  Movement synchrony reflects the coordination of body movements between
interacting dyads. The estimation of movement synchrony has been automated by
powerful deep learning models such as transformer networks. However, instead of
designing a specialized network for movement synchrony estimation, previous
transformer-based works broadly adopted architectures from other tasks such as
human activity recognition. Therefore, this paper proposed a skeleton-based
graph transformer for movement synchrony estimation. The proposed model applied
ST-GCN, a spatial-temporal graph convolutional neural network for skeleton
feature extraction, followed by a spatial transformer for spatial feature
generation. The spatial transformer is guided by a uniquely designed joint
position embedding shared between the same joints of interacting individuals.
Besides, we incorporated a temporal similarity matrix in temporal attention
computation considering the periodic intrinsic of body movements. In addition,
the confidence score associated with each joint reflects the uncertainty of a
pose, while previous works on movement synchrony estimation have not
sufficiently emphasized this point. Since transformer networks demand a
significant amount of data to train, we constructed a dataset for movement
synchrony estimation using Human3.6M, a benchmark dataset for human activity
recognition, and pretrained our model on it using contrastive learning. We
further applied knowledge distillation to alleviate information loss introduced
by pose detector failure in a privacy-preserving way. We compared our method
with representative approaches on PT13, a dataset collected from autism therapy
interventions. Our method achieved an overall accuracy of 88.98% and surpassed
its counterparts by a wide margin while maintaining data privacy.
