TY - GEN
T1 - Pedestrian activity detection in a multi-floor environment by a smart phone
AU - Lo, Chi Chung
AU - Chen, Yi Hsiu
AU - Tseng, Yu-Chee
AU - Huang, Shang Ming
AU - Hung, Yu Neng
AU - Tseng, Chiu Mei
AU - Ho, Yeh Chin
PY - 2012/12/1
Y1 - 2012/12/1
N2 - Indoor localization has attracted considerable attention recently. One approach is to use inertial sensors mounted on pedestrians to characterize the users' motions. However, few studies have focused on a multi floor environment where users' activities may include walking, running, and going up/down stairs. This paper proposes a lightweight activity detection system using inertial sensors on a smart phone to detect the behaviors of a pedestrian in the multi-floor indoor environment. The system first identifies strides using the accelerations values. It then uses the displacement, duration, and acceleration to classify their types. Our experimental results show that the stride detection accuracy is about 99%. In addition, the types of strides, namely walking, running, going upstairs, and going downstairs, can be detected with the accuracy of 94%, 91%, 95%, and 92%, respectively.
AB - Indoor localization has attracted considerable attention recently. One approach is to use inertial sensors mounted on pedestrians to characterize the users' motions. However, few studies have focused on a multi floor environment where users' activities may include walking, running, and going up/down stairs. This paper proposes a lightweight activity detection system using inertial sensors on a smart phone to detect the behaviors of a pedestrian in the multi-floor indoor environment. The system first identifies strides using the accelerations values. It then uses the displacement, duration, and acceleration to classify their types. Our experimental results show that the stride detection accuracy is about 99%. In addition, the types of strides, namely walking, running, going upstairs, and going downstairs, can be detected with the accuracy of 94%, 91%, 95%, and 92%, respectively.
UR - http://www.scopus.com/inward/record.url?scp=84873941464&partnerID=8YFLogxK
U2 - 10.1109/ICSENS.2012.6411387
DO - 10.1109/ICSENS.2012.6411387
M3 - Conference contribution
AN - SCOPUS:84873941464
SN - 9781457717659
T3 - Proceedings of IEEE Sensors
BT - IEEE SENSORS 2012 - Proceedings
T2 - 11th IEEE SENSORS 2012 Conference
Y2 - 28 October 2012 through 31 October 2012
ER -