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.

Since Amazon's Echo arrived in late 2014, voice assistants have become increasingly important in making sense of the disparate smart home devices like speakers, light bulbs, thermostats, security cameras and more. But in the last few months, Alexa and the Google Assistant started letting users do multiple things -- like turning down the thermostat, lowering the lights and telling you what's on the calendar -- with a single command. It's been a key addition that has made managing a variety of different smart home devices easier. We tried out Amazon's implementation of routines back in October alongside new Echo hardware, but Google didn't launch its version until last month. If Google's going to keep Amazon from dominating the voice assistant market, it needs to be as good at tying a smart home together.