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

Hierarchical Continuous Time Hidden Markov Model, with Application in Zero-Inflated Accelerometer Data