  We address the well-known wearable activity recognition problem of having to
work with sensors that are non-optimal in terms of information they provide but
have to be used due to wearability/usability concerns (e.g. the need to work
with wrist-worn IMUs because they are embedded in most smart watches). To
mitigate this problem we propose a method that facilitates the use of
information from sensors that are only present during the training process and
are unavailable during the later use of the system. The method transfers
information from the source sensors to the latent representation of the target
sensor data through contrastive loss that is combined with the classification
loss during joint training. We evaluate the method on the well-known PAMAP2 and
Opportunity benchmarks for different combinations of source and target sensors
showing average (over all activities) F1 score improvements of between 5% and
13% with the improvement on individual activities, particularly well suited to
benefit from the additional information going up to between 20% and 40%.
