{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T20:19:09Z","timestamp":1760473149843,"version":"build-2065373602"},"reference-count":24,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2022,8,17]],"date-time":"2022-08-17T00:00:00Z","timestamp":1660694400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Universidad Cat\u00f3lica del Maule"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Data"],"abstract":"<jats:p>Motion capture (MOCAP) is a widely used technique to record human, animal, and object movement for various applications such as animation, biomechanical assessment, and control systems. Different systems have been proposed based on diverse technologies, such as visible light cameras, infrared cameras with passive or active markers, inertial systems, or goniometer-based systems. Each system has pros and cons that make it usable in different scenarios. This paper presents a dataset that combines Optical Motion and Inertial Systems, capturing a well-known sports movement as the vertical jump. As a reference system, the optical motion capture consists of six Flex 3 Optitrack cameras with 100 FPS. On the other hand, we developed an inertial system consisting of seven custom-made devices based on the IMU MPU-9250, which includes a three-axis magnetometer, accelerometer and gyroscope, and an embedded Digital Motion Processor (DMP) attached to a microcontroller mounted on a Teensy 3.2 with an ARM Cortex-M4 processor with wireless operation using Bluetooth. The purpose of taking IMU data with a low-cost and customized system is the deployment of applications that can be performed with similar hardware and can be adjusted to different areas. The developed measurement system is flexible, and the acquisition format and enclosure can be customized. The proposed dataset comprises eight jumps recorded from four healthy humans using both systems. Experimental results on the dataset show two usage examples for measuring joint angles and COM position. The proposed dataset is publicly available online and can be used in comparative algorithms, biomechanical studies, skeleton reconstruction, sensor fusion techniques, or machine learning models.<\/jats:p>","DOI":"10.3390\/data7080116","type":"journal-article","created":{"date-parts":[[2022,8,17]],"date-time":"2022-08-17T21:23:56Z","timestamp":1660771436000},"page":"116","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Vertical Jump Data from Inertial and Optical Motion Tracking Systems"],"prefix":"10.3390","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2318-6552","authenticated-orcid":false,"given":"Mateo","family":"Rico-Garcia","sequence":"first","affiliation":[{"name":"Facultad de Ingenier\u00eda, Instituci\u00f3n Universitaria Pascual Bravo, Calle 73 No. 73A-226, Medellin 050034, Colombia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7720-8828","authenticated-orcid":false,"given":"Juan","family":"Botero-Valencia","sequence":"additional","affiliation":[{"name":"Grupo Sistemas de Control y Rob\u00f3tica, Facultad de Ingenier\u00edas, Instituto Tecnol\u00f3gico Metropolitano\u2014ITM, Calle 73 No. 76A-354, Medellin 050034, Colombia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9311-1193","authenticated-orcid":false,"given":"Ruber","family":"Hern\u00e1ndez-Garc\u00eda","sequence":"additional","affiliation":[{"name":"Research Center for Advanced Studies of Maule (CIEAM), Universidad Cat\u00f3lica del Maule, Avenida San Miguel 3605, Talca 3480094, Chile"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"D\u2019Amato, V., Volta, E., Oneto, L., Volpe, G., Camurri, A., and Anguita, D. (2021). Accuracy and intrusiveness in data-driven violin players skill levels prediction: Mocap against myo against kinect. International Work-Conference on Artificial Neural Networks, Springer.","DOI":"10.1007\/978-3-030-85099-9_30"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Ofori, E.K., Wang, S., and Bhatt, T. (2021). Validity of Inertial Sensors for Assessing Balance Kinematics and Mobility during Treadmill-Based Perturbation and Dance Training. Sensors, 21.","DOI":"10.3390\/s21093065"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Alarc\u00f3n-Aldana, A.C., Callejas-Cuervo, M., and Bo, A.P.L. (2020). Upper limb physical rehabilitation using serious videogames and motion capture systems: A systematic review. Sensors, 20.","DOI":"10.3390\/s20215989"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Stein, M., Janetzko, H., Seebacher, D., J\u00e4ger, A., Nagel, M., H\u00f6lsch, J., Kosub, S., Schreck, T., Keim, D.A., and Grossniklaus, M. 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