TY - JOUR
T1 - Step length estimation and activity detection in a PDR system based on a fuzzy model with inertial sensors
AU - Ibarra-Bonilla, Mariana Natalia
AU - Escamilla-Ambrosio, Ponciano Jorge
AU - Ramirez-Cortes, Juan Manuel
AU - Rangel-Magdaleno, Jose
AU - Gomez-Gil, Pilar
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2014.
PY - 2014
Y1 - 2014
N2 - This chapter presents an approach on pedestrian dead reckoning (PDR) which incorporates activity classification over a fuzzy inference system (FIS) for step length estimation. In the proposed algorithm, the pedestrian is equipped with an inertial measurement unit attached to the waist, which provides three-axis accelerometer and gyroscope signals. The main goal is to integrate the activity classification and step-length estimation algorithms into a PDR system. In order to improve the step-length estimation, several types of activities are classified using a multi-layer perceptron (MLP) neural network with feature extraction based on statistical parameters from wavelet decomposition. This work focuses on classifying activities that a pedestrian performs routinely in his daily life, such as walking, walking fast, jogging and running. The step-length is dynamically estimated using a multiple-input–single-output (MISO) fuzzy inference system. Results provide an average classification rate of 87.49% with an accuracy on steplength estimation about 92.57% in average.
AB - This chapter presents an approach on pedestrian dead reckoning (PDR) which incorporates activity classification over a fuzzy inference system (FIS) for step length estimation. In the proposed algorithm, the pedestrian is equipped with an inertial measurement unit attached to the waist, which provides three-axis accelerometer and gyroscope signals. The main goal is to integrate the activity classification and step-length estimation algorithms into a PDR system. In order to improve the step-length estimation, several types of activities are classified using a multi-layer perceptron (MLP) neural network with feature extraction based on statistical parameters from wavelet decomposition. This work focuses on classifying activities that a pedestrian performs routinely in his daily life, such as walking, walking fast, jogging and running. The step-length is dynamically estimated using a multiple-input–single-output (MISO) fuzzy inference system. Results provide an average classification rate of 87.49% with an accuracy on steplength estimation about 92.57% in average.
UR - http://www.scopus.com/inward/record.url?scp=84927126884&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-05170-3_45
DO - 10.1007/978-3-319-05170-3_45
M3 - Artículo
AN - SCOPUS:84927126884
SN - 1860-949X
VL - 547
SP - 631
EP - 645
JO - Studies in Computational Intelligence
JF - Studies in Computational Intelligence
ER -