Zero-shot Pose-classification based on Wearable Sensors
Human-action recognition (HAR) can be used in a wide range of applications such as life log, healthcare, video surveillance, and worker assistance. Recent advances in deep neural networks (DNN) have drastically enhanced the performance of HAR both in terms of recognition accuracy and coverage of the recognized actions. DNN-based methods, however, sometimes face difficulty in practical deployment, for example, a system user may want to change or add target actions to be recognized but this is not so trivial for DNN-based methods to do so since they require a large amount of training data of the new target actions.
