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Open AccessProceedings

Collection of Kinematic and Kinetic Data of Young and Adult, Male and Female Subjects Performing Periodic and Transient Gait Tasks for Gait Pattern Recognition

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Department of Industrial Engineering, University of Padua, 35121 Padua, Italy
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Department of Information Engineering, University of Padua, 35121 Padua, Italy
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Joint Veteran Center, Scientific Department, Army Medical Center, 00184 Rome, Italy
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DIAG, Sapienza University of Rome, 00185 Rome, Italy
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MES S.p.A. Meccanica per l’Elettronica e Servomeccanismi, 00131 Rome, Italy
*
Author to whom correspondence should be addressed.
Presented at the 13th conference of the International Sports Engineering Association, Online, 22–26 June 2020.
Proceedings 2020, 49(1), 6; https://doi.org/10.3390/proceedings2020049006
Published: 15 June 2020
The aim of the study was to develop a database of biomechanical data for multiple gait tasks. This database will be used to create a real-time gait pattern classifier that will be implemented in a new-generation active knee prosthesis. With this intent, we collected kinematic and kinetic data of 40 subjects performing 16 gait tasks, categorized as periodic and transient motions. We analyzed four distinct sub-populations, differentiated by age and gender. As the classifier will be based also on inertial data, we chose to synthesize these signals within the motion capture environment. To assess the effects of gender and age we performed a correlation analysis on the signals used as input of the classifier. The results obtained indicate that there is no need to differentiate into four distinct classes for the development of the classifier. Sample data of the dataset are made publicly available.
Keywords: database; populations; multiple gait-task; classifier; correlation; virtual IMU database; populations; multiple gait-task; classifier; correlation; virtual IMU
  • Externally hosted supplementary file 1
    Doi: 10.5281/zenodo.3628229
    Link: http://doi.org/10.5281/zenodo.3628229
    Description: This document contains supplementary material for the article “Collection of kinematic and kinetic data of young & adult, male & female subjects performing periodic and transient gait tasks for gait pattern recognition” (Authors: Paolo Mistretta, Cecilia Marchesini, Andrea Volpini, Luca Tagliapietra, Tommaso Sciarra, Aldo Lazich, Salvatore Forte, Mauro De Matteis, Emanuele Menegatti and Nicola Petrone) presented at the 13th conference of the International Sports Engineering Association, Tokyo, Japan, 22-25 June 2020. Data are contained in the file: “database_ISEA2020.mat”
MDPI and ACS Style

Mistretta, P.; Marchesini, C.; Volpini, A.; Tagliapietra, L.; Sciarra, T.; Lazich, A.; Forte, S.; Matteis, M.D.; Menegatti, E.; Petrone, N. Collection of Kinematic and Kinetic Data of Young and Adult, Male and Female Subjects Performing Periodic and Transient Gait Tasks for Gait Pattern Recognition. Proceedings 2020, 49, 6. https://doi.org/10.3390/proceedings2020049006

AMA Style

Mistretta P, Marchesini C, Volpini A, Tagliapietra L, Sciarra T, Lazich A, Forte S, Matteis MD, Menegatti E, Petrone N. Collection of Kinematic and Kinetic Data of Young and Adult, Male and Female Subjects Performing Periodic and Transient Gait Tasks for Gait Pattern Recognition. Proceedings. 2020; 49(1):6. https://doi.org/10.3390/proceedings2020049006

Chicago/Turabian Style

Mistretta, Paolo; Marchesini, Cecilia; Volpini, Andrea; Tagliapietra, Luca; Sciarra, Tommaso; Lazich, Aldo; Forte, Salvatore; Matteis, Mauro De; Menegatti, Emanuele; Petrone, Nicola. 2020. "Collection of Kinematic and Kinetic Data of Young and Adult, Male and Female Subjects Performing Periodic and Transient Gait Tasks for Gait Pattern Recognition" Proceedings 49, no. 1: 6. https://doi.org/10.3390/proceedings2020049006

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