  Machine learning algorithms are improving rapidly, but annotating training
data remains a bottleneck for many applications. In this paper, we show how
real data can be used for self-supervised learning without any transformations
by taking advantage of the symmetry present in the activities. Our approach
involves contrastive matching of two different sensors (left and right wrist or
leg-worn IMUs) to make representations of co-occurring sensor data more similar
and those of non-co-occurring sensor data more different. We test our approach
on the Opportunity and MM-Fit datasets. In MM-Fit we show significant
improvement over the baseline supervised and self-supervised method SimCLR,
while for Opportunity there is significant improvement over the supervised
baseline and slight improvement when compared to SimCLR. Moreover, our method
improves supervised baselines even when using only a small amount of the data
for training. Future work should explore under which conditions our method is
beneficial for human activity recognition systems and other related
applications.

Machine Learning;Signal Processing