Force Myography-Based Human Robot Interactions via Deep Domain Adaptation and Generalization.
applied force estimation in dynamic motion
domain adaptation
domain generalization
force myography technique
pretrained model
transfer learning
Journal
Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366
Informations de publication
Date de publication:
29 Dec 2021
29 Dec 2021
Historique:
received:
15
11
2021
revised:
25
12
2021
accepted:
27
12
2021
entrez:
11
1
2022
pubmed:
12
1
2022
medline:
13
1
2022
Statut:
epublish
Résumé
Estimating applied force using force myography (FMG) technique can be effective in human-robot interactions (HRI) using data-driven models. A model predicts well when adequate training and evaluation are observed in same session, which is sometimes time consuming and impractical. In real scenarios, a pretrained transfer learning model predicting forces quickly once fine-tuned to target distribution would be a favorable choice and hence needs to be examined. Therefore, in this study a unified supervised FMG-based deep transfer learner (SFMG-DTL) model using CNN architecture was pretrained with multiple sessions FMG source data (D
Identifiants
pubmed: 35009752
pii: s22010211
doi: 10.3390/s22010211
pmc: PMC8749939
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
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