  Accurate detection of individual intake gestures is a key step towards
automatic dietary monitoring. Both inertial sensor data of wrist movements and
video data depicting the upper body have been used for this purpose. The most
advanced approaches to date use a two-stage approach, in which (i) frame-level
intake probabilities are learned from the sensor data using a deep neural
network, and then (ii) sparse intake events are detected by finding the maxima
of the frame-level probabilities. In this study, we propose a single-stage
approach which directly decodes the probabilities learned from sensor data into
sparse intake detections. This is achieved by weakly supervised training using
Connectionist Temporal Classification (CTC) loss, and decoding using a novel
extended prefix beam search decoding algorithm. Benefits of this approach
include (i) end-to-end training for detections, (ii) simplified timing
requirements for intake gesture labels, and (iii) improved detection
performance compared to existing approaches. Across two separate datasets, we
achieve relative $F_1$ score improvements between 1.9% and 6.2% over the
two-stage approach for intake detection and eating/drinking detection tasks,
for both video and inertial sensors.
