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[2T6-GS-9-02] Real-time Cooking Activity Recognition Using Accelerometers
[[Online]]
Keywords:Human Activity Recognition, Human Computer Interaction, Real-time inference
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.
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