… Multi-modal HAR and knowledge distillation work are also … : A multimodal dataset for human
action recognition utilizing a … A deep learning approach to human activity recognition based …

… data but also the human domain knowledge to create handcrafted features. But it obviously
… dataset, indicating that the optimal handcrafted feature sets might be inconsistent across …

… -knowledge distillation to improve the stability of the training procedure and the performance
of human activity recognition. … data using hand-crafted features in statistical and frequency …

… devices such as smartwatches and smartphones, human action recognition based on
wearable-sensors has become a key research area in human activity understanding [9], [46]. …

… human action recognition has recently attracted much attention in the computer-vision … for
skeleton-based action recognition use handcrafted features to represent the human body [13], […

… While in simple scenarios hand-crafted features may suffice, deep learning methods have
… First, we employ a knowledge distillation paradigm [13] where a teacher model is trained on …

… the limited human knowledge that is leveraged for feature engineering. Hand-crafted features
… scenarios such as the heterogeneity of human activity. Furthermore, compared to device-…

… Human action recognition has been an active research topic in computer vision due to its …
of action recognition can be divided into two categories. One is traditional hand-crafted features…

… Early efforts focused on designing robust features and training efficient … handcrafted
features, optimizing each component separately, resulting in a non-optimal pedestrian detection …

… In the area of human activity recognition (HAR), the … pattern recognition challenges including:
handcrafted features, shallow … In this paper, we apply two knowledge distillation proposed …