A Fabric-based Inexpensive Wearable Neckband for Accurate and Reliable Dietary Activity Monitoring

Dietary habits play a significant role in public health and well-being. Monitoring dietary activities is thus essential for maintaining a healthy lifestyle and preventing many widespread diseases, such as diabetes, obesity, and hypertension. In this work, we present a low-cost wearable neckband for automatic diet activity monitoring. The $5 fabric-based device, comprising an electret microphone, a Bluetooth radio module, and a rechargeable Lithium-ion battery, can wirelessly transmit audio to a smart device in real-time. The classification algorithm processes the audio stream in 3s segments and extracts short-time spectral, waveform, and energy-based acoustic features. We compute various statistical functions from the acoustic features to obtain segmental feature vectors, which are subsequently used for machine learning. We perform an experimental evaluation using an in-house dataset collected using the neckband. We compare the performance of different classifiers in distinguishing between drinking, chewing solid foods, and other non-dietary activities. An averaged class-wise F-measure of 81.25% is achieved using the proposed wearable device and a Random Forest (RF) based classifier.