{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T22:19:05Z","timestamp":1773094745463,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":24,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,10,27]],"date-time":"2023-10-27T00:00:00Z","timestamp":1698364800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"the National Key Research and Development Program of China;National Defense Basic Scientific Research Program of China;Shanghai Major science and technology Project ;the Shanghai Industrial Collaborative Technology Innovation Project","award":["2022YFC3602700, 2022YFC3602703;NO.JCKY2021413B005;No.2021SHZDZX;No.XTCX-KJ-2022-2-14"],"award-info":[{"award-number":["2022YFC3602700, 2022YFC3602703;NO.JCKY2021413B005;No.2021SHZDZX;No.XTCX-KJ-2022-2-14"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,10,27]]},"DOI":"10.1145\/3633637.3633647","type":"proceedings-article","created":{"date-parts":[[2024,2,28]],"date-time":"2024-02-28T08:08:05Z","timestamp":1709107685000},"page":"65-71","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Comparative Analysis of Classification Methods for Diagnosing Myasthenia Gravis based on Lumbar Electromyography"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-0773-4831","authenticated-orcid":false,"given":"Yue","family":"Ma","sequence":"first","affiliation":[{"name":"Shanghai University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4261-9875","authenticated-orcid":false,"given":"Banghua","family":"Yang","sequence":"additional","affiliation":[{"name":"Shanghai University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2588-2188","authenticated-orcid":false,"given":"Peng","family":"Zan","sequence":"additional","affiliation":[{"name":"Shanghai University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-8865-2207","authenticated-orcid":false,"given":"Yonghuai","family":"Zhang","sequence":"additional","affiliation":[{"name":"Shanghai Shaonao Technology Co., Ltd., China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,2,28]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"USA2020","author":"Nations U.","year":"2020","unstructured":"Nations U. World population ageing 2019 (st\/esa\/ser. a\/444)[J]. Department of Economic and Social Affairs PD, editor. New York, USA2020, 2020."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"crossref","unstructured":"Lexell J Taylor C C Sj\u00f6str\u00f6m M. What is the cause of the ageing atrophy?: Total number size and proportion of different fiber types studied in whole vastus lateralis muscle from 15-to 83-year-old men[J]. Journal of the neurological sciences 1988 84(2-3): 275-294.","DOI":"10.1016\/0022-510X(88)90132-3"},{"key":"e_1_3_2_1_3_1","volume-title":"Dietary protein and muscle in older persons[J]. Current opinion in clinical nutrition and metabolic care","author":"Paddon-Jones D","year":"2014","unstructured":"Paddon-Jones D, Leidy H. Dietary protein and muscle in older persons[J]. Current opinion in clinical nutrition and metabolic care, 2014, 17(1): 5."},{"key":"e_1_3_2_1_4_1","volume-title":"An overview of sarcopenia: facts and numbers on prevalence and clinical impact[J]. Journal of cachexia, sarcopenia and muscle","author":"von Haehling S","year":"2010","unstructured":"von Haehling S, Morley J E, Anker S D. An overview of sarcopenia: facts and numbers on prevalence and clinical impact[J]. Journal of cachexia, sarcopenia and muscle, 2010, 1: 129-133."},{"key":"e_1_3_2_1_5_1","volume-title":"burden and challenges for public health[J]. Archives of public health","author":"Beaudart C","year":"2014","unstructured":"Beaudart C, Rizzoli R, Bruy\u00e8re O, Sarcopenia: burden and challenges for public health[J]. Archives of public health, 2014, 72(1): 1-8."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1532-5415.2004.52014.x"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jamcollsurg.2013.04.042"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1177\/0884533615569888"},{"key":"e_1_3_2_1_9_1","volume-title":"Optimal management of sarcopenia[J]. Clinical interventions in aging","author":"Burton L A","year":"2010","unstructured":"Burton L A, Sumukadas D. Optimal management of sarcopenia[J]. Clinical interventions in aging, 2010: 217-228."},{"key":"e_1_3_2_1_10_1","volume-title":"The role of DXA in sarcopenia[J]. Aging clinical and experimental research","author":"Guglielmi G","year":"2016","unstructured":"Guglielmi G, Ponti F, Agostini M, The role of DXA in sarcopenia[J]. Aging clinical and experimental research, 2016, 28: 1047-1060."