{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T21:57:25Z","timestamp":1782943045386,"version":"3.54.5"},"reference-count":41,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2022,4,28]],"date-time":"2022-04-28T00:00:00Z","timestamp":1651104000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Background: Muscular-activity timing is useful information that is extractable from surface EMG signals (sEMG). However, a reference method is not available yet. The aim of this study is to investigate the reliability of a novel machine-learning-based approach (DEMANN) in detecting the onset\/offset timing of muscle activation from sEMG signals. Methods: A dataset of 2880 simulated sEMG signals, stratified for signal-to-noise ratio (SNR) and time support, was generated to train a hidden single-layer fully-connected neural network. DEMANN\u2019s performance was evaluated on simulated sEMG signals and two different datasets of real sEMG signals. DEMANN was validated against different reference algorithms, including the acknowledged double-threshold statistical algorithm (DT). Results: DEMANN provided a reliable prediction of muscle onset\/offset in simulated and real sEMG signals, being minimally affected by SNR variability. When directly compared with state-of-the-art algorithms, DEMANN introduced relevant improvements in prediction performances. Conclusions: These outcomes support DEMANN\u2019s reliability in assessing onset\/offset events in different motor tasks and the condition of signal quality (different SNR), improving reference-algorithm performances. Unlike other works, DEMANN\u2019s adopts a machine learning approach where a neural network is trained by only simulated sEMG signals, avoiding the possible complications and costs associated with a typical experimental procedure, making this approach suitable to clinical practice.<\/jats:p>","DOI":"10.3390\/s22093393","type":"journal-article","created":{"date-parts":[[2022,4,28]],"date-time":"2022-04-28T22:20:06Z","timestamp":1651184406000},"page":"3393","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["Machine Learning for Detection of Muscular Activity from Surface EMG Signals"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5362-3776","authenticated-orcid":false,"given":"Francesco","family":"Di Nardo","sequence":"first","affiliation":[{"name":"Department of Information Engineering, Universit\u00e0 Politecnica delle Marche, Via Brecce Bianche, 60131 Ancona, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3983-8268","authenticated-orcid":false,"given":"Antonio","family":"Nocera","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, Universit\u00e0 Politecnica delle Marche, Via Brecce Bianche, 60131 Ancona, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0173-9862","authenticated-orcid":false,"given":"Alessandro","family":"Cucchiarelli","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, Universit\u00e0 Politecnica delle Marche, Via Brecce Bianche, 60131 Ancona, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sandro","family":"Fioretti","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, Universit\u00e0 Politecnica delle Marche, Via Brecce Bianche, 60131 Ancona, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0244-9322","authenticated-orcid":false,"given":"Christian","family":"Morbidoni","sequence":"additional","affiliation":[{"name":"Department of Management and Business Administration, University of Chieti-Pescara, 65127 Pescara, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/S0966-6362(01)00100-X","article-title":"The evolution of clinical gait analysis part l: Kinesiological EMG","volume":"14","author":"Sutherland","year":"2001","journal-title":"Gait Posture"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Rosati, S., Ghislieri, M., Dotti, G., Fortunato, D., Agostini, V., Knaflitz, M., and Balestra, G. 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