{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T19:45:20Z","timestamp":1774554320321,"version":"3.50.1"},"reference-count":37,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2018,8,14]],"date-time":"2018-08-14T00:00:00Z","timestamp":1534204800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["DUT18JC44"],"award-info":[{"award-number":["DUT18JC44"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This research on identification and classification of construction workers\u2019 activity contributes to the monitoring and management of individuals. Since a single sensor cannot meet management requirements of a complex construction environment, and integrated multiple sensors usually lack systemic flexibility and stability, this paper proposes an approach to construction-activity recognition based on smartphones. The accelerometers and gyroscopes embedded in smartphones were utilized to collect three-axis acceleration and angle data of eight main activities with relatively high frequency in simulated floor-reinforcing steel work. Data acquisition from multiple body parts enhanced the dimensionality of activity features to better distinguish between different activities. The CART algorithm of a decision tree was adopted to build a classification training model whose effectiveness was evaluated and verified through cross-validation. The results showed that the accuracy of classification for overall samples was up to 89.85% and the accuracy of prediction was 94.91%. The feasibility of using smartphones as data-acquisition tools in construction management was verified. Moreover, it was proved that the combination of a decision-tree algorithm with smartphones could achieve complex activity classification and identification.<\/jats:p>","DOI":"10.3390\/s18082667","type":"journal-article","created":{"date-parts":[[2018,8,14]],"date-time":"2018-08-14T10:31:16Z","timestamp":1534242676000},"page":"2667","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":44,"title":["Research on Construction Workers\u2019 Activity Recognition Based on Smartphone"],"prefix":"10.3390","volume":"18","author":[{"given":"Mingyuan","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Construction Management, Dalian University of Technology, Dalian 116000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuo","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Construction Management, Dalian University of Technology, Dalian 116000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuefeng","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Civil Engineering, Dalian University of Technology, Dalian 116000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhen","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Construction Management, Dalian University of Technology, Dalian 116000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,8,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1109\/MPRV.2008.40","article-title":"Wearable Activity tracking in car manufacturing","volume":"7","author":"Stiefmeier","year":"2008","journal-title":"IEEE Pervasive Comput."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.autcon.2012.08.003","article-title":"Automated task-level activity analysis through fusion of real time location sensors and worker\u2019s thoracic posture data","volume":"29","author":"Cheng","year":"2013","journal-title":"Autom. 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