Learning Behavioral Representations of Routines From Large-scale
  Unlabeled Wearable Time-series Data Streams using Hawkes Point Process
Tiantian Feng Brandon M Booth Shrikanth Narayanan
Continuously-worn wearable sensors enable researchers to collect copious
amounts of rich bio-behavioral time series recordings of real-life activities
of daily living, offering unprecedented opportunities to infer novel human
behavior patterns during daily routines. Existing approaches to routine
discovery through bio-behavioral data rely either on pre-defined notions of
activities or use additional non-behavioral measurements as contexts, such as
GPS location or localization within the home, presenting risks to user privacy.
In this work, we propose a novel wearable time-series mining framework, Hawkes
point process On Time series clusters for ROutine Discovery (HOT-ROD), for
uncovering behavioral routines from completely unlabeled wearable recordings.
We utilize a covariance-based method to generate time-series clusters and
discover routines via the Hawkes point process learning algorithm. We
empirically validate our approach for extracting routine behaviors using a
completely unlabeled time-series collected continuously from over 100
individuals both in and outside of the workplace during a period of ten weeks.
Furthermore, we demonstrate this approach intuitively captures daily
transitional relationships between physical activity states without using prior
knowledge. We also show that the learned behavioral patterns can assist in
illuminating an individual's personality and affect.