Class-Agnostic Repetitive Action Counting Using Wearable Devices.

Abstract

We present Class-agnostic Repetitive action Counting (CaRaCount), a novel approach to count repetitive human actions in the wild using wearable devices time series data. CaRaCount is the first few-shot class-agnostic method, being able to count repetitions of any action class with only a short exemplar data sequence containing a few examples from the action class of interest. To develop and evaluate this method, we collect a large-scale time series dataset of repetitive human actions in various context, containing smartwatch data from 10 subjects performing 50 different activities. Experiments on this dataset and three other activity counting datasets namely Crossfit, Recofit, and MM-Fit show that CaRaCount can count repetitive actions with low error, and it outperforms other baselines and state-of-the-art action counting methods. Finally, with a user experience study, we evaluate the usability of our real-time implementation. Our results highlight the efficiency and effectiveness of our approach when deployed outside the laboratory environments.