  Wearable devices including accelerometers are increasingly being used to
collect high-frequency human activity data in situ. There is tremendous
potential to use such data to inform medical decision making and public health
policies. However, modeling such data is challenging as they are
high-dimensional, heterogeneous, and subject to informative missingness, e.g.,
zero readings when the device is removed by the participant. We propose a
flexible and extensible continuous-time hidden Markov model to extract
meaningful activity patterns from human accelerometer data. To facilitate
estimation with massive data we derive an efficient learning algorithm that
exploits the hierarchical structure of the parameters indexing the proposed
model. We also propose a bootstrap procedure for interval estimation. The
proposed methods are illustrated using data from the 2003 - 2004 and 2005 -
2006 National Health and Nutrition Examination Survey.
