  Action recognition is an important component to improve autonomy of physical
rehabilitation devices, such as wearable robotic exoskeletons. Existing human
action recognition algorithms focus on adult applications rather than pediatric
ones. In this paper, we introduce BabyNet, a light-weight (in terms of
trainable parameters) network structure to recognize infant reaching action
from off-body stationary cameras. We develop an annotated dataset that includes
diverse reaches performed while in a sitting posture by different infants in
unconstrained environments (e.g., in home settings, etc.). Our approach uses
the spatial and temporal connection of annotated bounding boxes to interpret
onset and offset of reaching, and to detect a complete reaching action. We
evaluate the efficiency of our proposed approach and compare its performance
against other learning-based network structures in terms of capability of
capturing temporal inter-dependencies and accuracy of detection of reaching
onset and offset. Results indicate our BabyNet can attain solid performance in
terms of (average) testing accuracy that exceeds that of other larger networks,
and can hence serve as a light-weight data-driven framework for video-based
infant reaching action recognition.
