{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T15:49:59Z","timestamp":1778082599031,"version":"3.51.4"},"reference-count":31,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2021,11,16]],"date-time":"2021-11-16T00:00:00Z","timestamp":1637020800000},"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>This paper wants to stress the importance of human movement monitoring to prevent musculoskeletal disorders by proposing the WGD\u2014Working Gesture Dataset, a publicly available dataset of assembly line working gestures that aims to be used for worker\u2019s kinematic analysis. It contains kinematic data acquired from healthy subjects performing assembly line working activities using an optoelectronic motion capture system. The acquired data were used to extract quantitative indicators to assess how the working tasks were performed and to detect useful information to estimate the exposure to the factors that may contribute to the onset of musculoskeletal disorders. The obtained results demonstrate that the proposed indicators can be exploited to early detect incorrect gestures and postures and, consequently to prevent work-related disorders. The approach is general and independent on the adopted motion analysis system. It wants to provide indications for safely performing working activities. For example, the proposed WGD can also be used to evaluate the kinematics of workers in real working environments thanks to the adoption of unobtrusive measuring systems, such as wearable sensors through the extracted indicators and thresholds.<\/jats:p>","DOI":"10.3390\/s21227600","type":"journal-article","created":{"date-parts":[[2021,11,17]],"date-time":"2021-11-17T09:16:11Z","timestamp":1637140571000},"page":"7600","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["The WGD\u2014A Dataset of Assembly Line Working Gestures for Ergonomic Analysis and Work-Related Injuries Prevention"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6238-2241","authenticated-orcid":false,"given":"Christian","family":"Tamantini","sequence":"first","affiliation":[{"name":"Research Unit of Advanced Robotics and Human-Centred Technologies, Universit\u00e0 Campus Bio-Medico di Roma, 00128 Rome, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Francesca","family":"Cordella","sequence":"additional","affiliation":[{"name":"Research Unit of Advanced Robotics and Human-Centred Technologies, Universit\u00e0 Campus Bio-Medico di Roma, 00128 Rome, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0760-642X","authenticated-orcid":false,"given":"Clemente","family":"Lauretti","sequence":"additional","affiliation":[{"name":"Research Unit of Advanced Robotics and Human-Centred Technologies, Universit\u00e0 Campus Bio-Medico di Roma, 00128 Rome, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Loredana","family":"Zollo","sequence":"additional","affiliation":[{"name":"Research Unit of Advanced Robotics and Human-Centred Technologies, Universit\u00e0 Campus Bio-Medico di Roma, 00128 Rome, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"37","DOI":"10.5772\/57554","article-title":"Human hand motion analysis and synthesis of optimal power grasps for a robotic hand","volume":"11","author":"Cordella","year":"2014","journal-title":"Int. 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