An Automated Machine Learning Approach Applied to Robotic Stroke Rehabilitation

While machine learning methods have proven to be a highly valuable tool in solving numerous problems in assistive technology,state-of-the-art machine learning algorithms and corresponding results are not always accessible to assistive technology researchers due to required domain knowledge and complicated model parameters. This work explores the use of recent work in machine learning to entirely automate the machine learning pipeline, from feature extraction to classification. A nonparametrically guided autoencoder is used toextract features and perform classification while Bayesian optimization is used to automatically tune the parameters of the model for best performance. Empirical analysis is performed on a real-world rehabilitation research problem. The entirely automated approach significantly outperforms previously published results using carefully tuned machine learning algorithms on the same data.