  Passive and non-obtrusive health monitoring using wearables can potentially
bring new insights into the user's health status throughout the day and may
support clinical diagnosis and treatment. However, identifying segments of
free-living data that sufficiently reflect the user's health is challenging. In
this work we have studied the problem of modelling real-life gait which is a
very indicative behaviour for multiple movement disorders including Parkinson's
disease (PD). We have developed a probabilistic framework for unsupervised
analysis of the gait, clustering it into different types, which can be used to
evaluate gait abnormalities occurring in daily life. Using a unique dataset
which contains sensor and video recordings of people with and without PD in
their own living environment, we show that our model driven approach achieves
high accuracy gait detection and can capture clinical improvement after
medication intake.
