor or game data) is more important for determining the performance than selecting the machine learning classifier or level of difficulty.
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Anitha Kumari, K., Avinash Sharma, S. Nivethitha, V. Dharini, V. Sanjith, R. Vaishnavi, G. Jothika, and K. Shophiya. "Automated Outlier Detection for Electrical Motors and Transformers." Journal of Computational and Theoretical Nanoscience 17, no. 9 (July 1, 2020): 4703–8. http://dx.doi.org/10.1166/jctn.2020.9304.
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Electrical appliances most commonly consist of two electrical devices, namely, electrical motors and transformers. Typically, electrical motors are normally used in all sort of industrial purposes. Failures of such motors results in serious problems, such as overheat, shut down and even burnt, in their host systems. Thus, more attention have to be paid in detecting the outliers. In a similar way, to avoid the unexpected power reliability problems and system damages, the prediction of the failures in the transformers is expected to quantify the impacts. By predicting the failures, the lifetime of the transformers increases and unnecessary accidents is avoided. Therefore, this paper presents the detection of the outliers in electrical motors and failures in transformers using supervised machine learning algorithms. Machine learning techniques such as Support Vector Machine (SVM), Random Forest (RF) and regression techniques like Support Vector Regression (SVR), Polynomial Regression (PR) are used to analyze the use cases of different motor specifications. Evaluation and the efficiency of findings are proved by considering accuracy, precision, F-measure, and recall for motors. Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Square Error (RMSE) and R-squared Error (R2) are considered as metrics for transformers. The proposed approach helps to identify the anomalies like vibration loss, copper loss and overheating in the industrial motor and to determine the abnormal functioning of the transformer that in turn leads to ascertain the lifetime. The proposed system analyses the behaviour of the electrical machines using the energy meter data and reports the outliers to users. It also analyses the abnormalities occurring in the transformer using the parameters involved in the degradation of the paper-oil insulation system and the voltage of operation as a whole leads to the predict the lifetime.
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Sammut, Stephen, Ryan G. L. Koh, and José Zariffa. "Compensation Strategies for Bioelectric Signal Changes in Chronic Selective Nerve Cuff Recordings: A Simulation Study." Sensors 21, no. 2 (January 12, 2021): 506. http://dx.doi.org/10.3390/s21020506.
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Peripheral nerve interfaces (PNIs) allow us to extract motor, sensory, and autonomic information from the nervous system and use it as control signals in neuroprosthetic and neuromodulation applications. Recent efforts have aimed to improve the recording selectivity of PNIs, including by using spatiotemporal patterns from multi-contact nerve cuff electrodes as input to a convolutional neural network (CNN). Before such a methodology can be translated to humans, its performance in chronic implantation scenarios must be evaluated. In this simulation study, approaches were evaluated for maintaining selective recording performance in the presence of two chronic implantation challenges: the growth of encapsulation tissue and rotation of the nerve cuff electrode. Performance over time was examined in three conditions: training the CNN at baseline only, supervised re-training with explicitly labeled data at periodic intervals, and a semi-supervised self-learning approach. This study demonstrated that a selective recording algorithm trained at baseline will likely fail over time due to changes in signal characteristics resulting from the chronic challenges. Results further showed that periodically recalibrating the selective recording algorithm could maintain its performance over time, and that a self-learning approach has the potential to reduce the frequency of recalibration.
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WENG, JUYANG, TIANYU LUWANG, HONG LU, and XIANGYANG XUE. "A MULTILAYER IN-PLACE LEARNING NETWORK FOR DEVELOPMENT OF GENERAL INVARIANCES." International Journal of Humanoid Robotics 04, no. 02 (June 2007): 281–320. http://dx.doi.org/10.1142/s0219843607001072.
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Currently, there is a lack of general-purpose, in-place learning engines that incrementally learn multiple tasks, to develop "soft" multi-task-shared invariances in the intermediate internal representation while a developmental robot interacts with its environment. In-place learning is a biologically inspired concept, rooted in the genomic equivalence principle, meaning that each neuron is responsible for its own development while interacting with its environment. With in-place learning, there is no need for a separate learning network. Computationally, biologically inspired, in-place learning provides unusually efficient learning algorithms whose simplicity, low computational complexity, and generality are set apart from typical conventional learning algorithms. We present in this paper the multiple-layer in-place learning network (MILN) for this ambitious goal. As a key requirement for autonomous mental development, the network enables both unsupervised and supervised learning to occur concurrently, depending on whether motor supervision signals are available or not at the motor end (the last layer) during the agent's interactions with the environment. We present principles based on which MILN automatically develops invariant neurons in different layers and why such invariant neuronal clusters are important for learning later tasks in open-ended development. From sequentially sensed sensory streams, the proposed MILN incrementally develops a hierarchy of internal representations. The global invariance achieved through multi-layer invariances, with increasing invariance from early layers to the later layers. Experimental results with statistical performance measures are presented to show the effects of the principles.
