A dialogue-based annotation for activity recognition
Tittaya Mairittha, Nattaya Mairittha, Sozo Inoue · 2019
This paper presents a method to collect training labels for human activity recognition by using a dialogue system. To show the feasibility of using dialogue-based annotation, we implemented the dialogue system and conducted experiments in the lab setting. The preliminary performance of activity recognition attained the f-measure of 0.76. We also analyze the collected data to provide a better understanding of what users expect from the system, how they interact with it and its other potential uses. Finally, we discussed the results obtained and possible directions for future works.