Classifying human actions in daily life using computational intelligence techniques

Romina Torres, Mauricio Poblete, Rodrigo Salas

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

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Resumen

Nowadays, there are several effective computational intelligence techniques that, theoretically, could be useful to classify human daily life actions. Moreover, sensors are getting smaller, cheaper, portable and even wearable. In this paper, we have built an annotation tool by applying several computational intelligence techniques (K-Nearest Neighbor, the Support Vector Machine and the Multilayer Perceptron) to detect six types of human actions in daily life based on signals obtained from an accelerometer sensor (standing-up, walking, running, resting, jumping and sitting-down) with an accuracy over 85%. In the future, this component will be the base to infer abnormal behavior from common daily behavior that could be an emergency situation in evolution.

Idioma originalInglés
Título de la publicación alojada2017 CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, CHILECON 2017 - Proceedings
EditorialInstitute of Electrical and Electronics Engineers Inc.
Páginas1-5
Número de páginas5
ISBN (versión digital)9781538631232
DOI
EstadoPublicada - 19 dic. 2017
Publicado de forma externa
Evento2017 CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, CHILECON 2017 - Pucon, Chile
Duración: 18 oct. 201720 oct. 2017

Serie de la publicación

Nombre2017 CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, CHILECON 2017 - Proceedings
Volumen2017-January

Conferencia

Conferencia2017 CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, CHILECON 2017
País/TerritorioChile
CiudadPucon
Período18/10/1720/10/17

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