  The quality of sleep has a deep impact on people's physical and mental
health. People with insufficient sleep are more likely to report physical and
mental distress, activity limitation, anxiety, and pain. Moreover, in the past
few years, there has been an explosion of applications and devices for activity
monitoring and health tracking. Signals collected from these wearable devices
can be used to study and improve sleep quality. In this paper, we utilize the
relationship between physical activity and sleep quality to find ways of
assisting people improve their sleep using machine learning techniques. People
usually have several behavior modes that their bio-functions can be divided
into. Performing time series clustering on activity data, we find cluster
centers that would correlate to the most evident behavior modes for a specific
subject. Activity recipes are then generated for good sleep quality for each
behavior mode within each cluster. These activity recipes are supplied to an
activity recommendation engine for suggesting a mix of relaxed to intense
activities to subjects during their daily routines. The recommendations are
further personalized based on the subjects' lifestyle constraints, i.e. their
age, gender, body mass index (BMI), resting heart rate, etc, with the objective
of the recommendation being the improvement of that night's quality of sleep.
This would in turn serve a longer-term health objective, like lowering heart
rate, improving the overall quality of sleep, etc.
