About: Adaptive EMG-based hand gesture recognition using hyperdimensional computing

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An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Adaptive EMG-based hand gesture recognition using hyperdimensional computing
Creator	Khan, Yasser Ting, Jonathan Alexandrov, George Arias, Ana Benatti, Simone Benini, Luca Burghardt, Fred Menon, Alisha Moin, Ali Rabaey, Jan Rahimi, Abbas Tamakloe, Senam Yamamoto, Natasha Zhou, Andy
Source	ArXiv
abstract	Accurate recognition of hand gestures is crucial to the functionality of smart prosthetics and other modern human-computer interfaces. Many machine learning-based classifiers use electromyography (EMG) signals as input features, but they often misclassify gestures performed in different situational contexts (changing arm position, reapplication of electrodes, etc.) or with different effort levels due to changing signal properties. Here, we describe a learning and classification algorithm based on hyperdimensional (HD) computing that, unlike traditional machine learning algorithms, enables computationally efficient updates to incrementally incorporate new data and adapt to changing contexts. EMG signal encoding for both training and classification is performed using the same set of simple operations on 10,000-element random hypervectors enabling updates on the fly. Through human experiments using a custom EMG acquisition system, we demonstrate 88.87% classification accuracy on 13 individual finger flexion and extension gestures. Using simple model updates, we preserve this accuracy with less than 5.48% degradation when expanding to 21 commonly used gestures or when subject to changing situational contexts. We also show that the same methods for updating models can be used to account for variations resulting from the effort level with which a gesture is performed.
has issue date	2019-01-02(xsd:dateTime)
has license	arxiv
sha1sum (hex)	7c0a07a82c94cd89ab9822c34e62a674e10a0d33
resource representing a document's title	Adaptive EMG-based hand gesture recognition using hyperdimensional computing
resource representing a document's body	covid:7c0a07a82c94cd89ab9822c34e62a674e10a0d33#body_text
is schema:about of	named entity 'features' named entity 'learning' named entity 'degradation' named entity 'signal' named entity 'gestures' named entity 'signals' named entity 'set' named entity 'training' named entity 'extension' named entity 'model' named entity 'preserve' named entity 'EMG' named entity 'algorithms' named entity 'computing' named entity 'based' named entity 'prosthetics' named entity 'EMG' named entity 'performed' named entity 'updates' named entity 'system' named entity 'arm' named entity 'subject' named entity 'electrodes' named entity 'machine learning-based' named entity 'traditional machine learning' named entity 'electromyography' named entity 'computationally efficient' named entity 'gesture recognition' named entity 'computing' named entity 'flexion' named entity 'prosthetics' named entity 'machine learning' named entity 'DOF' named entity 'machine learning' named entity 'Nordic Semiconductor' named entity 'electrode' named entity 'EMG' named entity 'biosignals' named entity 'MATLAB' named entity 'electrode' named entity 'voltage' named entity 'user interfaces' named entity 'base station' named entity 'electrode' named entity 'EMG' named entity 'holographic' named entity 'inference' named entity 'extensor' named entity 'neurons' named entity 'hydrogel' named entity 'electrocorticography' named entity 'classifier' named entity 'EMG' named entity 'probability' named entity 'Li-ion battery' named entity 'DOF' named entity 'DOF' named entity 'MATLAB' named entity 'pattern recognition' named entity 'Source code' named entity 'computing' named entity 'GUI' named entity 'inference' named entity 'classifier' named entity 'FPGA' named entity 'electrode array' named entity 'EMG' named entity 'DOF' named entity 'electrode array' named entity 'inference' named entity 'classification model' named entity 'electrode' named entity 'EMG' named entity 'computing algorithm'

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