Learning under nonstationarity: covariate shift and class‐balance change

Sugiyama, Masashi; Yamada, Makoto; Plessis, Marthinus Christoffel du

doi:10.1002/wics.1275

Cited by 17 publications

(14 citation statements)

References 63 publications

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“…The mathematical models of EMG are not used in HGR due to the difficulty of parameter estimation in non-stationary processes. However, machine learning (ML) methods are widely used because ML can infer a solution for non-stationary processes [21] using several techniques; for example, covariate shift techniques [21,22], class-balance change [22], and segmentation in short stationary intervals [23]. HGR using ML is just one approach to myoelectric control [24], which uses EMG signals to extract control signals to command external devices [25,26], for example, prostheses [1], drones [8], input devices for a computer [27], etc.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Hand Gesture Recognition Using Surface Electromyography and Machine Learning: A Systematic Literature Review

Jaramillo-Yánez

Benalcázar

Mena-Maldonado

2020

Sensors

170

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Today, daily life is composed of many computing systems, therefore interacting with them in a natural way makes the communication process more comfortable. Human–Computer Interaction (HCI) has been developed to overcome the communication barriers between humans and computers. One form of HCI is Hand Gesture Recognition (HGR), which predicts the class and the instant of execution of a given movement of the hand. One possible input for these models is surface electromyography (EMG), which records the electrical activity of skeletal muscles. EMG signals contain information about the intention of movement generated by the human brain. This systematic literature review analyses the state-of-the-art of real-time hand gesture recognition models using EMG data and machine learning. We selected and assessed 65 primary studies following the Kitchenham methodology. Based on a common structure of machine learning-based systems, we analyzed the structure of the proposed models and standardized concepts in regard to the types of models, data acquisition, segmentation, preprocessing, feature extraction, classification, postprocessing, real-time processing, types of gestures, and evaluation metrics. Finally, we also identified trends and gaps that could open new directions of work for future research in the area of gesture recognition using EMG.

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Section: Introductionmentioning

confidence: 99%

Real-Time Hand Gesture Recognition Using Surface Electromyography and Machine Learning: A Systematic Literature Review

Jaramillo-Yánez

Benalcázar

Mena-Maldonado

2020

Sensors

170

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“…For that reason, uLSIF-based machine learning algorithms have been successfully used in solving various machine learning tasks [10]- [12].…”

Section: Introductionmentioning

confidence: 99%

Direct Density Ratio Estimation with Convolutional Neural Networks with Application in Outlier Detection

Nam

Sugiyama

2015

IEICE Trans. Inf. & Syst.

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SUMMARYRecently, the ratio of probability density functions was demonstrated to be useful in solving various machine learning tasks such as outlier detection, non-stationarity adaptation, feature selection, and clustering. The key idea of this density ratio approach is that the ratio is directly estimated so that difficult density estimation is avoided. So far, parametric and non-parametric direct density ratio estimators with various loss functions have been developed, and the kernel least-squares method was demonstrated to be highly useful both in terms of accuracy and computational efficiency. On the other hand, recent study in pattern recognition exhibited that deep architectures such as a convolutional neural network can significantly outperform kernel methods. In this paper, we propose to use the convolutional neural network in density ratio estimation, and experimentally show that the proposed method tends to outperform the kernelbased method in outlying image detection. key words: density ratio estimation, convolutional neural network, outlier detection

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“…In a next step, to differentiate between the classes, assistive BRIs use classifier calibration to weight features according to their relevance. However, learning brain self-regulation may lead to non-stationarity of these classes in the course of the training (Vidaurre et al, 2011a ; Sugiyama et al, 2013 ; Naros and Gharabaghi, 2015 ). Unsupervised adaptation of the feature weights may therefore lead to a switch in the mental strategy (Vidaurre et al, 2011b ; Bryan et al, 2013 ).…”

Section: Methodological Adjustmentsmentioning

confidence: 99%

“…Furthermore, when a patient alters the mental strategy in the course of the intervention, a classifier trained on the initial strategy can become misaligned. In classical brain-interface approaches, the adaptation of feature weights has been proposed for such cases (Vidaurre et al, 2011b ; Bryan et al, 2013 ; Sugiyama et al, 2013 ). But such data-driven approaches can be problematic for restorative approaches; classifier adaptation might condition the patients to explore alternative, i.e., therapeutically non-desired strategies (Bauer and Gharabaghi, 2015a , b ).…”

Section: Regularized Feature Weightsmentioning

confidence: 99%

Constraints and Adaptation of Closed-Loop Neuroprosthetics for Functional Restoration

Bauer

Gharabaghi

2017

Front. Neurosci.

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Closed-loop neuroprosthetics aim to compensate for lost function, e.g., by controlling external devices such as prostheses or wheelchairs. Such assistive approaches seek to maximize speed and classification accuracy for high-dimensional control. More recent approaches use similar technology, but aim to restore lost motor function in the long term. To achieve this goal, restorative neuroprosthetics attempt to facilitate motor re-learning and to strengthen damaged and/or alternative neural connections on the basis of neurofeedback training within rehabilitative environments. Such a restorative approach requires reinforcement learning of self-modulated brain activity which is considered to be beneficial for functional rehabilitation, e.g., improvement of β-power modulation over sensorimotor areas for post-stroke movement restoration. Patients with motor impairments, however, may also have a compromised ability for motor task-related regulation of the targeted brain activity. This would affect the estimation of feature weights and hence the classification accuracy of the feedback device. This, in turn, can frustrate the patients and compromise their motor learning. Furthermore, the feedback training may even become erroneous when unconstrained classifier adaptation—which is often used in assistive approaches—is also applied in this rehabilitation context. In conclusion, the conceptual switch from assistance toward restoration necessitates a methodological paradigm shift from classification accuracy toward instructional efficiency. Furthermore, a constrained feature space, a priori regularized feature weights, and difficulty adaptation present key elements of restorative brain interfaces. These factors need, therefore, to be addressed within a therapeutic framework to facilitate reinforcement learning of brain self-regulation for restorative purposes.

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Learning under nonstationarity: covariate shift and class‐balance change

Cited by 17 publications

References 63 publications

Real-Time Hand Gesture Recognition Using Surface Electromyography and Machine Learning: A Systematic Literature Review

Real-Time Hand Gesture Recognition Using Surface Electromyography and Machine Learning: A Systematic Literature Review

Direct Density Ratio Estimation with Convolutional Neural Networks with Application in Outlier Detection

Constraints and Adaptation of Closed-Loop Neuroprosthetics for Functional Restoration

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