Fall compensation detection from EEG using neuroevolution and genetic hyperparameter optimisation

Abstract

Detecting fall compensatory behaviour from large EEG datasets poses a difficult problem in big data which can be alleviated by evolutionary computation-based machine learning strategies. In this article, hyperheuristic optimisation solutions via evolutionary optimisation of deep neural network topologies and genetic programming of machine learning pipelines will be investigated. Wavelet extractions from signals recorded during physical activities present a binary problem for detecting fall compensation. The earlier results show that a Gaussian process model achieves an accuracy of 86.48%. Following this, artificial neural networks are evolved through evolutionary algorithms and score similarly to most standard models; the hyperparameters chosen are well outside the bounds of batch or manual searches. Five iterations of genetic programming scored higher than all other approaches, at a mean 90.52% accuracy. The best pipeline extracted polynomial features and performed Principal Components Analysis, before machine learning through a randomised set of decision trees, and passing the class prediction probabilities to a 72-nearest-neighbour algorithm. The best genetic solution could infer data in 0.02 s, whereas the second best genetic programming solution (89.79%) could infer data in only 0.3 ms. Graphical abstract