Semi-Markov kMeans Clustering and Activity Recognition from Body-Worn Sensors

Subsequence clustering aims to find patterns that appear repeatedly in time series data. We introduce a novel subsequence clustering technique that we call semi-Markov kmeans clustering. The clustering results in ideal examples of the repeating patterns and in labeled segmentations that can be used as training data for sophisticated discriminative methods like max-margin semi-Markov models. We are applying the new clustering technique to activity recognition from body-worn sensors by showing how it can enable a system to learn from data that is only annotated by an ordered list of activity types that have been undertaken. This kind of annotation, unlike a detailed segmentation of the sensor data, is easily provided by a non-expert user. We show that we can achieve equally good results using only an ordered list of activity types for training as when using a full detailed labeled segmentation

Methods for engineering symbolic human behaviour models for activity recognition

This work investigates the ability of symbolic models to encode context information that is later used for generating probabilistic models for activity recognition. The contributions of the work are as follows: it shows that it is possible to successfully use symbolic models for activity recognition; it provides a modelling toolkit that contains patterns for reducing the model complexity; it proposes a structured development process for building and evaluating computational causal behaviour models