Title :
Activity Recognition with sensors on mobile devices
Author :
Wei-Chih Hung ; Fan Shen ; Yi-Leh Wu ; Maw-Kae Hor ; Cheng-Yuan Tang
Author_Institution :
Dept. of Comput. Sci. & Inf. Eng., Nat. Taiwan Univ. of Sci. & Technol., Taipei, Taiwan
Abstract :
Recently, Activity Recognition (AR) has become a popular research topic and gained attention in the study field because of the increasing availability of sensors in consumer products, such as GPS sensors, vision sensors, audio sensors, light sensors, temperature sensors, direction sensors, and acceleration sensors. The availability of a variety of sensors creates many new opportunities for data mining applications. This paper proposes a mobile phone-based system that employs the accelerometer and the gyroscope signals for AR. To evaluate the proposed system, we employ a data set where 30 volunteers performed daily activities such as walking, lying, upstairs, sitting, and standing. The result shows that the features extracted from the gyroscope enhance the classification accuracy in term of dynamic activities recognition such as walking and upstairs. A comparison study shows that the recognition accuracies of the proposed framework using various classification algorithms are higher than previous works.
Keywords :
data mining; feature extraction; image motion analysis; image recognition; mobile computing; pattern classification; AR; accelerometer signal; activity recognition; classification algorithms; data mining; feature extraction; gyroscope signal; mobile devices; sensor availability; Abstracts; Gyroscopes; Humidity; Legged locomotion; Mobile handsets; Speech recognition; Support vector machines; Accelerometer; Activity Recognition; Classifier; Gyroscope; Smartphone;
Conference_Titel :
Machine Learning and Cybernetics (ICMLC), 2014 International Conference on
Conference_Location :
Lanzhou
Print_ISBN :
978-1-4799-4216-9
DOI :
10.1109/ICMLC.2014.7009650