Food Intake Monitoring: Automated Chew Event Detection in Chewing Sounds

Abstract

The analysis of the food intake behavior has the potential to provide insights into the development of obesity and eating disorders. As an elementary part of this analysis, chewing strokes have to be detected and counted. Our approach for food intake analysis is the evaluation of chewing sounds generated during the process of eating. These sounds were recorded by microphones applied to the outer ear canal of the user. Eight different algorithms for automated chew event detection were presented and evaluated on two datasets. The first dataset contained food intake sounds from the consumption of six types of food. The second dataset consisted of recordings of different environmental sounds. These datasets contained 68,094 chew events in around 18 h recording data. The results of the automated chew event detection were compared to manual annotations. Precision and recall over 80% were achieved by most of the algorithms. A simple noise reduction algorithm using spectral subtraction was implemented for signal enhancement. Its benefit on the chew event detection performance was evaluated. A reduction of the number of false detections by 28% on average was achieved by maintaining the detection performance. The system is able to be used for calculation of the chewing frequency in laboratory settings.

A Novel Wearable Device for Food Intake and Physical Activity Recognition

“…The second stage of eating involves chewing and can be monitored via chewing sounds [ 16 , 17 , 18 ], EMG and force sensors [ 19 , 20 , 21 ], or capturing jaw vibrations during chewing using strain sensors [ 22 , 23 , 24 , 25 ]. In [ 18 ], use of a conduction microphone was suggested for capturing chewing sounds. An acoustic based approach for detection of chewing suffers from the presence of environmental acoustic noise and, therefore, requires the use of additional reference microphones to eliminate environmental noise [ 18 , 26 ].…”

Unobtrusive electromyography-based eating detection in daily life: A new tool to address underreporting?

“…Apart from the limitation that eating with the non-dominant hand will be missed most bite counters still rely on the user input to press a start button before the eating episodes in naturalistic environments. Other approaches aim at detecting eating episodes based on continuous measurements of swallowing and/or chewing activities: For example, audio recording at the inner ear has been used (Amft, Kusserow, & Troster, 2009;Bedri, Verlekar, Thomaz, Avva, & Starner, 2015;Nishimura & Kuroda, 2008;Papapanagiotou, Diou, Zhou, van den Boer, et al, 2016;Päßler & Fischer, 2014). Because of specialized algorithms that are needed to process the acoustic signals, most devices achieve acceptable results in laboratory setting with restricted food types and eating episodes, however, their accuracy in unrestricted, more challenging environments needs to be established.…”

Interactions with recognized patients using smart glasses

“…Other emerging applications for smart glasses belong to the emerging "quantify yourself" movement trend, starting from top-level classification of activities, such as the differentiation between walking, cycling and driving [6], and going to very high resolution activity recognition like detecting chewing and swallowing activities [7] and other activities of daily living (ADL, e.g. by analysis of the capability of self-maintenance) in mobile healthcare.…”