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Baker, Sunderland, Anand Tekriwal, Gidon Felsen, Elijah Christensen, Lisa Hirt, Steven G. Ojemann, Daniel R. Kramer, Drew S. Kern, and John A. Thompson. "Automatic extraction of upper-limb kinematic activity using deep learning-based markerless tracking during deep brain stimulation implantation for Parkinson’s disease: A proof of concept study." PLOS ONE 17, no. 10 (October 20, 2022): e0275490. http://dx.doi.org/10.1371/journal.pone.0275490.
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Optimal placement of deep brain stimulation (DBS) therapy for treating movement disorders routinely relies on intraoperative motor testing for target determination. However, in current practice, motor testing relies on subjective interpretation and correlation of motor and neural information. Recent advances in computer vision could improve assessment accuracy. We describe our application of deep learning-based computer vision to conduct markerless tracking for measuring motor behaviors of patients undergoing DBS surgery for the treatment of Parkinson’s disease. Video recordings were acquired during intraoperative kinematic testing (N = 5 patients), as part of standard of care for accurate implantation of the DBS electrode. Kinematic data were extracted from videos post-hoc using the Python-based computer vision suite DeepLabCut. Both manual and automated (80.00% accuracy) approaches were used to extract kinematic episodes from threshold derived kinematic fluctuations. Active motor epochs were compressed by modeling upper limb deflections with a parabolic fit. A semi-supervised classification model, support vector machine (SVM), trained on the parameters defined by the parabolic fit reliably predicted movement type. Across all cases, tracking was well calibrated (i.e., reprojection pixel errors 0.016–0.041; accuracies >95%). SVM predicted classification demonstrated high accuracy (85.70%) including for two common upper limb movements, arm chain pulls (92.30%) and hand clenches (76.20%), with accuracy validated using a leave-one-out process for each patient. These results demonstrate successful capture and categorization of motor behaviors critical for assessing the optimal brain target for DBS surgery. Conventional motor testing procedures have proven informative and contributory to targeting but have largely remained subjective and inaccessible to non-Western and rural DBS centers with limited resources. This approach could automate the process and improve accuracy for neuro-motor mapping, to improve surgical targeting, optimize DBS therapy, provide accessible avenues for neuro-motor mapping and DBS implantation, and advance our understanding of the function of different brain areas.
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Dai, Mengxi, Dezhi Zheng, Shucong Liu, and Pengju Zhang. "Transfer Kernel Common Spatial Patterns for Motor Imagery Brain-Computer Interface Classification." Computational and Mathematical Methods in Medicine 2018 (2018): 1–9. http://dx.doi.org/10.1155/2018/9871603.
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Motor-imagery-based brain-computer interfaces (BCIs) commonly use the common spatial pattern (CSP) as preprocessing step before classification. The CSP method is a supervised algorithm. Therefore a lot of time-consuming training data is needed to build the model. To address this issue, one promising approach is transfer learning, which generalizes a learning model can extract discriminative information from other subjects for target classification task. To this end, we propose a transfer kernel CSP (TKCSP) approach to learn a domain-invariant kernel by directly matching distributions of source subjects and target subjects. The dataset IVa of BCI Competition III is used to demonstrate the validity by our proposed methods. In the experiment, we compare the classification performance of the TKCSP against CSP, CSP for subject-to-subject transfer (CSP SJ-to-SJ), regularizing CSP (RCSP), stationary subspace CSP (ssCSP), multitask CSP (mtCSP), and the combined mtCSP and ssCSP (ss + mtCSP) method. The results indicate that the superior mean classification performance of TKCSP can achieve 81.14%, especially in case of source subjects with fewer number of training samples. Comprehensive experimental evidence on the dataset verifies the effectiveness and efficiency of the proposed TKCSP approach over several state-of-the-art methods.
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Anastasiev, Alexey, Hideki Kadone, Aiki Marushima, Hiroki Watanabe, Alexander Zaboronok, Shinya Watanabe, Akira Matsumura, Kenji Suzuki, Yuji Matsumaru, and Eiichi Ishikawa. "Supervised Myoelectrical Hand Gesture Recognition in Post-Acute Stroke Patients with Upper Limb Paresis on Affected and Non-Affected Sides." Sensors 22, no. 22 (November 11, 2022): 8733. http://dx.doi.org/10.3390/s22228733.
