  Sensor data streams from wearable devices and smart environments are widely
studied in areas like human activity recognition (HAR), person identification,
or health monitoring. However, most of the previous works in activity and
sensor stream analysis have been focusing on one aspect of the data, e.g. only
recognizing the type of the activity or only identifying the person who
performed the activity. We instead propose an approach that uses a weakly
supervised multi-output siamese network that learns to map the data into
multiple representation spaces, where each representation space focuses on one
aspect of the data. The representation vectors of the data samples are
positioned in the space such that the data with the same semantic meaning in
that aspect are closely located to each other. Therefore, as demonstrated with
a set of experiments, the trained model can provide metrics for clustering data
based on multiple aspects, allowing it to address multiple tasks simultaneously
and even to outperform single task supervised methods in many situations. In
addition, further experiments are presented that in more detail analyze the
effect of the architecture and of using multiple tasks within this framework,
that investigate the scalability of the model to include additional tasks, and
that demonstrate the ability of the framework to combine data for which only
partial relationship information with respect to the target tasks is available.
