  Human Activity Recognition (HAR) is a powerful tool for understanding human
behaviour. Applying HAR to wearable sensors can provide new insights by
enriching the feature set in health studies, and enhance the personalisation
and effectiveness of health, wellness, and fitness applications. Wearable
devices provide an unobtrusive platform for user monitoring, and due to their
increasing market penetration, feel intrinsic to the wearer. The integration of
these devices in daily life provide a unique opportunity for understanding
human health and wellbeing. This is referred to as the "quantified self"
movement. The analyses of complex health behaviours such as sleep,
traditionally require a time-consuming manual interpretation by experts. This
manual work is necessary due to the erratic periodicity and persistent
noisiness of human behaviour. In this paper, we present a robust automated
human activity recognition algorithm, which we call RAHAR. We test our
algorithm in the application area of sleep research by providing a novel
framework for evaluating sleep quality and examining the correlation between
the aforementioned and an individual's physical activity. Our results improve
the state-of-the-art procedure in sleep research by 15 percent for area under
ROC and by 30 percent for F1 score on average. However, application of RAHAR is
not limited to sleep analysis and can be used for understanding other health
problems such as obesity, diabetes, and cardiac diseases.
