FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4313890951> ?p ?o ?g. }

• W4313890951 endingPage "1123" @default.
• W4313890951 startingPage "1123" @default.
• W4313890951 abstract "Falling events are a global health concern with short- and long-term physical and psychological implications, especially for the elderly population. This work aims to monitor human activity in an indoor environment and recognize falling events without requiring users to carry a device or sensor on their bodies. A sensing platform based on the transmission of a continuous wave (CW) radio-frequency (RF) probe signal was developed using general-purpose equipment. The CW probe signal is similar to the pilot subcarriers transmitted by commercial off-the-shelf WiFi devices. As a result, our methodology can easily be integrated into a joint radio sensing and communication scheme. The sensing process is carried out by analyzing the changes in phase, amplitude, and frequency that the probe signal suffers when it is reflected or scattered by static and moving bodies. These features are commonly extracted from the channel state information (CSI) of WiFi signals. However, CSI relies on complex data acquisition and channel estimation processes. Doppler radars have also been used to monitor human activity. While effective, a radar-based fall detection system requires dedicated hardware. In this paper, we follow an alternative method to characterize falling events on the basis of the Doppler signatures imprinted on the CW probe signal by a falling person. A multi-class deep learning framework for classification was conceived to differentiate falling events from other activities that can be performed in indoor environments. Two neural network models were implemented. The first is based on a long-short-term memory network (LSTM) and the second on a convolutional neural network (CNN). A series of experiments comprising 11 subjects were conducted to collect empirical data and test the system’s performance. Falls were detected with an accuracy of 92.1% for the LSTM case, while for the CNN, an accuracy rate of 92.1% was obtained. The results demonstrate the viability of human fall detection based on a radio sensing system such as the one described in this paper." @default.
• W4313890951 created "2023-01-10" @default.
• W4313890951 creator A5003549146 @default.
• W4313890951 creator A5006712184 @default.
• W4313890951 creator A5057603637 @default.
• W4313890951 date "2023-01-08" @default.
• W4313890951 modified "2023-09-30" @default.
• W4313890951 title "Deep Learning Multi-Class Approach for Human Fall Detection Based on Doppler Signatures" @default.
• W4313890951 cites W1889795651 @default.
• W4313890951 cites W2050564816 @default.
• W4313890951 cites W2103852913 @default.
• W4313890951 cites W2112796928 @default.
• W4313890951 cites W2183484971 @default.
• W4313890951 cites W2270470215 @default.
• W4313890951 cites W2706629214 @default.
• W4313890951 cites W2736707111 @default.
• W4313890951 cites W2786312799 @default.
• W4313890951 cites W2790688089 @default.
• W4313890951 cites W2804356137 @default.
• W4313890951 cites W2806729898 @default.
• W4313890951 cites W2952065976 @default.
• W4313890951 cites W2970828833 @default.
• W4313890951 cites W2987456113 @default.
• W4313890951 cites W2999525623 @default.
• W4313890951 cites W3004787993 @default.
• W4313890951 cites W3083719185 @default.
• W4313890951 cites W3127240170 @default.
• W4313890951 cites W3179595057 @default.
• W4313890951 cites W3185080645 @default.
• W4313890951 cites W3189581706 @default.
• W4313890951 cites W3194730353 @default.
• W4313890951 cites W3201909415 @default.
• W4313890951 cites W3211334853 @default.
• W4313890951 cites W4205693476 @default.
• W4313890951 cites W4206045754 @default.
• W4313890951 cites W4206098315 @default.
• W4313890951 cites W4210407589 @default.
• W4313890951 cites W4213432563 @default.
• W4313890951 cites W4223626062 @default.
• W4313890951 cites W4226088340 @default.
• W4313890951 cites W4291910529 @default.
• W4313890951 cites W4293030613 @default.
• W4313890951 doi "https://doi.org/10.3390/ijerph20021123" @default.
• W4313890951 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36673883" @default.
• W4313890951 hasPublicationYear "2023" @default.
• W4313890951 type Work @default.
• W4313890951 citedByCount "2" @default.
• W4313890951 countsByYear W43138909512023 @default.
• W4313890951 crossrefType "journal-article" @default.
• W4313890951 hasAuthorship W4313890951A5003549146 @default.
• W4313890951 hasAuthorship W4313890951A5006712184 @default.
• W4313890951 hasAuthorship W4313890951A5057603637 @default.
• W4313890951 hasBestOaLocation W43138909511 @default.
• W4313890951 hasConcept C111919701 @default.
• W4313890951 hasConcept C121332964 @default.
• W4313890951 hasConcept C127162648 @default.
• W4313890951 hasConcept C1276947 @default.
• W4313890951 hasConcept C142757262 @default.
• W4313890951 hasConcept C154945302 @default.
• W4313890951 hasConcept C199360897 @default.
• W4313890951 hasConcept C205649164 @default.
• W4313890951 hasConcept C2779843651 @default.
• W4313890951 hasConcept C41008148 @default.
• W4313890951 hasConcept C554190296 @default.
• W4313890951 hasConcept C62649853 @default.
• W4313890951 hasConcept C761482 @default.
• W4313890951 hasConcept C76155785 @default.
• W4313890951 hasConcept C79403827 @default.
• W4313890951 hasConcept C96513508 @default.
• W4313890951 hasConcept C98045186 @default.
• W4313890951 hasConceptScore W4313890951C111919701 @default.
• W4313890951 hasConceptScore W4313890951C121332964 @default.
• W4313890951 hasConceptScore W4313890951C127162648 @default.
• W4313890951 hasConceptScore W4313890951C1276947 @default.
• W4313890951 hasConceptScore W4313890951C142757262 @default.
• W4313890951 hasConceptScore W4313890951C154945302 @default.
• W4313890951 hasConceptScore W4313890951C199360897 @default.
• W4313890951 hasConceptScore W4313890951C205649164 @default.
• W4313890951 hasConceptScore W4313890951C2779843651 @default.
• W4313890951 hasConceptScore W4313890951C41008148 @default.
• W4313890951 hasConceptScore W4313890951C554190296 @default.
• W4313890951 hasConceptScore W4313890951C62649853 @default.
• W4313890951 hasConceptScore W4313890951C761482 @default.
• W4313890951 hasConceptScore W4313890951C76155785 @default.
• W4313890951 hasConceptScore W4313890951C79403827 @default.
• W4313890951 hasConceptScore W4313890951C96513508 @default.
• W4313890951 hasConceptScore W4313890951C98045186 @default.
• W4313890951 hasIssue "2" @default.
• W4313890951 hasLocation W43138909511 @default.
• W4313890951 hasLocation W43138909512 @default.
• W4313890951 hasLocation W43138909513 @default.
• W4313890951 hasOpenAccess W4313890951 @default.
• W4313890951 hasPrimaryLocation W43138909511 @default.
• W4313890951 hasRelatedWork W2056825543 @default.
• W4313890951 hasRelatedWork W2133875105 @default.
• W4313890951 hasRelatedWork W2135254854 @default.
• W4313890951 hasRelatedWork W2352492056 @default.
• W4313890951 hasRelatedWork W2359317704 @default.

• next