Motion based adaptive step length estimation using smartphone

Author

Jung Ho Lee ; Beomju Shin ; Seok Lee Jae Hun Kim ; Chulki Kim ; Taikjin Lee ; Jinwoo Park

Author_Institution

Sensor Syst. Res. Center, Korea Inst. of Sci. & Technol., Seoul, South Korea

fYear

2014

fDate

22-25 June 2014

Firstpage

1

Lastpage

2

Abstract

This paper presents a motion recognition based step length estimation algorithm using smartphone. Motion of a user is identified based on the hybrid model of Decision Tree (DT), Artificial Neural Network (ANN) and Support Vector Machine (SVM). The parameters of linear combination based step length model are adapted based on the result motion recognition. In order to verify the proposed algorithm, we performed experiments on 5 subjects and showed accuracy of step length estimation as RMSE.

Keywords

image motion analysis; image recognition; neural nets; smart phones; support vector machines; artificial neural network; decision tree; motion based adaptive step length estimation; motion recognition; smart phone; step length estimation algorithm; support vector machine; Acceleration; Accelerometers; Artificial neural networks; Estimation; Legged locomotion; Medical services; Support vector machines; healthcare; motion; navigation; smartphone; step length;

fLanguage

English

Publisher

ieee

Conference_Titel

Consumer Electronics (ISCE 2014), The 18th IEEE International Symposium on

Conference_Location

JeJu Island

Type

conf

DOI

10.1109/ISCE.2014.6884456

Filename

6884456