  Few-shot learning is a technique to learn a model with a very small amount of
labeled training data by transferring knowledge from relevant tasks. In this
paper, we propose a few-shot learning method for wearable sensor based human
activity recognition, a technique that seeks high-level human activity
knowledge from low-level sensor inputs. Due to the high costs to obtain human
generated activity data and the ubiquitous similarities between activity modes,
it can be more efficient to borrow information from existing activity
recognition models than to collect more data to train a new model from scratch
when only a few data are available for model training. The proposed few-shot
human activity recognition method leverages a deep learning model for feature
extraction and classification while knowledge transfer is performed in the
manner of model parameter transfer. In order to alleviate negative transfer, we
propose a metric to measure cross-domain class-wise relevance so that knowledge
of higher relevance is assigned larger weights during knowledge transfer.
Promising results in extensive experiments show the advantages of the proposed
approach.
