  Existing work in human activity detection classifies physical activities
using a single fixed-length subset of a sensor signal. However, temporally
consecutive subsets of a sensor signal are not utilized. This is not optimal
for classifying physical activities (composite activities) that are composed of
a temporal series of simpler activities (atomic activities). A sport consists
of physical activities combined in a fashion unique to that sport. The
constituent physical activities and the sport are not fundamentally different.
We propose a computational graph architecture for human activity detection
based on the readings of a triaxial accelerometer. The resulting model learns
1) a representation of the atomic activities of a sport and 2) to classify
physical activities as compositions of the atomic activities. The proposed
model, alongside with a set of baseline models, was tested for a simultaneous
classification of eight physical activities (walking, nordic walking, running,
soccer, rowing, bicycling, exercise bicycling and lying down). The proposed
model obtained an overall mean accuracy of 77.91% (population) and 95.28%
(personalized). The corresponding accuracies of the best baseline model were
73.52% and 90.03%. However, without combining consecutive atomic activities,
the corresponding accuracies of the proposed model were 71.52% and 91.22%. The
results show that our proposed model is accurate, outperforms the baseline
models and learns to combine simple activities into complex activities.
Composite activities can be classified as combinations of atomic activities.
Our proposed architecture is a basis for accurate models in human activity
detection.
