Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/74424
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Campo DC | Valor | Idioma |
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dc.contributor.author | Moreira, Luís Carlos Rodrigues | por |
dc.contributor.author | Figueiredo, Joana | por |
dc.contributor.author | Vilas-Boas, João Paulo | por |
dc.contributor.author | Santos, Cristina | por |
dc.date.accessioned | 2021-10-19T13:25:21Z | - |
dc.date.available | 2021-10-19T13:25:21Z | - |
dc.date.issued | 2021-08-06 | - |
dc.identifier.citation | Moreira, L.; Figueiredo, J.; Vilas-Boas, J.P.; Santos, C.P. Kinematics, Speed, and Anthropometry-Based Ankle Joint Torque Estimation: A Deep Learning Regression Approach. Machines 2021, 9, 154. https://doi.org/10.3390/machines9080154 | por |
dc.identifier.issn | 2075-1702 | - |
dc.identifier.uri | https://hdl.handle.net/1822/74424 | - |
dc.description.abstract | Powered Assistive Devices (PADs) have been proposed to enable repetitive, user-oriented gait rehabilitation. They may include torque controllers that typically require reference joint torque trajectories to determine the most suitable level of assistance. However, a robust approach able to automatically estimate user-oriented reference joint torque trajectories, namely ankle torque, while considering the effects of varying walking speed, body mass, and height on the gait dynamics, is needed. This study evaluates the accuracy and generalization ability of two Deep Learning (DL) regressors (Long-Short Term Memory and Convolutional Neural Network (CNN)) to generate user-oriented reference ankle torque trajectories by innovatively customizing them according to the walking speed (ranging from 1.0 to 4.0 km/h) and users’ body height and mass (ranging from 1.51 to 1.83 m and 52.0 to 83.7 kg, respectively). Furthermore, this study hypothesizes that DL regressors can estimate joint torque without resourcing electromyography signals. CNN was the most robust algorithm (Normalized Root Mean Square Error: 0.70 ± 0.06; Spearman Correlation: 0.89 ± 0.03; Coefficient of Determination: 0.91 ± 0.03). No statistically significant differences were found in CNN accuracy (<i>p</i>-value > 0.05) whether electromyography signals are included as inputs or not, enabling a less obtrusive and accurate setup for torque estimation. | por |
dc.description.sponsorship | This work was funded in part by the Fundação para a Ciência e Tecnologia (FCT) with the Reference Scholarship under Grant 2020.05711.BD, and in part by the FEDER Funds through the COMPETE 2020—Programa Operacional Competitividade e Internacionalização (POCI) and P2020 with the Reference Project SmartOs Grant POCI-01-0247-FEDER-039868, and by FCT national funds, under the national support to R&D units grant, through the reference project UIDB/04436/2020 and UIDP/04436/2020. | por |
dc.language.iso | eng | por |
dc.publisher | Multidisciplinary Digital Publishing Institute | por |
dc.relation | POCI-01-0247-FEDER-039868 | por |
dc.relation | UIDB/04436/2020 | por |
dc.relation | UIDP/04436/2020 | por |
dc.rights | openAccess | por |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | por |
dc.subject | Ankle joint torque estimation | por |
dc.subject | Deep learning regression | por |
dc.subject | Electromyography | por |
dc.subject | Smart machines | por |
dc.subject | Human motion analysis | por |
dc.title | Kinematics, speed, and anthropometry-based ankle joint torque estimation: a deep learning regression approach | por |
dc.type | article | por |
dc.peerreviewed | yes | por |
dc.relation.publisherversion | https://www.mdpi.com/2075-1702/9/8/154 | por |
oaire.citationStartPage | 1 | por |
oaire.citationEndPage | 18 | por |
oaire.citationIssue | 8 | por |
oaire.citationVolume | 9 | por |
dc.date.updated | 2021-08-26T13:27:21Z | - |
dc.identifier.doi | 10.3390/machines9080154 | por |
dc.subject.wos | Science & Technology | por |
sdum.journal | Machines | por |
oaire.version | VoR | por |
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machines-09-00154-v2.pdf | 3,42 MB | Adobe PDF | Ver/Abrir |
Este trabalho está licenciado sob uma Licença Creative Commons