JOINT ANGLE ESTIMATION DURING FAST CUTTING MANOEUVRES USING ARTIFICIAL NEURAL NETWORKS

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Athletes’ movement biomechanics are of high interest to predict injury risk. However, using a standard optical measurement set-up with cameras and force plates influences the athlete’s performance. Alternative systems such as commercial IMU systems are still jeopardised by measurement discrepancies in the analysis of joint angles. Therefore, this study aims to estimate hip, knee and ankle joint angles from simulated IMU data during the execution and depart contact of a maximum effort 90° cutting manoeuvre using a feed-forward neural network. Simulated accelerations and angular rates of the feet, shanks, thighs and pelvis as input data. The correlation coefficient between the measured and predicted data indicates strong correlations. Hence, the proposed method can be used to predict motion kinematics during a fast change of direction.
Original languageEnglish
Title of host publicationISBS - Conference Proceedings Archive : 37th Conference of the International Society of Biomechanics in Sports; ISBS2019; conference proceedings
EditorsSarah Breen, Mark Walsh, Meredith Stutz
Number of pages4
Volume1
PublisherInternational Society of Biomechanics in Sports
Publication date09.2019
Pages101 - 104
Article number23
Publication statusPublished - 09.2019
EventConference of the International Society of Biomechanics in Sports (ISBS) - Miami University , Oxford, United States
Duration: 21.07.201925.07.2019
Conference number: 37

ID: 4884829

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