Robust Indoor Human Activity Recognition Using Wireless Signals.
Sensors (Basel)
; 15(7): 17195-208, 2015 Jul 15.
Article
en En
| MEDLINE
| ID: mdl-26184231
ABSTRACT
Wireless signals-based activity detection and recognition technology may be complementary to the existing vision-based methods, especially under the circumstance of occlusions, viewpoint change, complex background, lighting condition change, and so on. This paper explores the properties of the channel state information (CSI) of Wi-Fi signals, and presents a robust indoor daily human activity recognition framework with only one pair of transmission points (TP) and access points (AP). First of all, some indoor human actions are selected as primitive actions forming a training set. Then, an online filtering method is designed to make actions' CSI curves smooth and allow them to contain enough pattern information. Each primitive action pattern can be segmented from the outliers of its multi-input multi-output (MIMO) signals by a proposed segmentation method. Lastly, in online activities recognition, by selecting proper features and Support Vector Machine (SVM) based multi-classification, activities constituted by primitive actions can be recognized insensitive to the locations, orientations, and speeds.
Palabras clave
Texto completo:
1
Base de datos:
MEDLINE
Asunto principal:
Tecnología Inalámbrica
/
Movimiento
Límite:
Aged
/
Humans
Idioma:
En
Revista:
Sensors (Basel)
Año:
2015
Tipo del documento:
Article
País de afiliación:
China