  Though research has shown the complementarity of camera- and inertial-based
data, datasets which offer both egocentric video and inertial-based sensor data
remain scarce. In this paper, we introduce WEAR, an outdoor sports dataset for
both vision- and inertial-based human activity recognition (HAR). The dataset
comprises data from 18 participants performing a total of 18 different workout
activities with untrimmed inertial (acceleration) and camera (egocentric video)
data recorded at 10 different outside locations. Unlike previous egocentric
datasets, WEAR provides a challenging prediction scenario marked by purposely
introduced activity variations as well as an overall small information overlap
across modalities. Benchmark results obtained using each modality separately
show that each modality interestingly offers complementary strengths and
weaknesses in their prediction performance. Further, in light of the recent
success of temporal action localization models following the architecture
design of the ActionFormer, we demonstrate their versatility by applying them
in a plain fashion using vision, inertial and combined (vision + inertial)
features as input. Results demonstrate both the applicability of vision-based
temporal action localization models for inertial data and fusing both
modalities by means of simple concatenation, with the combined approach (vision
+ inertial features) being able to produce the highest mean average precision
and close-to-best F1-score. The dataset and code to reproduce experiments is
publicly available via: https://mariusbock.github.io/wear/
