{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T12:14:56Z","timestamp":1777637696708,"version":"3.51.4"},"reference-count":32,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2020,9,24]],"date-time":"2020-09-24T00:00:00Z","timestamp":1600905600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,11,24]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>This paper develops computational models to monitor patients with hip replacement surgery. The Kinect camera (Xbox One) is used to capture the movements of patients who are performing rehabilitation exercises with both lower limbs, specifically, \u2018side step\u2019 and \u2018knee lift\u2019 with each leg. The information is measured at 25 body points with their respective coordinates. Features selection algorithms are applied to the 75 attributes of the initial and final position vector of each rehab exercise. Different classification techniques have been tested and Bayesian networks, supervised classifier system and genetic algorithm with neural network have been selected and jointly applied to identify the correct and incorrect movements during the execution of the rehabilitation exercises. Besides, prediction models of the evolution of a patient are developed based on the average values of some motion related variables (opening leg angle, head movement, hip movement and execution speed). These models can help to fasten the recovery of these patients.<\/jats:p>","DOI":"10.1093\/jigpal\/jzaa032","type":"journal-article","created":{"date-parts":[[2020,8,5]],"date-time":"2020-08-05T19:27:25Z","timestamp":1596655645000},"page":"874-888","source":"Crossref","is-referenced-by-count":10,"title":["Intelligent models for movement detection and physical evolution of patients with hip surgery"],"prefix":"10.1093","volume":"29","author":[{"given":"C\u00e9sar","family":"Guevara","sequence":"first","affiliation":[{"name":"Universidad Tecnol\u00f3gica Indoam\u00e9rica, Centro de investigaci\u00f3n en Mecatr\u00f3nica y Sistemas Interactivo MIST, 170301-Quito, Equador"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Matilde","family":"Santos","sequence":"additional","affiliation":[{"name":"Instituto de Tecnolog\u00eda del Conocimiento, University Complutense of Madrid, 28040-Madrid, 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