Enhanced human activity recognition in medical emergencies using a hybrid deep CNN and bi-directional LSTM model with wearable sensors

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Location: Deutsche Nationalbibliothek Frankfurt am Main

Extent: 1 Online-Ressource.

Language: Englisch

Bibliographic citation: 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

Classification: Ingenieurwissenschaften und Maschinenbau

Creator: Chandramouli, Nishanth Adithya
Natarajan, Sivaramakrishnan
Alharbi, Amal H.
Kannan, Subhash
Khafaga, Doaa Sami
Raju, Sekar Kidambi
Eid, Marwa M.
El-kenawy, El-Sayed M.

Contributor: SpringerLink (Online service)

DOI: 10.1038/s41598-024-82045-y

URN: urn:nbn:de:101:1-2503062111122.756524924207

Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 15.08.2025, 7:36 AM CEST

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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.
SpringerLink (Online service)

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Enhanced human activity recognition in medical emergencies using a hybrid deep CNN and bi-directional LSTM model with wearable sensors

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