  Activities, such as walking and sitting, are commonly used in biomedical
settings either as an outcome or covariate of interest. Researchers have
traditionally relied on surveys to quantify activity levels of subjects in both
research and clinical settings, but surveys are not objective in nature and
have many known limitations, such as recall bias. Smartphones provide an
opportunity for unobtrusive objective measurement of various activities in
naturalistic settings, but their data tends to be noisy and needs to be
analyzed with care. We explored the potential of smartphone accelerometer and
gyroscope data to distinguish between five different types of activity:
walking, sitting, standing, ascending stairs, and descending stairs. We
conducted a study in which four participants followed a study protocol and
performed a sequence of various activities with one phone in their front pocket
and another phone in their back pocket. The subjects were filmed throughout,
and the obtained footage was annotated to establish ground truth activity. We
applied the so-called movelet method to classify their activity. Our results
demonstrate the promise of smartphones for activity detection in naturalistic
settings, but they also highlight common challenges in this field of research.
