Unsupervised Time Series Segmentation for High-Dimensional Body Sensor Network Data Streams

The vast amounts of data which can be collected using body-sensor networks with high temporal and spatial resolution require a novel analysis approach. In this context, state-of-the-art Bayesian approaches based on variational, non-parametric or MCMC derived methods often become computationally intractable when faced with several million data points. Here, we present how the simple combination of PCA, approximate Bayesian segmentation and temporal correlation processing can achieve reliable time series segmentation. We use our method, which relies on simple iterative covariance, correlation and maximum likelihood operations, to perform complex behavioural time series segmentation over millions of samples in 18 dimensions in linear time and space. Our approach is suitable for even higher dimensional data streams as performance scales near constantly with the dimensionality of the time series samples. We validate this novel approach on an artificially-generated time series and demonstrate that our method is very robust to noise and achieves a segmentation accuracy of over 86% of matching segments against ground-truth. We conclude that our approach makes Big Data driven approaches to stream processing Body Sensor Network (BSN) data tractable, and is required for BSN-driven Neurotechnology applications in Brain-Machine Interfacing and Neuroprosthetics.