@inproceedings{b97565eab7dc4dd7a082b0fc2d32eedd,
title = "Motion based adaptive step length estimation using smartphone",
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 = "healthcare, motion, navigation, smartphone, step length",
author = "Lee, {Jung Ho} and Beomju Shin and Seok Lee and Kim, {Jae Hun} and Chulki Kim and Taikjin Lee and Jinwoo Park",
year = "2014",
doi = "10.1109/ISCE.2014.6884456",
language = "English",
isbn = "9781479945924",
series = "Proceedings of the International Symposium on Consumer Electronics, ISCE",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "ISCE 2014 - 18th IEEE International Symposium on Consumer Electronics",
note = "18th IEEE International Symposium on Consumer Electronics, ISCE 2014 ; Conference date: 22-06-2014 Through 25-06-2014",
}