Can Ensemble Deep Learning Identify People by Their Gait Using Data Collected from Multi-Modal Sensors in Their Insole?

Moon, Jucheol; Minaya, Nelson Hebert; Le, Nhat Anh; Park, Hee-Chan; Choi, Sang-Il

Moon, Jucheol; Minaya, Nelson Hebert; Le, Nhat Anh; Park, Hee-Chan; Choi, Sang-Il.

Afiliación

Moon J; Department of Computer Engineering and Computer Science, California State University, Long Beach, CA 90840, USA.
Minaya NH; Department of Computer Engineering and Computer Science, California State University, Long Beach, CA 90840, USA.
Le NA; Department of Computer Engineering and Computer Science, California State University, Long Beach, CA 90840, USA.
Park HC; Department of Computer Science and Engineering, Dankook University, Yongin-si 16890, Korea.
Choi SI; Department of Computer Science and Engineering, Dankook University, Yongin-si 16890, Korea.

Sensors (Basel) ; 20(14)2020 Jul 18.

Article en En | MEDLINE | ID: mdl-32708442

Asunto(s)

Identificación Biométrica; Aprendizaje Profundo; Análisis de la Marcha; Zapatos; Dispositivos Electrónicos Vestibles; Biometría; Humanos

Palabras clave

deep learning; gait analysis; multi-modality; user identification; wearable sensors

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Zapatos / Identificación Biométrica / Dispositivos Electrónicos Vestibles / Análisis de la Marcha / Aprendizaje Profundo Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: Sensors (Basel) Año: 2020 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Suiza

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