@inproceedings{792f1c2eba6a4c9183b28c721bb8705f,
title = "Scale fingerprint clustering-based gait tracking algorithm under high-frequency gait mode switching environment",
abstract = "The MEMS inertial accelerometer has the ability to sense the acceleration information of the human body. By making use of this ability, plus the simple mode recognition algorithm, human gait could be identified from several dynamic locomotion gait modes; however, this algorithm is only effective in a low-frequency gait mode switching situation. Once the gait mode switching frequency comes to 0.05Hz or higher, its tracking accuracy drops down to 80% or less. This paper presents a gait tracking algorithm based on clustered Reference Point (RP) fingerprint, to improve the tracking accuracy under high-frequency gait mode switching situation. By running on an Android mobile phone, the new algorithm is proved by at least 13%.",
keywords = "Clustered RP Fingerprint, High-frequency Gait Mode Switching, MEMS Inertial Accelerometer",
author = "Y. Liu and Wang, {S. L.} and L. Wang and Li, {L. L.}",
note = "Publisher Copyright: {\textcopyright} 2015 Taylor & Francis Group, London.; International Conference on Energy, Environment and Green Building Materials, EEGBM 2014 ; Conference date: 28-11-2014 Through 30-11-2014",
year = "2015",
doi = "10.1201/b18511-59",
language = "English",
isbn = "9781138027183",
series = "Energy, Environment and Green Building Materials - Proceedings of the International Conference on Energy, Environment and Green Building Materials, EEGBM 2014",
publisher = "CRC Press/Balkema",
pages = "281--284",
editor = "Ai. Sheng",
booktitle = "Energy, Environment and Green Building Materials - Proceedings of the International Conference on Energy, Environment and Green Building Materials, EEGBM 2014",
}