@inproceedings{02154075bf8b45f786952fae5228e126,
title = "Predictive Models in Biomechanics",
abstract = "This paper investigates the opportunity of predictive musculoskeletal models that do not require experimental input of kinematics and ground reaction forces. First, the requirements of such models are reviewed and, subsequent-ly, an example model of running is derived by means of principal component analysis. The generation of different running styles using the model is demonstrated, and we conclude that this type of models has the potential to predict motion behavior given shallow input describing the individual.",
keywords = "Musculoskeletal models, Running, Statistics, Principal Component Analysis",
author = "John Rasmussen",
year = "2018",
month = sep,
day = "5",
doi = "10.1007/978-3-319-97286-2_9",
language = "English",
isbn = "978-3-319-97285-5",
series = "Advances in Intelligent Systems and Computing",
publisher = "Springer",
pages = "98 -- 106",
editor = "Katarzyna Arkusz and Romuald Bedzinski and Tomasz Klekiel and Szczepan Piszczatowski",
booktitle = "Biomechanics in Medicine and Biology",
address = "Germany",
edition = "1",
note = "International Conference of the Polish Society of Biomechanics ; Conference date: 05-09-2018 Through 07-09-2018",
}