EMG Signal Classification Using Reflection Coefficients and Extreme Value Machine
Electromyography is a promising approach to the gesture recognition of humans if an efficient classifier with high accuracy is available. In this paper, we propose to utilize Extreme Value Machine (EVM) as a high-performance algorithm for the classification of EMG signals. We employ reflection coefficients obtained from an Autoregressive (AR) model to train a set of classifiers. Our experimental results indicate that EVM has better accuracy in comparison to the conventional classifiers approved in the literature based on K-Nearest Neighbors (KNN) and Support Vector Machine (SVM).
Code (0)
등록된 구현이 없습니다.
Tasks
ClassificationGesture RecognitionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Receiving RISs: Enabling Channel Estimation and Autonomous Configuration
This chapter focuses on a hardware architecture for semi-passive Reconfigurable Intelligent Surfaces (RISs) and investigates its consideration for boosting the performance of Multiple-Input Multiple-Output (MIMO) communi…
RIS with insufficient phase shifting capability: Modeling, beamforming, and experimental validations
Most research works on reconfigurable intelligent surfaces (RIS) rely on idealized models of the reflection coefficients, i.e., uniform reflection amplitude for any phase and sufficient phase shifting capability. In prac…
Towards Ubiquitous Positioning by Leveraging Reconfigurable Intelligent Surface
The received signal strength (RSS) based technique is widely utilized for ubiquitous positioning due to its advantage of simple implementability. However, its accuracy is limited because the RSS values of adjacent locati…
Learning-Based Adaptive IRS Control with Limited Feedback Codebooks
Intelligent reflecting surfaces (IRS) consist of configurable meta-atoms, which can change the wireless propagation environment through design of their reflection coefficients. We consider a practical setting where (i) t…
Deep Reinforcement Learning-Based Adaptive IRS Control with Limited Feedback Codebooks
Intelligent reflecting surfaces (IRS) consist of configurable meta-atoms, which can alter the wireless propagation environment through design of their reflection coefficients. We consider adaptive IRS control in the prac…
Deep Reinforcement Learningreinforcement-learningReinforcement Learning (RL)