Joint segmentation of multivariate time series with hidden process regression for human activity recognition
We present a novel way for automatic human activity recognition from acceleration data. It is based on a well-established statistical latent data model. It works in an entirely unsupervised way and does not require feature extraction preprocessing. It is assessed on a real-world automatic human activity recognition problem.
hidden process regression human activity recognition acceleration data times series segmentation unsupervised learning expectationmaximization algorithm