{"title":"与力量训练相关的双变量表面肌电信号的改进复合高斯模型","authors":"Durgesh Kusuru;Anish C. Turlapaty;Mainak Thakur","doi":"10.1109/THMS.2024.3486450","DOIUrl":null,"url":null,"abstract":"Recent literature suggests that the surface electromyography (sEMG) signals have nonstationary statistical characteristics, specifically due to the random nature of the covariance. Thus, the suitability of a statistical model for sEMG signals is determined by the choice of an appropriate model for describing the covariance. The purpose of this study is to propose a compound-Gaussian (CG) model for multivariate sEMG signals in which the latent variable of covariance is modeled as a random variable that follows an exponential model. The parameters of the model are estimated using the iterative expectation maximization (EM) algorithm. Further, a new dataset, electromyography analysis of human activities database 2 (EMAHA-DB2), is developed. The proposed model is evaluated through both qualitative and quantitative methods. Based on the model fitting analysis on the sEMG signals from EMAHA-DB2, it is found that the proposed CG model fits more closely to the empirical pdf of sEMG signals than the existing models. In addition, statistical analyses are carried out among the models and estimated parameters under different scenarios. The estimate of the exponential model's rate parameter exhibits a clear relationship with training weights, potentially correlating with underlying motor unit activity. Finally, the average signal power estimates of the channels show distinctive dependency on the training weights, the subject's training experience, and the type of activity.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"55 1","pages":"58-70"},"PeriodicalIF":3.5000,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An Improved Compound Gaussian Model for Bivariate Surface EMG Signals Related to Strength Training\",\"authors\":\"Durgesh Kusuru;Anish C. Turlapaty;Mainak Thakur\",\"doi\":\"10.1109/THMS.2024.3486450\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Recent literature suggests that the surface electromyography (sEMG) signals have nonstationary statistical characteristics, specifically due to the random nature of the covariance. Thus, the suitability of a statistical model for sEMG signals is determined by the choice of an appropriate model for describing the covariance. The purpose of this study is to propose a compound-Gaussian (CG) model for multivariate sEMG signals in which the latent variable of covariance is modeled as a random variable that follows an exponential model. The parameters of the model are estimated using the iterative expectation maximization (EM) algorithm. Further, a new dataset, electromyography analysis of human activities database 2 (EMAHA-DB2), is developed. The proposed model is evaluated through both qualitative and quantitative methods. Based on the model fitting analysis on the sEMG signals from EMAHA-DB2, it is found that the proposed CG model fits more closely to the empirical pdf of sEMG signals than the existing models. In addition, statistical analyses are carried out among the models and estimated parameters under different scenarios. The estimate of the exponential model's rate parameter exhibits a clear relationship with training weights, potentially correlating with underlying motor unit activity. Finally, the average signal power estimates of the channels show distinctive dependency on the training weights, the subject's training experience, and the type of activity.\",\"PeriodicalId\":48916,\"journal\":{\"name\":\"IEEE Transactions on Human-Machine Systems\",\"volume\":\"55 1\",\"pages\":\"58-70\"},\"PeriodicalIF\":3.5000,\"publicationDate\":\"2024-11-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Human-Machine Systems\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10754887/\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Human-Machine Systems","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10754887/","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
An Improved Compound Gaussian Model for Bivariate Surface EMG Signals Related to Strength Training
Recent literature suggests that the surface electromyography (sEMG) signals have nonstationary statistical characteristics, specifically due to the random nature of the covariance. Thus, the suitability of a statistical model for sEMG signals is determined by the choice of an appropriate model for describing the covariance. The purpose of this study is to propose a compound-Gaussian (CG) model for multivariate sEMG signals in which the latent variable of covariance is modeled as a random variable that follows an exponential model. The parameters of the model are estimated using the iterative expectation maximization (EM) algorithm. Further, a new dataset, electromyography analysis of human activities database 2 (EMAHA-DB2), is developed. The proposed model is evaluated through both qualitative and quantitative methods. Based on the model fitting analysis on the sEMG signals from EMAHA-DB2, it is found that the proposed CG model fits more closely to the empirical pdf of sEMG signals than the existing models. In addition, statistical analyses are carried out among the models and estimated parameters under different scenarios. The estimate of the exponential model's rate parameter exhibits a clear relationship with training weights, potentially correlating with underlying motor unit activity. Finally, the average signal power estimates of the channels show distinctive dependency on the training weights, the subject's training experience, and the type of activity.
期刊介绍:
The scope of the IEEE Transactions on Human-Machine Systems includes the fields of human machine systems. It covers human systems and human organizational interactions including cognitive ergonomics, system test and evaluation, and human information processing concerns in systems and organizations.