A new force profile signal for a convex solution of muscle force estimation from electromyographic signals*
文献类型： 会议论文
第一作者： Mehdi Shirzadi
作者： Mehdi Shirzadi 1 ; Arezoo Mirshamsi 2 ; Alp E?refo?lu 3 ; Monica Rojas-Martínez 1 ; Hamid Reza Marateb 2 ; Miquel Angel Ma?anas 4 ; Taian Vieira 5 ;
作者机构： 1.Department of Automatic Control (ESAII), Biomedical Engineering Research Centre (CREB), Universitat Politèncicna de Catalunya (UPC), Barcelona, Spain
2.Biomedical Engineering Department, Engineering Faculty, University of Isfahan, Isfahan, Iran
3.Laboratory of Applied Biology & Research Unit in Applied Neurophysiology (LABNeuro) – Université Libre de Bruxelles, Brussels, Belgium
4.Department of Automatic Control (ESAII), Biomedical Engineering Research Centre (CREB), Universitat Politèncicna de Catalunya (UPC), Barcelona, Spain|CIBER of Bioengineering, Biomaterilas and Nanomedicine (CIBER-BBN), Madrid, Spain
5.Department of Electronics and Telecommunication, Laboratory for Engineering of the Neuromuscular System (LISiN), PolitoBIOMed Lab – Politecnico di Torino, Turin, Italy
关键词： Training;Correlation;Biological system modeling;Force;Estimation;Muscles;Predictive models
会议名称： Annual International Conference of the IEEE Engineering in Medicine and Biology Society
主办单位：
页码： 1-4
摘要： High-Density Surface Electromyography (HD-sEMG) is a non-invasive technique for measuring the electrical activity of a muscle with multiple, closely spaced electrodes. Estimation of muscle force is one of the applications of HD-sEMG. Usually, validating different EMG-Force models entails simple movements limited to laboratory settings. The validity of these models in more ecological conditions, requesting force production over a wide frequency band, remains unknown. In this study, we, therefore, compare the results of force prediction using four different types of input force profiles that can be representative of daily life activities, and we investigate whether the crest factor of these different input signals affects force prediction. For predicting the force from sEMG signals, we used our real-time and convex methods. HD-sEMG signals were recorded with 144 channels from the biceps brachii, brachioradialis, and triceps (long, lateral, and medial head) muscles of 24 healthy subjects during random signal, random phase, Schroeder phase, and minimum crest factor (crestmin) signal. The correlation and coefficient of determination (R2) between measured and predicted forces were calculated for the different force feedback profiles. The crestmin signal showed significantly better results based on statistical tests (P-value 2 equal to 0.92±0.03 and 0.86±0.05, respectively. The results demonstrate that the crest factor of input signals is a crucial parameter that can impact the performance of EMG-Force models and must be considered during training.Clinical Relevance— This study demonstrates that lower crest factor multisine force profiles result in improved fitness for force prediction and can be used as an alternative to random signals.
