@inproceedings{b765fa0246bf42d3ac746b8b88f552b1,
title = "Vaulting Detection with the Multi-Model Unscented Kalman Filter",
abstract = "A method for automatically detecting vaulting gait is presented. Two gait models are parameterized based on data from humans, representing a normal gait and a vaulting gait. Structural observability analysis is performed taking into account measurements from a wearable sensor package. A multi-model Unscented Kalman Filter is applied to sensor data from live trials to automatically detect the gait mode.",
author = "Macht, {Jesse P.} and Taylor, {Josh A.} and Fae Azhari",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 20th IEEE International Conference on Automation Science and Engineering, CASE 2024 ; Conference date: 28-08-2024 Through 01-09-2024",
year = "2024",
doi = "10.1109/CASE59546.2024.10711410",
language = "English (US)",
series = "IEEE International Conference on Automation Science and Engineering",
publisher = "IEEE Computer Society",
pages = "3306--3311",
booktitle = "2024 IEEE 20th International Conference on Automation Science and Engineering, CASE 2024",
address = "United States",
}