Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/83872
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Campo DC | Valor | Idioma |
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dc.contributor.author | Monteiro, Sara | por |
dc.contributor.author | Figueiredo, Joana | por |
dc.contributor.author | Santos, Cristina | por |
dc.date.accessioned | 2023-04-06T15:35:57Z | - |
dc.date.available | 2023-04-06T15:35:57Z | - |
dc.date.issued | 2023-04-03 | - |
dc.identifier.isbn | 979-8-3503-0121-2 | - |
dc.identifier.uri | https://hdl.handle.net/1822/83872 | - |
dc.description.abstract | There is evidence that the energy expended by humans can be reduced by wearing lower limb exoskeletons with user-oriented assistance strategies, such as human-in-theloop (HITL) controllers. HITL algorithms can be implemented in exoskeletons for the automatic and online optimization of controller parameters, such as the torque profile, depending on the energy expenditure (EE) measured in real-time. This way, it is possible to minimize the EE and tailor the exoskeleton assistance for each specific user. But measuring EE is not trivial. It is more commonly estimated by indirect calorimetry, however, this method requires expensive equipment, takes too long, and is infeasible for everyday use in the real world. Therefore, this study explores machine and deep learning regression models (RMs) as EE estimators in different motor activities based on data acquired by wearable sensors and anthropometric features. Several inputs were tested but the best performance was achieved by the heart rate, the 3-axis acceleration of the chest, wrist, thigh, and ankle, and the body mass index. Results from a public dataset are presented, after the preprocessing of the data. The bestperforming RM was an exponential Gaussian process regressor (GPR), that obtained root-mean-squared errors of 0.56 W/kg, 0.45 W/kg, and 0.60 W/kg for the standing, sitting, and walking activities, respectively. The GPR model outperformed a support vector machine, a boosted decision tree, a bagged decision tree, and a convolutional neural network. | por |
dc.description.sponsorship | COMPETE 2020—Programa Operacional Competitividade e Internacionalização (POCI) and P2020 with the Reference Project SmartOs Grant POCI-01-0247-FEDER-039868. | por |
dc.language.iso | eng | por |
dc.relation | info:eu-repo/grantAgreement/FCT/CEEC IND 3ed/2020.03393.CEECIND%2FCP1600%2FCT0011/PT | por |
dc.relation | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04436%2F2020/PT | por |
dc.relation | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F04436%2F2020/PT | por |
dc.rights | openAccess | por |
dc.subject | Artificial intelligence | por |
dc.subject | Energy expenditure | por |
dc.subject | Human-in-the-loop control | por |
dc.subject | Exoskeleton assistance | por |
dc.subject | Wearable sensors | por |
dc.title | Towards a more efficient human-exoskeleton assistance | por |
dc.type | conferencePaper | por |
dc.peerreviewed | yes | por |
oaire.citationStartPage | 181 | por |
oaire.citationEndPage | 186 | por |
dc.identifier.doi | 10.1109/ICARSC58346.2023.10129556 | por |
dc.subject.fos | Engenharia e Tecnologia::Engenharia Médica | por |
sdum.conferencePublication | 23rd IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC2023) | por |
oaire.version | AM | por |
dc.subject.ods | Saúde de qualidade | por |
Aparece nas coleções: | CMEMS - Artigos em livros de atas/Papers in proceedings |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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Towards a more efficient human-exoskeleton assistance.pdf | 402,64 kB | Adobe PDF | Ver/Abrir |