Collection of Kinematic and Kinetic Data of Young and Adult, Male and Female Subjects Performing Periodic and Transient Gait Tasks for Gait Pattern Recognition
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-06-01
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| Series: | Proceedings |
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| Online Access: | https://www.mdpi.com/2504-3900/49/1/6 |
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| author | Paolo Mistretta Cecilia Marchesini Andrea Volpini Luca Tagliapietra Tommaso Sciarra Aldo Lazich Salvatore Forte Mauro De Matteis Emanuele Menegatti Nicola Petrone |
| author_facet | Paolo Mistretta Cecilia Marchesini Andrea Volpini Luca Tagliapietra Tommaso Sciarra Aldo Lazich Salvatore Forte Mauro De Matteis Emanuele Menegatti Nicola Petrone |
| author_sort | Paolo Mistretta |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T19:11:12Z |
| format | Article |
| id | doaj.art-81d196f4c13c4a36a70d1436d82d6668 |
| institution | Directory Open Access Journal |
| issn | 2504-3900 |
| language | English |
| last_indexed | 2024-03-10T19:11:12Z |
| publishDate | 2020-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Proceedings |
| spelling | doaj.art-81d196f4c13c4a36a70d1436d82d66682023-11-20T03:47:53ZengMDPI AGProceedings2504-39002020-06-01491610.3390/proceedings2020049006Collection of Kinematic and Kinetic Data of Young and Adult, Male and Female Subjects Performing Periodic and Transient Gait Tasks for Gait Pattern RecognitionPaolo Mistretta0Cecilia Marchesini1Andrea Volpini2Luca Tagliapietra3Tommaso Sciarra4Aldo Lazich5Salvatore Forte6Mauro De Matteis7Emanuele Menegatti8Nicola Petrone9Department of Industrial Engineering, University of Padua, 35121 Padua, ItalyDepartment of Industrial Engineering, University of Padua, 35121 Padua, ItalyDepartment of Industrial Engineering, University of Padua, 35121 Padua, ItalyDepartment of Information Engineering, University of Padua, 35121 Padua, ItalyJoint Veteran Center, Scientific Department, Army Medical Center, 00184 Rome, ItalyJoint Veteran Center, Scientific Department, Army Medical Center, 00184 Rome, ItalyMES S.p.A. Meccanica per l’Elettronica e Servomeccanismi, 00131 Rome, ItalyMES S.p.A. Meccanica per l’Elettronica e Servomeccanismi, 00131 Rome, ItalyDepartment of Information Engineering, University of Padua, 35121 Padua, ItalyDepartment of Industrial Engineering, University of Padua, 35121 Padua, ItalyThe 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.https://www.mdpi.com/2504-3900/49/1/6databasepopulationsmultiple gait-taskclassifiercorrelationvirtual IMU |
| spellingShingle | Paolo Mistretta Cecilia Marchesini Andrea Volpini Luca Tagliapietra Tommaso Sciarra Aldo Lazich Salvatore Forte Mauro De Matteis Emanuele Menegatti Nicola Petrone 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 database populations multiple gait-task classifier correlation virtual IMU |
| title | Collection of Kinematic and Kinetic Data of Young and Adult, Male and Female Subjects Performing Periodic and Transient Gait Tasks for Gait Pattern Recognition |
| title_full | Collection of Kinematic and Kinetic Data of Young and Adult, Male and Female Subjects Performing Periodic and Transient Gait Tasks for Gait Pattern Recognition |
| title_fullStr | Collection of Kinematic and Kinetic Data of Young and Adult, Male and Female Subjects Performing Periodic and Transient Gait Tasks for Gait Pattern Recognition |
| title_full_unstemmed | Collection of Kinematic and Kinetic Data of Young and Adult, Male and Female Subjects Performing Periodic and Transient Gait Tasks for Gait Pattern Recognition |
| title_short | Collection of Kinematic and Kinetic Data of Young and Adult, Male and Female Subjects Performing Periodic and Transient Gait Tasks for Gait Pattern Recognition |
| title_sort | collection of kinematic and kinetic data of young and adult male and female subjects performing periodic and transient gait tasks for gait pattern recognition |
| topic | database populations multiple gait-task classifier correlation virtual IMU |
| url | https://www.mdpi.com/2504-3900/49/1/6 |
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