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A database of physical therapy exercises with variability of execution collected by wearable sensors

dc.contributor.authorGarcía-de-Villa, Sara
dc.contributor.authorJiménez-Martín, Ana
dc.contributor.authorGarcía-Domínguez, Juan Jesús
dc.date.accessioned2024-01-09T09:57:34Z
dc.date.available2024-01-09T09:57:34Z
dc.date.issued2022-06-03
dc.identifier.citationGarcía-de-Villa, S., Jiménez-Martín, A. & García-Domínguez, J.J. A database of physical therapy exercises with variability of execution collected by wearable sensors. Sci Data 9, 266 (2022). https://doi.org/10.1038/s41597-022-01387-2es
dc.identifier.issn2052-4463
dc.identifier.urihttps://hdl.handle.net/10115/28292
dc.descriptionThe authors would like to thank Andrea Martínez-Parra and F. Javier Redondo-García for their collaboration in the measurement campaign and data processing, and the volunteers that performed the exercises to create this database. This work was supported by Junta de Comunidades de Castilla La Mancha (FrailCheck SBPLY/17/180501/000392), the Spanish Ministry of Science, Innovation and Universities (MICROCEBUS RTI2018-095168-B-C51) and Comunidad de Madrid (RACC CM/JIN/2021-016).es
dc.description.abstractThis document introduces the PHYTMO database, which contains data from physical therapies recorded with inertial sensors, including information from an optical reference system. PHYTMO includes the recording of 30 volunteers, aged between 20 and 70 years old. A total amount of 6 exercises and 3 gait variations were recorded. The volunteers performed two series with a minimum of 8 repetitions in each one. PHYTMO includes magneto-inertial data, together with a highly accurate location and orientation in the 3D space provided by the optical system. The files were stored in CSV format to ensure its usability. The aim of this dataset is the availability of data for two main purposes: the analysis of techniques for the identification and evaluation of exercises using inertial sensors and the validation of inertial sensor-based algorithms for human motion monitoring. Furthermore, the database stores enough data to apply Machine Learning-based algorithms. The participants’ age range is large enough to establish age-based metrics for the exercises evaluation or the study of differences in motions between different groups.es
dc.language.isoenges
dc.publisherNaturees
dc.rightsAtribución 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectTurn ratees
dc.subjectSpecific forcees
dc.subjectMagnetic fieldes
dc.subjectLocationses
dc.subjectOrientationses
dc.subjectInertial measurement unites
dc.subjectStereophotogrammetric systemes
dc.subjectHumanses
dc.subjectQuality of lifees
dc.subjectRehabilitationes
dc.titleA database of physical therapy exercises with variability of execution collected by wearable sensorses
dc.typeinfo:eu-repo/semantics/articlees
dc.identifier.doi10.1038/s41597-022-01387-2es
dc.rights.accessRightsinfo:eu-repo/semantics/embargoedAccesses


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