Inferring Intentions to Speak Using Accelerometer Data In-the-Wild
Litian Li Jord Molhoek Jing Zhou
Humans have good natural intuition to recognize when another person has
something to say. It would be interesting if an AI can also recognize
intentions to speak. Especially in scenarios when an AI is guiding a group
discussion, this can be a useful skill. This work studies the inference of
successful and unsuccessful intentions to speak from accelerometer data. This
is chosen because it is privacy-preserving and feasible for in-the-wild
settings since it can be placed in a smart badge. Data from a real-life social
networking event is used to train a machine-learning model that aims to infer
intentions to speak. A subset of unsuccessful intention-to-speak cases in the
data is annotated. The model is trained on the successful intentions to speak
and evaluated on both the successful and unsuccessful cases. In conclusion,
there is useful information in accelerometer data, but not enough to reliably
capture intentions to speak. For example, posture shifts are correlated with
intentions to speak, but people also often shift posture without having an
intention to speak, or have an intention to speak without shifting their
posture. More modalities are likely needed to reliably infer intentions to
speak.