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A Data Set of Human Body Movements for Physical Rehabilitation Exercises

by 1,*,†, 2,†, 2,† and 2,†
1
Industrial Technology, University of Idaho, 1776 Science Center Drive, Idaho Falls, ID 83402, USA
2
Department of Movement Sciences, University of Idaho, 875 Perimeter Drive, Moscow, ID 83844, USA
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Received: 10 November 2017 / Revised: 8 January 2018 / Accepted: 8 January 2018 / Published: 11 January 2018
The article presents University of Idaho-Physical Rehabilitation Movement Data (UI-PRMD), a publically available data set of movements related to common exercises performed by patients in physical rehabilitation programs. For the data collection, 10 healthy subjects performed 10 repetitions of different physical therapy movements with a Vicon optical tracker and a Microsoft Kinect sensor used for the motion capturing. The data are in a format that includes positions and angles of full-body joints. The objective of the data set is to provide a basis for mathematical modeling of therapy movements, as well as for establishing performance metrics for evaluation of patient consistency in executing the prescribed rehabilitation exercises. View Full-Text
Keywords: movement data set; physical therapy exercises; physical rehabilitation; motion capturing; human skeletons movement data set; physical therapy exercises; physical rehabilitation; motion capturing; human skeletons
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MDPI and ACS Style

Vakanski, A.; Jun, H.-p.; Paul, D.; Baker, R. A Data Set of Human Body Movements for Physical Rehabilitation Exercises. Data 2018, 3, 2. https://doi.org/10.3390/data3010002

AMA Style

Vakanski A, Jun H-p, Paul D, Baker R. A Data Set of Human Body Movements for Physical Rehabilitation Exercises. Data. 2018; 3(1):2. https://doi.org/10.3390/data3010002

Chicago/Turabian Style

Vakanski, Aleksandar; Jun, Hyung-pil; Paul, David; Baker, Russell. 2018. "A Data Set of Human Body Movements for Physical Rehabilitation Exercises" Data 3, no. 1: 2. https://doi.org/10.3390/data3010002

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