  We present the design, implementation, and evaluation of a multi-sensor
low-power necklace 'NeckSense' for automatically and unobtrusively capturing
fine-grained information about an individual's eating activity and eating
episodes, across an entire waking-day in a naturalistic setting. The NeckSense
fuses and classifies the proximity of the necklace from the chin, the ambient
light, the Lean Forward Angle, and the energy signals to determine chewing
sequences, a building block of the eating activity. It then clusters the
identified chewing sequences to determine eating episodes. We tested NeckSense
with 11 obese and 9 non-obese participants across two studies, where we
collected more than 470 hours of data in naturalistic setting. Our result
demonstrates that NeckSense enables reliable eating-detection for an entire
waking-day, even in free-living environments. Overall, our system achieves an
F1-score of 81.6% in detecting eating episodes in an exploratory study.
Moreover, our system can achieve a F1-score of 77.1% for episodes even in an
all-day-around free-living setting. With more than 15.8 hours of battery-life
NeckSense will allow researchers and dietitians to better understand natural
chewing and eating behaviors, and also enable real-time interventions.
