Real-time Cooking Activity Recognition Using Accelerometers

Abstract

Cooking by ourselves is important to manage our health. But it’s difficult especially for beginners to reproduce recipes. As a solution to this issue, systems supporting persons who are cooking by feedback from their activities are considerable. In this study, we propose a method of real-time cooking activity recognition using accelerometers to realize such systems. Our proposed method classifies the subject’s activities during cooking of curry to 7 classes by convolutional LSTM with frequency of one time per a second. We confirmed a performance of our proposed method was 86.39% in batch inference, 61.32% in real-time inference as results of evaluation with macro-average of recalls.