"The road ahead for preventive medicine seems clear. It is the delivery
of high quality, personalised (as opposed to depersonalised) comprehensive
medical care to all." Burney, Steiger, and Georges (1964)
This world's population is ageing, and this is set to intensify over the next forty years.
This demographic shift will result in signicant economic and societal burdens (partic-
ularly on healthcare systems). The instantiation of a proactive, preventative approach
to delivering healthcare is long recognised, yet is still proving challenging. Recent work
has focussed on enabling older adults to age in place in their own homes. This may
be realised through the recent technological advancements of aordable healthcare sen-
sors and systems which continuously support independent living, particularly through
longitudinally monitoring deviations in behavioural and health metrics. Overall health
status is contingent on multiple factors including, but not limited to, physical health,
mental health, and social and emotional wellbeing; sleep is implicitly linked to each of
these factors.
This thesis focusses on the investigation and development of an unobtrusive sleep mon-
itoring system, particularly suited towards long-term placement in the homes of older
adults. The Under Mattress Bed Sensor (UMBS) is an unobstrusive, pressure sensing
grid designed to infer bed times and bed exits, and also for the detection of development
of bedsores. This work extends the capacity of this sensor. Specically, the novel contri-
butions contained within this thesis focus on an in-depth review of the state-of-the-art
advances in sleep monitoring, and the development and validation of algorithms which
extract and quantify UMBS-derived sleep metrics.
Preliminary experimental and community deployments investigated the suitability of the
sensor for long-term monitoring. Rigorous experimental development rened algorithms
which extract respiration rate as well as motion metrics which outperform traditional
forms of ambulatory sleep monitoring. Spatial, temporal, statistical and spatiotemporal
features were derived from UMBS data as a means of describing movement during sleep.
These features were compared across experimental, domestic and clinical data sets, and
across multiple sleeping episodes. Lastly, the optimal classier (built using a combina-
tion of the UMBS-derived features) was shown to infer sleep/wake state accurately and
reliably across both younger and older cohorts.
Through long-term deployment, it is envisaged that the UMBS-derived features (in-
cluding spatial, temporal, statistical and spatiotemporal features, respiration rate, and
sleep/wake state) may be used to provide unobtrusive, continuous insights into over-
all health status, the progression of the symptoms of chronic conditions, and allow the
objective measurement of daily (sleep/wake) patterns and routines
