  Passive monitoring in daily life may provide invaluable insights about a
person's health throughout the day. Wearable sensor devices are likely to play
a key role in enabling such monitoring in a non-obtrusive fashion. However,
sensor data collected in daily life reflects multiple health and behavior
related factors together. This creates the need for structured principled
analysis to produce reliable and interpretable predictions that can be used to
support clinical diagnosis and treatment. In this work we develop a principled
modelling approach for free-living gait (walking) analysis. Gait is a promising
target for non-obtrusive monitoring because it is common and indicative of
various movement disorders such as Parkinson's disease (PD), yet its analysis
has largely been limited to experimentally controlled lab settings. To locate
and characterize stationary gait segments in free living using accelerometers,
we present an unsupervised statistical framework designed to segment signals
into differing gait and non-gait patterns. Our flexible probabilistic framework
combines empirical assumptions about gait into a principled graphical model
with all of its merits. We demonstrate the approach on a new video-referenced
dataset including unscripted daily living activities of 25 PD patients and 25
controls, in and around their own houses. We evaluate our ability to detect
gait and predict medication induced fluctuations in PD patients based on
modelled gait. Our evaluation includes a comparison between sensors attached at
multiple body locations including wrist, ankle, trouser pocket and lower back.
