{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T21:14:13Z","timestamp":1762809253021,"version":"build-2065373602"},"reference-count":77,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2022,4,14]],"date-time":"2022-04-14T00:00:00Z","timestamp":1649894400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Horizon 2020 Framework Programme of the European Union for Research and Innovation","award":["826304"],"award-info":[{"award-number":["826304"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Physical exercise (PE) is beneficial for both physical and psychological health aspects. However, excessive training can lead to physical fatigue and an increased risk of lower limb injuries. In order to tailor training loads and durations to the needs and capacities of an individual, physical fatigue must be estimated. Different measurement devices and techniques (i.e., ergospirometers, electromyography, and motion capture systems) can be used to identify physical fatigue. The field of biomechanics has succeeded in capturing changes in human movement with optical systems, as well as with accelerometers or inertial measurement units (IMUs), the latter being more user-friendly and adaptable to real-world scenarios due to its wearable nature. There is, however, still a lack of consensus regarding the possibility of using biomechanical parameters measured with accelerometers to identify physical fatigue states in PE. Nowadays, the field of biomechanics is beginning to open towards the possibility of identifying fatigue state using machine learning algorithms. Here, we selected and summarized accelerometer-based articles that either (a) performed analyses of biomechanical parameters that change due to fatigue in the lower limbs or (b) performed fatigue identification based on features including biomechanical parameters. We performed a systematic literature search and analysed 39 articles on running, jumping, walking, stair climbing, and other gym exercises. Peak tibial and sacral acceleration were the most common measured variables and were found to significantly increase with fatigue (respectively, in 6\/13 running articles and 2\/4 jumping articles). Fatigue classification was performed with an accuracy between 78% and 96% and Pearson\u2019s correlation with an RPE (rate of perceived exertion) between r = 0.79 and r = 0.95. We recommend future effort toward the standardization of fatigue protocols and methods across articles in order to generalize fatigue identification results and increase the use of accelerometers to quantify physical fatigue in PE.<\/jats:p>","DOI":"10.3390\/s22083008","type":"journal-article","created":{"date-parts":[[2022,4,19]],"date-time":"2022-04-19T02:39:31Z","timestamp":1650335971000},"page":"3008","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Accelerometer-Based Identification of Fatigue in the Lower Limbs during Cyclical Physical Exercise: A Systematic Review"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2269-298X","authenticated-orcid":false,"given":"Luca","family":"Marotta","sequence":"first","affiliation":[{"name":"Roessingh Research and Development, 7522 AH Enschede, The Netherlands"},{"name":"Department of Biomedical Signals and Systems, Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS), University of Twente, 7522 NB Enschede, The Netherlands"}]},{"given":"Bouke L.","family":"Scheltinga","sequence":"additional","affiliation":[{"name":"Roessingh Research and Development, 7522 AH Enschede, The Netherlands"},{"name":"Department of Biomedical Signals and Systems, Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS), University of Twente, 7522 NB Enschede, The Netherlands"}]},{"given":"Robbert","family":"van Middelaar","sequence":"additional","affiliation":[{"name":"Department of Biomedical Signals and Systems, Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS), University of Twente, 7522 NB Enschede, The Netherlands"}]},{"given":"Wichor M.","family":"Bramer","sequence":"additional","affiliation":[{"name":"Medical Library, Erasmus University Medical Center, 3000 CA Rotterdam, The Netherlands"}]},{"given":"Bert-Jan F.","family":"van Beijnum","sequence":"additional","affiliation":[{"name":"Department of Biomedical Signals and Systems, Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS), University of Twente, 7522 NB Enschede, The Netherlands"}]},{"given":"Jasper","family":"Reenalda","sequence":"additional","affiliation":[{"name":"Roessingh Research and Development, 7522 AH Enschede, The Netherlands"},{"name":"Department of Biomedical Signals and Systems, Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS), University of Twente, 7522 NB Enschede, The Netherlands"}]},{"given":"Jaap H.","family":"Buurke","sequence":"additional","affiliation":[{"name":"Roessingh Research and Development, 7522 AH Enschede, The Netherlands"},{"name":"Department of Biomedical Signals and Systems, Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS), University of Twente, 7522 NB Enschede, The Netherlands"},{"name":"Roessingh Rehabilitation Centre, 7522 AH Enschede, The Netherlands"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"509","DOI":"10.3389\/fpsyg.2018.00509","article-title":"Effects of Physical Exercise on Cognitive Functioning and Wellbeing: Biological and Psychological Benefits","volume":"9","author":"Mandolesi","year":"2018","journal-title":"Front. 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