@inbook{87ae116633484c34be3b9cb03c3dedf6,
title = "Classification of Older Adults with/without a Fall History Using Machine Learning Methods",
abstract = " Falling is a serious problem in an aged society such that assessment of the risk of falls for individuals is imperative for the research and practice of falls prevention. This paper introduces an application of several machine learning methods for training a classifier which is capable of classifying individual older adults into a high risk group and a low risk group (distinguished by whether or not the members of the group have a recent history of falls). Using a 3D motion capture system, significant gait features related to falls risk are extracted. By training these features, classification hypotheses are obtained based on machine learning techniques (K Nearest neighbour, Naive Bayes, Logistic Regression, Neural Network, and Support Vector Machine). Training and test accuracies with sensitivity and specificity of each of these techniques are assessed. The feature adjustment and tuning of the machine learning algorithms are discussed. The outcome of the study will benefit the prediction and prevention of falls.",
keywords = "accuracy, history, joints, kinematics, logistics, support vector machines, training",
author = "Lin Zhang and Ou Ma and Fabre, {Jennifer M.} and Wood, {Robert H.} and Garcia, {Stephanie U.} and Ivey, {Kayla M.} and McCann, {Evan D.}",
note = "Falling is a serious problem in an aged society such that assessment of the risk of falls for individuals is imperative for the research and practice of fa",
year = "2015",
doi = "10.1109/EMBC.2015.7319945",
language = "American English",
series = "Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference",
pages = "6760--3",
booktitle = "2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)",
}