  Wearable sensor based human activity recognition is a challenging problem due
to difficulty in modeling spatial and temporal dependencies of sensor signals.
Recognition models in closed-set assumption are forced to yield members of
known activity classes as prediction. However, activity recognition models can
encounter an unseen activity due to body-worn sensor malfunction or disability
of the subject performing the activities. This problem can be addressed through
modeling solution according to the assumption of open-set recognition. Hence,
the proposed self attention based approach combines data hierarchically from
different sensor placements across time to classify closed-set activities and
it obtains notable performance improvement over state-of-the-art models on five
publicly available datasets. The decoder in this autoencoder architecture
incorporates self-attention based feature representations from encoder to
detect unseen activity classes in open-set recognition setting. Furthermore,
attention maps generated by the hierarchical model demonstrate explainable
selection of features in activity recognition. We conduct extensive leave one
subject out validation experiments that indicate significantly improved
robustness to noise and subject specific variability in body-worn sensor
signals. The source code is available at:
github.com/saif-mahmud/hierarchical-attention-HAR
