{"doi":"10.1109/embc.2014.6944322","title":"Analysis of progression of fatigue conditions in biceps brachii muscles using surface electromyography signals and complexity based features","abstract":null,"journal":"2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society","year":2014,"id":660519,"datarank":0.38474240361923057,"base_score":2.5649493574615367,"endowment":2.5649493574615367,"self_citation_contribution":0.38474240361923057,"citation_network_contribution":0.0,"self_endowment_contribution":0.38474240361923057,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":12,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1234298,"name":"Navaneethakrishna Makaram","orcid":"0000-0002-5751-8842","position":1,"is_corresponding":false},{"id":1322522,"name":"S. Ramakrishnan","orcid":"0000-0003-2905-7388","position":2,"is_corresponding":false},{"id":1724293,"name":"P. A. Karthick","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Analysis of progression of fatigue conditions in biceps brachii muscles using surface electromyography signals and complexity based features","abstract":"Muscle fatigue is a neuromuscular condition where muscle performance decreases due to sustained or intense contraction. It is experienced by both normal and abnormal subjects. In this work, an attempt has been made to analyze the progression of muscle fatigue in biceps brachii muscles using surface electromyography (sEMG) signals. The sEMG signals are recorded from fifty healthy volunteers during dynamic contractions under well defined protocol. The acquired signals are preprocessed and segmented in to six equal parts for further analysis. The features, such as activity, mobility, complexity, sample entropy and spectral entropy are extracted from all six zones. The results are found showing that the extracted features except complexity feature have significant variations in differentiating non-fatigue and fatigue zone respectively. Thus, it appears that, these features are useful in automated analysis of various neuromuscular activities in normal and pathological conditions.","is_dataset_classified":null,"base_score":2.5649493574615367,"endowment":2.5649493574615367,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"25570690","pmcid":null,"openalex_id":"https://openalex.org/W2318171354","authors":[],"funders":[],"total_grants":0,"fwci":0.6005,"citation_percentile":0.68775982,"influential_citations":0,"citation_trend":[{"year":2015,"count":1},{"year":2019,"count":1},{"year":2020,"count":1},{"year":2021,"count":3},{"year":2022,"count":2},{"year":2023,"count":2},{"year":2025,"count":2}],"oa_status":"closed","license":null,"oa_locations":[{"url":"http://xplorestaging.ieee.org/ielx7/6923026/6943513/06944322.pdf?arnumber=6944322","host_type":"publisher"},{"url":"https://doi.org/10.1109/embc.2014.6944322","host_type":""},{"url":"https://pubmed.ncbi.nlm.nih.gov/25570690","host_type":"repository"}],"fields_of_study":["Muscle activation and electromyography studies","Advanced Sensor and Energy Harvesting Materials","Motor Control and Adaptation","Adult","Algorithms","Arm","Electromyography","Entropy","Humans","Male","Muscle Contraction","Muscle Fatigue","Muscle, Skeletal","Signal Processing, Computer-Assisted"],"mesh_terms":["Adult","Algorithms","Arm","Electromyography","Humans","Male","Muscle Contraction","Signal Processing, Computer-Assisted","Muscle, Skeletal","Muscle Fatigue","Entropy"],"keywords":["Biceps","Electromyography","Muscle fatigue","Sample entropy","Physical medicine and rehabilitation","Computer science","Muscle contraction","Biomedical engineering","Pattern recognition (psychology)","Artificial intelligence","Medicine","Anatomy"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-12T09:22:27.844600Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}