},{"key":"e_1_3_2_1_11_1","first-page":"1","article-title":"A Cross-Sectional Study[J]","volume":"2022","author":"Thackeray M","unstructured":"Thackeray M, Orford N R, Kotowicz M A, Estimation of Whole-Body and Appendicular Lean Mass from Spine and Hip Dual Energy X-ray Absorptiometry: A Cross-Sectional Study[J]. Calcified Tissue International, 2022: 1-8.","journal-title":"Calcified Tissue International"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"crossref","unstructured":"Tandon P Mourtzakis M Low G Comparing the Variability Between Measurements for Sarcopenia Using Magnetic Resonance Imaging and Computed Tomography Imaging[J]. American journal of transplantation: official journal of the American Society of Transplantation and the American Society of Transplant Surgeons 2016 16(9): 2766-2767.","DOI":"10.1111\/ajt.13832"},{"key":"e_1_3_2_1_13_1","volume-title":"A body sensor network with electromyogram and inertial sensors: Multimodal interpretation of muscular activities[J]","author":"Ghasemzadeh H","year":"2009","unstructured":"Ghasemzadeh H, Jafari R, Prabhakaran B. A body sensor network with electromyogram and inertial sensors: Multimodal interpretation of muscular activities[J]. IEEE transactions on information technology in biomedicine, 2009, 14(2): 198-206."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.14219\/jada.archive.2006.0288"},{"key":"e_1_3_2_1_15_1","volume-title":"Sphincter EMG and differential diagnosis of multiple system atrophy[J]. Movement disorders: official journal of the Movement Disorder Society","author":"Vodu\u0161ek D B","year":"2001","unstructured":"Vodu\u0161ek D B. Sphincter EMG and differential diagnosis of multiple system atrophy[J]. Movement disorders: official journal of the Movement Disorder Society, 2001, 16(4): 600-607."},{"key":"e_1_3_2_1_16_1","volume-title":"Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders[J]. Computers in biology and medicine","author":"Subasi A.","year":"2013","unstructured":"Subasi A. Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders[J]. Computers in biology and medicine, 2013, 43(5): 576-586."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12984-020-0645-2"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.3390\/s22072721"},{"issue":"1","key":"e_1_3_2_1_19_1","first-page":"012010","article-title":"Assessment of muscles fatigue based on surface EMG signals using machine learning and statistical approaches: a review[C]\/\/IOP conference series: materials science and engineering","volume":"705","author":"Yousif H A","year":"2019","unstructured":"Yousif H A, Zakaria A, Rahim N A, Assessment of muscles fatigue based on surface EMG signals using machine learning and statistical approaches: a review[C]\/\/IOP conference series: materials science and engineering. IOP Publishing, 2019, 705(1): 012010.","journal-title":"IOP Publishing"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.robot.2014.01.008"},{"issue":"5","key":"e_1_3_2_1_21_1","first-page":"793","article-title":"EMG signals analysis of BF and RF muscles in autism spectrum disorder (ASD) during walking[J]. International Journal on Advanced Science","volume":"6","author":"Nor M N M","year":"2016","unstructured":"Nor M N M, Jailani R, Tahir N M, EMG signals analysis of BF and RF muscles in autism spectrum disorder (ASD) during walking[J]. International Journal on Advanced Science, Engineering and Information Technology, 2016, 6(5): 793-798.","journal-title":"Engineering and Information Technology"},{"key":"e_1_3_2_1_22_1","first-page":"237","article-title":"EMG signals for assessment of neuromuscular disorder using empirical mode decomposition and logistic regression[C]\/\/2019 International Conference on Applied and Engineering Mathematics (ICAEM)","volume":"2019","author":"Khan M U","unstructured":"Khan M U, Aziz S, Bilal M, Classification of EMG signals for assessment of neuromuscular disorder using empirical mode decomposition and logistic regression[C]\/\/2019 International Conference on Applied and Engineering Mathematics (ICAEM). IEEE, 2019: 237-243.","journal-title":"IEEE"},{"key":"e_1_3_2_1_23_1","volume-title":"Evaluation of three machine learning algorithms for the automatic classification of EMG patterns in gait disorders[J]. Frontiers in neurology","author":"Fricke C","year":"2021","unstructured":"Fricke C, Alizadeh J, Zakhary N, Evaluation of three machine learning algorithms for the automatic classification of EMG patterns in gait disorders[J]. Frontiers in neurology, 2021, 12: 666458."},{"key":"e_1_3_2_1_24_1","volume-title":"Multiday evaluation of techniques for EMG-based classification of hand motions[J]","author":"Waris A","year":"2018","unstructured":"Waris A, Niazi I K, Jamil M, Multiday evaluation of techniques for EMG-based classification of hand motions[J]. IEEE journal of biomedical and health informatics, 2018, 23(4): 1526-1534."}],"event":{"name":"ICCPR 2023: 2023 12th International Conference on Computing and Pattern Recognition","location":"Qingdao China","acronym":"ICCPR 2023"},"container-title":["2023 12th International Conference on Computing and Pattern Recognition"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3633637.3633647","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3633637.3633647","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T17:17:20Z","timestamp":1755883040000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3633637.3633647"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,27]]},"references-count":24,"alternative-id":["10.1145\/3633637.3633647","10.1145\/3633637"],"URL":"https:\/\/doi.org\/10.1145\/3633637.3633647","relation":{},"subject":[],"published":{"date-parts":[[2023,10,27]]},"assertion":[{"value":"2024-02-28","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}