A wearable action recognition system based on acceleration and attitude angles using real-time detection algorithm.
Annu Int Conf IEEE Eng Med Biol Soc
; 2017: 2385-2389, 2017 Jul.
Article
en En
| MEDLINE
| ID: mdl-29060378
ABSTRACT
Falls are a main cause of trauma and death. The purpose of this study is to adopt unique resultant acceleration and attitude angles to distinguish falls from activities of daily life before impact. In this study, we developed a wearable action recognition system to acquire action data. The moving average filter was employed to deal with raw data, and then complementary filter was adopted to compromise sensor data for attitude angles. The real-time detection algorithm embedded in this device was applied to recognize six actions based on processed data. Eight subjects (five males, three females) participated in the experiment. The optimal features and related thresholds were extracted. In addition, the real-time action detection results indicated that the real-time action recognition model reached an accuracy of 96.25%, with 98% for male and 93.3% for female. Thus, our device potentially achieves a high sensitivity of fall-related actions recognition.
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1
Bases de datos:
MEDLINE
Asunto principal:
Dispositivos Electrónicos Vestibles
Tipo de estudio:
Diagnostic_studies
/
Prognostic_studies
Límite:
Female
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Humans
/
Male
Idioma:
En
Revista:
Annu Int Conf IEEE Eng Med Biol Soc
Año:
2017
Tipo del documento:
Article