{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,2]],"date-time":"2026-08-02T21:27:08Z","timestamp":1785706028681,"version":"3.56.0"},"reference-count":64,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2020,12,3]],"date-time":"2020-12-03T00:00:00Z","timestamp":1606953600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100008982","name":"Qatar National Research Fund","doi-asserted-by":"publisher","award":["NPRP 10-1231-160071"],"award-info":[{"award-number":["NPRP 10-1231-160071"]}],"id":[{"id":"10.13039\/100008982","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Fatigue is defined as \u201ca loss of force-generating capacity\u201d in a muscle that can intensify tremor. Tremor quantification can facilitate early detection of fatigue onset so that preventative or corrective controls can be taken to minimize work-related injuries and improve the performance of tasks that require high-levels of accuracy. We focused on developing a system that recognizes and classifies voluntary effort and detects phases of fatigue. The experiment was designed to extract and evaluate hand-tremor data during the performance of both rest and effort tasks. The data were collected from the wrist and finger of the participant\u2019s dominant hand. To investigate tremor, time, frequency domain features were extracted from the accelerometer signal for segments of 45 and 90 samples\/window. Analysis using advanced signal processing and machine-learning techniques such as decision tree, k-nearest neighbor, support vector machine, and ensemble classifiers were applied to discover models to classify rest and effort tasks and the phases of fatigue. Evaluation of the classifier\u2019s performance was assessed based on various metrics using 5-fold cross-validation. The recognition of rest and effort tasks using an ensemble classifier based on the random subspace and window length of 45 samples was deemed to be the most accurate (96.1%). The highest accuracy (~98%) that distinguished between early and late fatigue phases was achieved using the same classifier and window length.<\/jats:p>","DOI":"10.3390\/s20236897","type":"journal-article","created":{"date-parts":[[2020,12,3]],"date-time":"2020-12-03T11:15:43Z","timestamp":1606994143000},"page":"6897","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Classification of Fatigue Phases in Healthy and Diabetic Adults Using Wearable Sensor"],"prefix":"10.3390","volume":"20","author":[{"given":"Lilia","family":"Aljihmani","sequence":"first","affiliation":[{"name":"Department of Electrical &amp; Computer Engineering, Texas A &amp; M University at Qatar, Doha 23874, Qatar"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Oussama","family":"Kerdjidj","sequence":"additional","affiliation":[{"name":"Department of Electrical &amp; Computer Engineering, Texas A &amp; M University at Qatar, Doha 23874, Qatar"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yibo","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of Industrial &amp; Systems Engineering, Texas A &amp; M University, College Station, TX 77843, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8254-8365","authenticated-orcid":false,"given":"Ranjana K.","family":"Mehta","sequence":"additional","affiliation":[{"name":"Department of Industrial &amp; Systems Engineering, Texas A &amp; M University, College Station, TX 77843, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0017-5866","authenticated-orcid":false,"given":"Madhav","family":"Erraguntla","sequence":"additional","affiliation":[{"name":"Department of Industrial &amp; Systems Engineering, Texas A &amp; M University, College Station, TX 77843, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9962-5470","authenticated-orcid":false,"given":"Farzan","family":"Sasangohar","sequence":"additional","affiliation":[{"name":"Department of Industrial &amp; Systems Engineering, Texas A &amp; M University, College Station, TX 77843, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Khalid","family":"Qaraqe","sequence":"additional","affiliation":[{"name":"Department of Electrical &amp; Computer Engineering, Texas A &amp; M University at Qatar, Doha 23874, Qatar"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,12,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1016\/S0165-0270(97)02251-6","article-title":"Measurement of human muscle fatigue","volume":"74","year":"1997","journal-title":"J. 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