  In this paper, we present Fusion-GCN, an approach for multimodal action
recognition using Graph Convolutional Networks (GCNs). Action recognition
methods based around GCNs recently yielded state-of-the-art performance for
skeleton-based action recognition. With Fusion-GCN, we propose to integrate
various sensor data modalities into a graph that is trained using a GCN model
for multi-modal action recognition. Additional sensor measurements are
incorporated into the graph representation, either on a channel dimension
(introducing additional node attributes) or spatial dimension (introducing new
nodes). Fusion-GCN was evaluated on two public available datasets, the
UTD-MHAD- and MMACT datasets, and demonstrates flexible fusion of RGB
sequences, inertial measurements and skeleton sequences. Our approach gets
comparable results on the UTD-MHAD dataset and improves the baseline on the
large-scale MMACT dataset by a significant margin of up to 12.37% (F1-Measure)
with the fusion of skeleton estimates and accelerometer measurements.
