  We introduce statistical methods for predicting the types of human activity
at sub-second resolution using triaxial accelerometry data. The major
innovation is that we use labeled activity data from some subjects to predict
the activity labels of other subjects. To achieve this, we normalize the data
across subjects by matching the standing up and lying down portions of triaxial
accelerometry data. This is necessary to account for differences between the
variability in the position of the device relative to gravity, which are
induced by body shape and size as well as by the ambiguous definition of device
placement. We also normalize the data at the device level to ensure that the
magnitude of the signal at rest is similar across devices. After normalization
we use overlapping movelets (segments of triaxial accelerometry time series)
extracted from some of the subjects to predict the movement type of the other
subjects. The problem was motivated by and is applied to a laboratory study of
20 older participants who performed different activities while wearing
accelerometers at the hip. Prediction results based on other people's labeled
dictionaries of activity performed almost as well as those obtained using their
own labeled dictionaries. These findings indicate that prediction of activity
types for data collected during natural activities of daily living may actually
be possible.