Full textAbstract:
In clinical practice, acute post-stroke paresis of the extremities fundamentally complicates timely rehabilitation of motor functions; however, recently, residual and distorted musculoskeletal signals have been used to initiate feedback-driven solutions for establishing motor rehabilitation. Here, we investigate the possibilities of basic hand gesture recognition in acute stroke patients with hand paresis using a novel, acute stroke, four-component multidomain feature set (ASF-4) with feature vector weight additions (ASF-14NP, ASF-24P) and supervised learning algorithms trained only by surface electromyography (sEMG). A total of 19 (65.9 ± 12.4 years old; 12 men, seven women) acute stroke survivors (12.4 ± 6.3 days since onset) with hand paresis (Brunnstrom stage 4 ± 1/4 ± 1, SIAS 3 ± 1/3 ± 2, FMA-UE 40 ± 20) performed 10 repetitive hand movements reflecting basic activities of daily living (ADLs): rest, fist, pinch, wrist flexion, wrist extension, finger spread, and thumb up. Signals were recorded using an eight-channel, portable sEMG device with electrode placement on the forearms and thenar areas of both limbs (four sensors on each extremity). Using data preprocessing, semi-automatic segmentation, and a set of extracted feature vectors, support vector machine (SVM), linear discriminant analysis (LDA), and k-nearest neighbors (k-NN) classifiers for statistical comparison and validity (paired t-tests, p-value < 0.05), we were able to discriminate myoelectrical patterns for each gesture on both paretic and non-paretic sides. Despite any post-stroke conditions, the evaluated total accuracy rate by the 10-fold cross-validation using SVM among four-, five-, six-, and seven-gesture models were 96.62%, 94.20%, 94.45%, and 95.57% for non-paretic and 90.37%, 88.48%, 88.60%, and 89.75% for paretic limbs, respectively. LDA had competitive results using PCA whereas k-NN was a less efficient classifier in gesture prediction. Thus, we demonstrate partial efficacy of the combination of sEMG and supervised learning for upper-limb rehabilitation procedures for early acute stroke motor recovery and various treatment applications.
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Torabi, Faraz. "Imitation Learning from Observation." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 9900–9901. http://dx.doi.org/10.1609/aaai.v33i01.33019900.
Full textAbstract:
Humans and other animals have a natural ability to learn skills from observation, often simply from seeing the effects of these skills: without direct knowledge of the underlying actions being taken. For example, after observing an actor doing a jumping jack, a child can copy it despite not knowing anything about what's going on inside the actor's brain and nervous system. The main focus of this thesis is extending this ability to artificial autonomous agents, an endeavor recently referred to as "imitation learning from observation." Imitation learning from observation is especially relevant today due to the accessibility of many online videos that can be used as demonstrations for robots. Meanwhile, advances in deep learning have enabled us to solve increasingly complex control tasks mapping visual input to motor commands. This thesis contributes algorithms that learn control policies from state-only demonstration trajectories. Two types of algorithms are considered. The first type begins by recovering the missing action information from demonstrations and then leverages existing imitation learning algorithms on the full state-action trajectories. Our preliminary work has shown that learning an inverse dynamics model of the agent in a self-supervised fashion and then inferring the actions performed by the demonstrator enables sufficient action recovery for this purpose. The second type of algorithm uses model-free end-to-end learning. Our preliminary results indicate that iteratively optimizing a policy based on the closeness of the imitator's and expert's state transitions leads to a policy that closely mimics the demonstrator's trajectories.
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Mohammed, Mohammed Guhdar, Belnd Saadi Salih, and Vaman Muhammed Haji. "Employing EMG sensors in Bionic limbs based on a New Binary Trick Method." Science Journal of University of Zakho 11, no. 1 (January 29, 2023): 54–58. http://dx.doi.org/10.25271/sjuoz.2023.11.1.1027.
Full textAbstract:
Human muscles can be read by using electromyography (EMG) sensors, which are electrical signals generated by the muscles of human and animal bodies. This means it is possible to use electricity generated by muscles to control actuators/servo motors for any specific tasks. This could support a wide range of applications, especially for people with disabilities. One such application would be making bionic limbs based on servo motors. According to a study held by the K4D helpdesk report based on estimations that 15.3% of the world’s population has a moderate or severe disability, this proportion is likely to increase to 18-20% in conflict- affected areas (Thompson, 2017). The goal of this study is to make bionic limbs affordable by minimizing the cost while maintaining accuracy at an acceptable rate. To achieve this goal, the study proposes a new idea for using electromyography (EMG) sensors in bionic limbs, which suggests a decrease in the number of EMG sensors to decrease the cost and power consumption. Decreasing the number of EMG sensors will result in a loss of accuracy in controlling actuators (servo motors) because usually, each sensor is responsible for activating one servo motor. In normal projects, one will need at least six EMG sensors to control six servo motors. The study will use only three EMG sensors to control/activate six servo motors depending on the binary trick idea suggested by this study, which is manipulating all three input signals from EMG sensors at once and then deciding which servo motor to activate by using a supervised machine learning technique such as K-nearest neighbors (kNN).