分类号： q81
- 相关文献
[1]Division of dataset into training and validation subsets by the jackknife validations to predict the pH optimum for beta-cellobiosidase. Shaomin Yan,Guang Wu. 2022
[2]Influence of the Fuzzy Function on the Estimation of the Fuzzy Sample Entropy with Fixed Tolerance Values for the Evaluation of Surface EMG Muscle Activity. Abel Torres,Luis Estrada-Petrocelli. 2023
[3]An Optimized Portable Cable-Driven Haptic Robot Enables Free Motion and Hard Contact. Changqi Zhang,Cui Wang,Qingkai Yang,Mingming Zhang. 2023
[4]A High-Resolution Grassland Aboveground Biomass Estimation Method Based on SfM and Grassland Height Factor. Mengdi Cao,Shanshan Cao,Wei Sun,Fantao Kong. 2023
[5]Design and Implementation of Multi-factor Aided Spatial Interpolation Software Tool for Forest Biomass. Wei Sun,Shanshan Cao,Fantao Kong,Jifang Liu. 2023
[6]Exploring the Impact of Acquisition and Reconstruction Parameters on an Imaging-Based Lung Cancer Risk Model. Luoting Zhuang,Anil Yadav,Grace H Kim,Seyed Mohammad Hossein Tabatabaei,Ashley Prosper,William Hsu. 2024
[7]Pairing Predictive Simulations and Human Experiments in Ankle Exoskeleton Walking. Alexander Weiss,Anthony Chen,Daniel B. Janischowky,Isabella Shih,Gillian C. Phillips,Jessica C. Selinger,Anne D. Koelewijn. 2024
[8]Multivariable Regression Model to Estimate Tidal Volume for Different Respiratory Patterns. Daniel Romero Pérez,Jordi Solà Soler,Leon Balchin,Arantxa Mas Serra,Manuel Lujan Torné,Melinda R. Popoviciu Koborzan,Beatriz F. Giraldo. 2023
[9]Deep Learning-Based Predictive Model for Revascularization of Chronic Total Occlusions on Angiographic Imaging. Sara Pérez-Martínez,Agustín Fernández-Cisnal,Manuel Pérez-Pelegrí,Sergio García-Blas,Gema Mi?ana,Ernesto Valero,Juan Sanchis,David Moratal. 2023
[10]Automatically estimated meals in Model Predictive Control-Moving Horizon Estimation control strategy. Máté Siket,Kamilla Novák,Levente Kovács,Gy?rgy Eigner. 2022
[11]A predictive model of the distribution of T-cell phenotype variations. Laura D’Orsi,Elena Lo Presti,Giuseppe Giacopelli,Andrea De Gaetano. 2024
[12]VolRAFT: Volumetric Optical Flow Network for Digital Volume Correlation of Synchrotron Radiation-based Micro-CT Images of Bone-Implant Interfaces. Tak Ming Wong,Julian Moosmann,Berit Zeller-Plumhoff. 2024
[13]XNet: Wavelet-Based Low and High Frequency Fusion Networks for Fully- and Semi-Supervised Semantic Segmentation of Biomedical Images. Yanfeng Zhou,Jiaxing Huang,Chenlong Wang,Le Song,Ge Yang. 2023
[14]Automatic Tool for Pulmonary Artery Hemodynamic Assessment from 4D flow MRI. Maialen Stephens,ángel Gaitán,Rahul Kumar,Jorge Nuche,Irene Marco,Juan Delgado,Jesús Ruíz-Cabello,Karen López-Linares,Javier Bóbeda,Haizea Erostarbe. 2023
[15]Non-contact Apnea-Hypopnea Index Estimation using Near Infrared Video *. Kaiyin Zhu,Azadeh Yadollahi,Babak Taati. 2019
[16]Topological Analysis of Deep Network Training. Andrea De Gaetano. 2024
[17]Expression analyses and antiviral properties of the Beijing-You and White Leghorn myxovirus resistance gene with different amino acids at position 631. Du, L. X.,Zhao, G. P.,Zhang, C. S..
[18]Determination of Chlormequat in Muscles of Mice by Solid Phase Extraction-Liquid Chromatography-Tandem Mass Spectrometry. Li Chun-Mei,Jin Fen,Xu Si-Yuan,Liu Jia-Jia,Jin Mao-Jun,Shao Hua,Wang Jing,Li Chun-Mei,Yang Ming-Qi,Liu Jia-Jia. 2012
[19]Estimation of carbon and nitrogen contents in citrus canopy by low-altitude remote sensing. Liu Xuefeng,Lyu Qiang,He Shaolan,Yi Shilai,Wang Zhitao,Xie Rangjin,Zheng Yongqiang,Deng Lie,Liu Xuefeng,Lyu Qiang,Deng Lie,Hu Deyu. 2016
[20]Estimating the Contribution of New Seed Cultivars to Increases in Crop Yields: A Case Study for Corn. Qian, Jiarong,Zhao, Zhijun. 2017
