HEAR: Fog-Enabled Energy-Aware Online Human Eating Activity Recognition
Nafiul Rashid, Manik Dautta, Peter Tseng, Mohammad Abdullah Al Faruque
Abstract
Eating activity recognition (EAR) plays an important role in ensuring healthy eating habits. Recent advancements of the Internet of Things (IoT) have bolstered automated EAR through various wearable edge devices. State-of-the-art work uses some offline trained classifiers at the fog device to recognize eating activities. However, the eating habits of a person change quite frequently and vary from person to person. Therefore, the classifiers should be updated continuously with new data to adapt to these changes and be personalized over time through online learning. To the best of our knowledge, no state-of-the-art work has addressed this issue so far. In this article, we propose an online learning methodology called human eating activity recognition (HEAR) by introducing an online update phase. We also design an algorithm to be used in the online update phase that provides approximate true labels for the new data. Moreover, we also design a wearable neckband as the edge device to capture eating activity data (Chewing, Swallowing, Talking, and Idle) in a lab environment. Through a detailed experimental evaluation on 12 users, we show that an online learned neural network (OLNN) classifier using our HEAR methodology performs better than any state-of-the-art offline trained classifier. We also demonstrate that our OLNN classifier is energy efficient compared to the competitive offline trained classifiers.