Enhanced human activity recognition in medical emergencies using a hybrid deep CNN and bi-directional LSTM model with wearable sensors
- Standort
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Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
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1 Online-Ressource.
- Sprache
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Englisch
- Erschienen in
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Enhanced human activity recognition in medical emergencies using a hybrid deep CNN and bi-directional LSTM model with wearable sensors ; volume:14 ; number:1 ; day:28 ; month:12 ; year:2024 ; pages:1-24 ; date:12.2024
Scientific reports ; 14, Heft 1 (28.12.2024), 1-24, 12.2024
- Klassifikation
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Ingenieurwissenschaften und Maschinenbau
- Urheber
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Chandramouli, Nishanth Adithya
Natarajan, Sivaramakrishnan
Alharbi, Amal H.
Kannan, Subhash
Khafaga, Doaa Sami
Raju, Sekar Kidambi
Eid, Marwa M.
El-kenawy, El-Sayed M.
- Beteiligte Personen und Organisationen
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SpringerLink (Online service)
- DOI
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10.1038/s41598-024-82045-y
- URN
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urn:nbn:de:101:1-2503062111122.756524924207
- Rechteinformation
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
-
15.08.2025, 07:36 MESZ
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Beteiligte
- Chandramouli, Nishanth Adithya
- Natarajan, Sivaramakrishnan
- Alharbi, Amal H.
- Kannan, Subhash
- Khafaga, Doaa Sami
- Raju, Sekar Kidambi
- Eid, Marwa M.
- El-kenawy, El-Sayed M.
- SpringerLink (Online service)
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