FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3204207455> ?p ?o ?g. }

• W3204207455 endingPage "138143" @default.
• W3204207455 startingPage "138132" @default.
• W3204207455 abstract "With recent advances in the field of sensing, it has become possible to build better assistive technologies. This enables the strengthening of eldercare with regard to daily routines and the provision of personalised care to users. For instance, it is possible to detect a person’s behaviour based on wearable or ambient sensors; however, it is difficult for users to wear devices 24/7, as they would have to be recharged regularly because of their energy consumption. Similarly, although cameras have been widely used as ambient sensors, they carry the risk of breaching users’ privacy. This paper presents a novel sensing approach based on deep learning for human activity recognition using a non-wearable ultra-wideband (UWB) radar sensor. UWB sensors protect privacy better than RGB cameras because they do not collect visual data. In this study, UWB sensors were mounted on a mobile robot to monitor and observe subjects from a specific distance (namely, 1.5–2.0 m). Initially, data were collected in a lab environment for five different human activities. Subsequently, the data were used to train a model using the state-of-the-art deep learning approach, namely long short-term memory (LSTM). Conventional training approaches were also tested to validate the superiority of LSTM. As a UWB sensor collects many data points in a single frame, enhanced discriminant analysis was used to reduce the dimensions of the features through application of principal component analysis to the raw dataset, followed by linear discriminant analysis. The enhanced discriminant features were fed into the LSTMs. Finally, the trained model was tested using new inputs. The proposed LSTM-based activity recognition approach performed better than conventional approaches, with an accuracy of 99.6%. We applied 5-fold cross-validation to test our approach. We also validated our approach on publically available dataset. The proposed method can be applied in many prominent fields, including human–robot interaction for various practical applications, such as mobile robots for eldercare." @default.
• W3204207455 created "2021-10-11" @default.
• W3204207455 creator A5044746485 @default.
• W3204207455 creator A5051442365 @default.
• W3204207455 creator A5071144214 @default.
• W3204207455 date "2021-01-01" @default.
• W3204207455 modified "2023-10-15" @default.
• W3204207455 title "Ultra-Wideband Radar-Based Activity Recognition Using Deep Learning" @default.
• W3204207455 cites W1517879342 @default.
• W3204207455 cites W1930624869 @default.
• W3204207455 cites W1970693311 @default.
• W3204207455 cites W1988790447 @default.
• W3204207455 cites W2015200434 @default.
• W3204207455 cites W2017194250 @default.
• W3204207455 cites W2020648835 @default.
• W3204207455 cites W2026168302 @default.
• W3204207455 cites W2032160998 @default.
• W3204207455 cites W2049498328 @default.
• W3204207455 cites W2049662336 @default.
• W3204207455 cites W2062950526 @default.
• W3204207455 cites W2064675550 @default.
• W3204207455 cites W2090269566 @default.
• W3204207455 cites W2120081246 @default.
• W3204207455 cites W2121647436 @default.
• W3204207455 cites W2123504417 @default.
• W3204207455 cites W2133218851 @default.
• W3204207455 cites W2135643018 @default.
• W3204207455 cites W2139212933 @default.
• W3204207455 cites W2155655319 @default.
• W3204207455 cites W2195342085 @default.
• W3204207455 cites W2324333583 @default.
• W3204207455 cites W2507653100 @default.
• W3204207455 cites W2548875745 @default.
• W3204207455 cites W2653764441 @default.
• W3204207455 cites W2769087576 @default.
• W3204207455 cites W2792396140 @default.
• W3204207455 cites W279795307 @default.
• W3204207455 cites W2883594635 @default.
• W3204207455 cites W2889114526 @default.
• W3204207455 cites W2897704812 @default.
• W3204207455 cites W2911964244 @default.
• W3204207455 cites W2939888035 @default.
• W3204207455 cites W2944605902 @default.
• W3204207455 cites W2945695009 @default.
• W3204207455 cites W2965479259 @default.
• W3204207455 cites W2979856235 @default.
• W3204207455 cites W2992424090 @default.
• W3204207455 cites W2998019206 @default.
• W3204207455 cites W2998271236 @default.
• W3204207455 cites W3013964275 @default.
• W3204207455 cites W3017078789 @default.
• W3204207455 cites W3019086662 @default.
• W3204207455 cites W3025823157 @default.
• W3204207455 cites W3030949666 @default.
• W3204207455 cites W3034226296 @default.
• W3204207455 cites W3034433427 @default.
• W3204207455 cites W3039573164 @default.
• W3204207455 cites W3039610325 @default.
• W3204207455 cites W3082167482 @default.
• W3204207455 cites W3095176739 @default.
• W3204207455 cites W4239510810 @default.
• W3204207455 cites W2088881590 @default.
• W3204207455 doi "https://doi.org/10.1109/access.2021.3117667" @default.
• W3204207455 hasPublicationYear "2021" @default.
• W3204207455 type Work @default.
• W3204207455 sameAs 3204207455 @default.
• W3204207455 citedByCount "12" @default.
• W3204207455 countsByYear W32042074552022 @default.
• W3204207455 countsByYear W32042074552023 @default.
• W3204207455 crossrefType "journal-article" @default.
• W3204207455 hasAuthorship W3204207455A5044746485 @default.
• W3204207455 hasAuthorship W3204207455A5051442365 @default.
• W3204207455 hasAuthorship W3204207455A5071144214 @default.
• W3204207455 hasBestOaLocation W32042074551 @default.
• W3204207455 hasConcept C108583219 @default.
• W3204207455 hasConcept C111919701 @default.
• W3204207455 hasConcept C119857082 @default.
• W3204207455 hasConcept C121687571 @default.
• W3204207455 hasConcept C149635348 @default.
• W3204207455 hasConcept C150594956 @default.
• W3204207455 hasConcept C154945302 @default.
• W3204207455 hasConcept C186967261 @default.
• W3204207455 hasConcept C202444582 @default.
• W3204207455 hasConcept C31972630 @default.
• W3204207455 hasConcept C33923547 @default.
• W3204207455 hasConcept C41008148 @default.
• W3204207455 hasConcept C52622490 @default.
• W3204207455 hasConcept C54290928 @default.
• W3204207455 hasConcept C554190296 @default.
• W3204207455 hasConcept C69738355 @default.
• W3204207455 hasConcept C76155785 @default.
• W3204207455 hasConcept C9652623 @default.
• W3204207455 hasConceptScore W3204207455C108583219 @default.
• W3204207455 hasConceptScore W3204207455C111919701 @default.
• W3204207455 hasConceptScore W3204207455C119857082 @default.
• W3204207455 hasConceptScore W3204207455C121687571 @default.
• W3204207455 hasConceptScore W3204207455C149635348 @default.
• W3204207455 hasConceptScore W3204207455C150594956 @default.

• next