A Laplacian Gaussian Mixture Model for Surface EMG Signals of Human Arm Activity
The probability density function (pdf) of surface Electromyography (sEMG) signals follows any one of the standalone standard distributions: the Gaussian or the Laplacian. Further, the choice of the model is dependent on muscle contraction force (MCF) levels. Hence, a unified model is proposed which explains the statistical nature of sEMG signals at different MCF levels. In this paper, we propose the Laplacian Gaussian Mixture (LGM) model for the signals recorded from upper limbs. This model is able to explain the sEMG signals from different activities corresponding to different MCF levels. The model is tested on different bench-mark sEMG data sets and is validated using both the qualitative and quantitative perspectives. It is determined that for low and medium contraction force levels the proposed mixture model is more accurate than both the Laplacian and the Gaussian models. Whereas for high contraction force level, the LGM model behaves as a Gaussian model. The mixing weights of the LGM model are analyzed and it is observed that for low and medium MCF levels both the mixing weights of LGM model do contribute. Whereas for high contraction force levels the Laplacian weight becomes weaker. The proposed LGM model for sEMG signals from upper limbs explains sEMG signals at different MCF levels. The proposed model helps in improved understanding of statistical nature of sEMG signals and better feature representation in the classification problems.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Analysis of LGM Model for sEMG Signals related to Weight Training
Statistical models of Surface electromyography (sEMG) signals have several applications such as better understanding of sEMG signal generation, improved pattern recognition based control of wearable exoskeletons and pros…
Fisher Vectors Derived from Hybrid Gaussian-Laplacian Mixture Models for Image Annotation
In the traditional object recognition pipeline, descriptors are densely sampled over an image, pooled into a high dimensional non-linear representation and then passed to a classifier. In recent years, Fisher Vectors hav…
Image RetrievalObject RecognitionSentenceA clustering tool for nucleotide sequences using Laplacian Eigenmaps and Gaussian Mixture Models
We propose a new procedure for clustering nucleotide sequences based on the "Laplacian Eigenmaps" and Gaussian Mixture modelling. This proposal is then applied to a set of 100 DNA sequences from the mitochondrially encod…
ClusteringAssociating Neural Word Embeddings With Deep Image Representations Using Fisher Vectors
In recent years, the problem of associating a sentence with an image has gained a lot of attention. This work continues to push the envelope and makes further progress in the performance of image annotation and image sea…
Image RetrievalSentenceWord EmbeddingsOn the Behavior of the Expectation-Maximization Algorithm for Mixture Models
Finite mixture models are among the most popular statistical models used in different data science disciplines. Despite their broad applicability, inference under these models typically leads to computationally challengi…