Matches in SemOpenAlex for { <https://semopenalex.org/work/W4322746413> ?p ?o ?g. }
Showing items 1 to 82 of
82
with 100 items per page.
- W4322746413 endingPage "182" @default.
- W4322746413 startingPage "164" @default.
- W4322746413 abstract "Reducing spinal loads using exoskeletons has become one of the optimal solution in reducing compression of the lumbar spine. Medical research has proved that the reduction compression of the lumbar spine is a key risk factor for musculoskeletal injuries. In this paper we present a deep learning based approach which is aimed at increasing the universality of lower back support for the exoskeletons with automatic control strategy. Our approach is aimed at solving the problem of recognizing human intentions in a lower-back exoskeleton using deep learning. To train and evaluate our approach deep learning model, we collected dataset using from wearable sensors, such as IMU. Our deep learning model is a Long short-term memory neural network which forecasts next values of 6 angles. The mean squared error and coefficient of determination are used for evaluation of the model. Using mean squared error and coefficient of determination we evaluated our model on dataset comprised of 700 samples and achieved performance of 0.3 and 0.99 for MSE and $$R^2$$ , respectively." @default.
- W4322746413 created "2023-03-03" @default.
- W4322746413 creator A5023540132 @default.
- W4322746413 creator A5024080016 @default.
- W4322746413 creator A5033040150 @default.
- W4322746413 date "2023-01-01" @default.
- W4322746413 modified "2023-09-25" @default.
- W4322746413 title "Deep Learning Based Approach for Human Intention Estimation in Lower-Back Exoskeleton" @default.
- W4322746413 cites W2063598276 @default.
- W4322746413 cites W2101032778 @default.
- W4322746413 cites W2145343602 @default.
- W4322746413 cites W2172156083 @default.
- W4322746413 cites W2526225066 @default.
- W4322746413 cites W2620371446 @default.
- W4322746413 cites W2736191430 @default.
- W4322746413 cites W2807119323 @default.
- W4322746413 cites W2810474558 @default.
- W4322746413 cites W2914615046 @default.
- W4322746413 cites W2946654283 @default.
- W4322746413 cites W2963563124 @default.
- W4322746413 cites W2964203186 @default.
- W4322746413 cites W2964474109 @default.
- W4322746413 cites W2979994277 @default.
- W4322746413 cites W3080612571 @default.
- W4322746413 cites W3083323811 @default.
- W4322746413 cites W3095743069 @default.
- W4322746413 cites W3101794397 @default.
- W4322746413 cites W3121094196 @default.
- W4322746413 cites W3130291524 @default.
- W4322746413 cites W3181089280 @default.
- W4322746413 cites W3202535612 @default.
- W4322746413 cites W4242738563 @default.
- W4322746413 doi "https://doi.org/10.1007/978-3-031-28073-3_12" @default.
- W4322746413 hasPublicationYear "2023" @default.
- W4322746413 type Work @default.
- W4322746413 citedByCount "0" @default.
- W4322746413 crossrefType "book-chapter" @default.
- W4322746413 hasAuthorship W4322746413A5023540132 @default.
- W4322746413 hasAuthorship W4322746413A5024080016 @default.
- W4322746413 hasAuthorship W4322746413A5033040150 @default.
- W4322746413 hasConcept C105795698 @default.
- W4322746413 hasConcept C108583219 @default.
- W4322746413 hasConcept C119857082 @default.
- W4322746413 hasConcept C139945424 @default.
- W4322746413 hasConcept C146549078 @default.
- W4322746413 hasConcept C149635348 @default.
- W4322746413 hasConcept C150594956 @default.
- W4322746413 hasConcept C154945302 @default.
- W4322746413 hasConcept C33923547 @default.
- W4322746413 hasConcept C41008148 @default.
- W4322746413 hasConcept C44154836 @default.
- W4322746413 hasConcept C50644808 @default.
- W4322746413 hasConceptScore W4322746413C105795698 @default.
- W4322746413 hasConceptScore W4322746413C108583219 @default.
- W4322746413 hasConceptScore W4322746413C119857082 @default.
- W4322746413 hasConceptScore W4322746413C139945424 @default.
- W4322746413 hasConceptScore W4322746413C146549078 @default.
- W4322746413 hasConceptScore W4322746413C149635348 @default.
- W4322746413 hasConceptScore W4322746413C150594956 @default.
- W4322746413 hasConceptScore W4322746413C154945302 @default.
- W4322746413 hasConceptScore W4322746413C33923547 @default.
- W4322746413 hasConceptScore W4322746413C41008148 @default.
- W4322746413 hasConceptScore W4322746413C44154836 @default.
- W4322746413 hasConceptScore W4322746413C50644808 @default.
- W4322746413 hasLocation W43227464131 @default.
- W4322746413 hasOpenAccess W4322746413 @default.
- W4322746413 hasPrimaryLocation W43227464131 @default.
- W4322746413 hasRelatedWork W3014300295 @default.
- W4322746413 hasRelatedWork W3164822677 @default.
- W4322746413 hasRelatedWork W4223943233 @default.
- W4322746413 hasRelatedWork W4225161397 @default.
- W4322746413 hasRelatedWork W4250304930 @default.
- W4322746413 hasRelatedWork W4312200629 @default.
- W4322746413 hasRelatedWork W4360585206 @default.
- W4322746413 hasRelatedWork W4364306694 @default.
- W4322746413 hasRelatedWork W4380075502 @default.
- W4322746413 hasRelatedWork W4380086463 @default.
- W4322746413 isParatext "false" @default.
- W4322746413 isRetracted "false" @default.
- W4322746413 workType "book-chapter" @default.