Scalable Deep Learning for Pilot Performance Analysis Using Multimodal Physiological Time Series

Sensors used to collect human physiological data often necessitate the processing and classification of time series data, which can quickly become intractable with very lengthy inputs or many time series features. In this study we compared the performance of two methods of time series feature extraction and dimensionality reduction, Minimally Random Convolutional Kernel Transform (MiniRocket) and statistical feature engineering using TSFresh, to determine the optimal hardware configurations and associated performance-accuracy trade-offs between model speed and complexity. Our results showed that MiniRocket scales extremely well with only linear complexity while the scaling of TSFresh is dependent on the set of features selected for computation. Further, MiniRocket outperformed the TSFresh model accuracy for all configurations except the most comprehensive (but slowest) feature extraction set thereby highlighting MiniRocket as a great all-purpose dimensionality reduction tool for human physiological time series data.