  This paper introduces the Turn-Taking Spiking Neural Network (TTSNet), which
is a cognitive model to perform early turn-taking prediction about human or
agent's intentions. The TTSNet framework relies on implicit and explicit
multimodal communication cues (physical, neurological and physiological) to be
able to predict when the turn-taking event will occur in a robust and
unambiguous fashion. To test the theories proposed, the TTSNet framework was
implemented on an assistant robotic nurse, which predicts surgeon's turn-taking
intentions and delivers surgical instruments accordingly. Experiments were
conducted to evaluate TTSNet's performance in early turn-taking prediction. It
was found to reach a F1 score of 0.683 given 10% of completed action, and a F1
score of 0.852 at 50% and 0.894 at 100% of the completed action. This
performance outperformed multiple state-of-the-art algorithms, and surpassed
human performance when limited partial observation is given (< 40%). Such early
turn-taking prediction capability would allow robots to perform collaborative
actions proactively, in order to facilitate collaboration and increase team
efficiency.
