  Activity recognition using built-in sensors in smart and wearable devices
provides great opportunities to understand and detect human behavior in the
wild and gives a more holistic view of individuals' health and well being.
Numerous computational methods have been applied to sensor streams to recognize
different daily activities. However, most methods are unable to capture
different layers of activities concealed in human behavior. Also, the
performance of the models starts to decrease with increasing the number of
activities. This research aims at building a hierarchical classification with
Neural Networks to recognize human activities based on different levels of
abstraction. We evaluate our model on the Extrasensory dataset; a dataset
collected in the wild and containing data from smartphones and smartwatches. We
use a two-level hierarchy with a total of six mutually exclusive labels namely,
"lying down", "sitting", "standing in place", "walking", "running", and
"bicycling" divided into "stationary" and "non-stationary". The results show
that our model can recognize low-level activities (stationary/non-stationary)
with 95.8% accuracy and overall accuracy of 92.8% over six labels. This is 3%
above our best performing baseline.
