FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4210756681> ?p ?o ?g. }

• W4210756681 endingPage "12" @default.
• W4210756681 startingPage "1" @default.
• W4210756681 abstract "According to statistics, stroke is the second or third leading cause of death and adult disability. Stroke causes losing control of the motor function, paralysis of body parts, and severe back pain for which a physiotherapist employs many therapies to restore the mobility needs of everyday life. This research article presents an automated approach to detect different therapy exercises performed by stroke patients during rehabilitation. The detection of rehabilitation exercise is a complex area of human activity recognition (HAR). Due to numerous achievements and increasing popularity of deep learning (DL) techniques, in this research article a DL model that combines convolutional neural network (CNN) and long short-term memory (LSTM) is proposed and is named as 3-Layer CNN-LSTM model. The dataset is collected through RGB (red, green, and blue) camera under the supervision of a physiotherapist, which is resized in the preprocessing stage. The 3-layer CNN-LSTM model takes preprocessed data at the convolutional layer. The convolutional layer extracts useful features from input data. The extracted features are then processed by adjusting weights through fully connected (FC) layers. The FC layers are followed by the LSTM layer. The LSTM layer further processes this data to learn its spatial and temporal dynamics. For comparison, we trained CNN model over the prescribed dataset and achieved 89.9% accuracy. The conducted experimental examination shows that the 3-Layer CNN-LSTM outperforms CNN and KNN algorithm and achieved 91.3% accuracy." @default.
• W4210756681 created "2022-02-08" @default.
• W4210756681 creator A5016996520 @default.
• W4210756681 creator A5019813284 @default.
• W4210756681 creator A5049447073 @default.
• W4210756681 creator A5054926386 @default.
• W4210756681 creator A5067743408 @default.
• W4210756681 creator A5080228493 @default.
• W4210756681 date "2022-02-04" @default.
• W4210756681 modified "2023-10-01" @default.
• W4210756681 title "Automated Detection of Rehabilitation Exercise by Stroke Patients Using 3-Layer CNN-LSTM Model" @default.
• W4210756681 cites W1549517954 @default.
• W4210756681 cites W1982333761 @default.
• W4210756681 cites W2060280062 @default.
• W4210756681 cites W2103522741 @default.
• W4210756681 cites W2115667691 @default.
• W4210756681 cites W2121812409 @default.
• W4210756681 cites W2139049236 @default.
• W4210756681 cites W2153896900 @default.
• W4210756681 cites W2158764002 @default.
• W4210756681 cites W2314394422 @default.
• W4210756681 cites W2325859395 @default.
• W4210756681 cites W2371638596 @default.
• W4210756681 cites W2582594972 @default.
• W4210756681 cites W2761551360 @default.
• W4210756681 cites W2778455075 @default.
• W4210756681 cites W2790046776 @default.
• W4210756681 cites W2803636599 @default.
• W4210756681 cites W2810474558 @default.
• W4210756681 cites W2911053187 @default.
• W4210756681 cites W2946659740 @default.
• W4210756681 cites W2969535679 @default.
• W4210756681 cites W2979737317 @default.
• W4210756681 cites W2980448754 @default.
• W4210756681 cites W2982001680 @default.
• W4210756681 cites W2989064674 @default.
• W4210756681 cites W3004500412 @default.
• W4210756681 cites W3011785450 @default.
• W4210756681 cites W3013330736 @default.
• W4210756681 cites W3016856208 @default.
• W4210756681 cites W3087714553 @default.
• W4210756681 cites W3091268974 @default.
• W4210756681 cites W3100321043 @default.
• W4210756681 cites W3124221386 @default.
• W4210756681 cites W3143391165 @default.
• W4210756681 cites W3157219911 @default.
• W4210756681 cites W3193585534 @default.
• W4210756681 cites W3197315056 @default.
• W4210756681 cites W3204369957 @default.
• W4210756681 cites W99952337 @default.
• W4210756681 doi "https://doi.org/10.1155/2022/1563707" @default.
• W4210756681 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35154616" @default.
• W4210756681 hasPublicationYear "2022" @default.
• W4210756681 type Work @default.
• W4210756681 citedByCount "4" @default.
• W4210756681 countsByYear W42107566812022 @default.
• W4210756681 countsByYear W42107566812023 @default.
• W4210756681 crossrefType "journal-article" @default.
• W4210756681 hasAuthorship W4210756681A5016996520 @default.
• W4210756681 hasAuthorship W4210756681A5019813284 @default.
• W4210756681 hasAuthorship W4210756681A5049447073 @default.
• W4210756681 hasAuthorship W4210756681A5054926386 @default.
• W4210756681 hasAuthorship W4210756681A5067743408 @default.
• W4210756681 hasAuthorship W4210756681A5080228493 @default.
• W4210756681 hasBestOaLocation W42107566811 @default.
• W4210756681 hasConcept C108583219 @default.
• W4210756681 hasConcept C119857082 @default.
• W4210756681 hasConcept C127413603 @default.
• W4210756681 hasConcept C153180895 @default.
• W4210756681 hasConcept C154945302 @default.
• W4210756681 hasConcept C178790620 @default.
• W4210756681 hasConcept C185592680 @default.
• W4210756681 hasConcept C1862650 @default.
• W4210756681 hasConcept C2778818304 @default.
• W4210756681 hasConcept C2779227376 @default.
• W4210756681 hasConcept C2780645631 @default.
• W4210756681 hasConcept C34736171 @default.
• W4210756681 hasConcept C41008148 @default.
• W4210756681 hasConcept C71924100 @default.
• W4210756681 hasConcept C78519656 @default.
• W4210756681 hasConcept C81363708 @default.
• W4210756681 hasConcept C82990744 @default.
• W4210756681 hasConceptScore W4210756681C108583219 @default.
• W4210756681 hasConceptScore W4210756681C119857082 @default.
• W4210756681 hasConceptScore W4210756681C127413603 @default.
• W4210756681 hasConceptScore W4210756681C153180895 @default.
• W4210756681 hasConceptScore W4210756681C154945302 @default.
• W4210756681 hasConceptScore W4210756681C178790620 @default.
• W4210756681 hasConceptScore W4210756681C185592680 @default.
• W4210756681 hasConceptScore W4210756681C1862650 @default.
• W4210756681 hasConceptScore W4210756681C2778818304 @default.
• W4210756681 hasConceptScore W4210756681C2779227376 @default.
• W4210756681 hasConceptScore W4210756681C2780645631 @default.
• W4210756681 hasConceptScore W4210756681C34736171 @default.
• W4210756681 hasConceptScore W4210756681C41008148 @default.
• W4210756681 hasConceptScore W4210756681C71924100 @default.
• W4210756681 hasConceptScore W4210756681C78519656 @default.
• W4210756681 hasConceptScore W4210756681C81363708 @default.

• next