Accelerometer based gait analysis - Multi variate assessment of fall risk with FD-NEAT

This paper describes an accelerometer based gait analysis system for the assessment of fall risk. The assessment is based on 22 different features calculated from the signal. The different features are combined using machine learning algorithms in order to decide whether the subject has an increased fall risk. Results from Naive Bayes, Neural Networks, Locally Weighted Learning, Support Vector Machines and C4.5 are reported and compared. It is argued that the neural networks provide low accuracy results because of the high dimensionality of the feature space compared to the available data. It is shown that FD-NEAT (a method from neuro evolution which simultaneously learns the network topology, the network weights and the relevant features) outperforms the other methods in the given classification task. The system is evaluated on a database consisting of 40 elderly with known fall risk and 40 healthy elderly controls.