Proceedings of the ACM Interactive Mobile Wearable Ubiquitous Technology
Department of Computer Science
In this paper, we propose Auracle, a wearable earpiece that can automatically recognize eating behavior. More specifically, in free-living conditions, we can recognize when and for how long a person is eating. Using an off-the-shelf contact microphone placed behind the ear, Auracle captures the sound of a person chewing as it passes through the bone and tissue of the head. This audio data is then processed by a custom analog/digital circuit board. To ensure reliable (yet comfortable) contact between microphone and skin, all hardware components are incorporated into a 3D-printed behind-the-head framework. We collected field data with 14 participants for 32 hours in free-living conditions and additional eating data with 10 participants for 2 hours in a laboratory setting. We achieved accuracy exceeding 92.8% and F1 score exceeding 77.5% for eating detection. Moreover, Auracle successfully detected 20-24 eating episodes (depending on the metrics) out of 26 in free-living conditions. We demonstrate that our custom device could sense, process, and classify audio data in real time. Additionally, we estimateAuracle can last 28.1 hours with a 110 mAh battery while communicating its observations of eating behavior to a smartphone over Bluetooth.
Dartmouth Digital Commons Citation
Bi, Shengjie; Wang, Tao; Tobias, Nicole; Nordrum, Josephine; Wang, Shang; Halvorsen, George; Sen, Sougata; Peterson, Ron; Caine, Kelly; Odame, Kofi; Halter, Ryan; Sorber, Jacob; and Kotz, David, "Detecting Eating Episodes with an Ear-mounted Sensor" (2018). Open Dartmouth: Published works by Dartmouth faculty. 3012.