  The problem of automatic identification of physical activities performed by
human subjects is referred to as Human Activity Recognition (HAR). There exist
several techniques to measure motion characteristics during these physical
activities, such as Inertial Measurement Units (IMUs). IMUs have a cornerstone
position in this context, and are characterized by usage flexibility, low cost,
and reduced privacy impact. With the use of inertial sensors, it is possible to
sample some measures such as acceleration and angular velocity of a body, and
use them to learn models that are capable of correctly classifying activities
to their corresponding classes. In this paper, we propose to use Convolutional
Neural Networks (CNNs) to classify human activities. Our models use raw data
obtained from a set of inertial sensors. We explore several combinations of
activities and sensors, showing how motion signals can be adapted to be fed
into CNNs by using different network architectures. We also compare the
performance of different groups of sensors, investigating the classification
potential of single, double and triple sensor systems. The experimental results
obtained on a dataset of 16 lower-limb activities, collected from a group of
participants with the use of five different sensors, are very promising.
