  This paper presents an implementation on child activity recognition (CAR)
with a graph convolution network (GCN) based deep learning model since prior
implementations in this domain have been dominated by CNN, LSTM and other
methods despite the superior performance of GCN. To the best of our knowledge,
we are the first to use a GCN model in child activity recognition domain. In
overcoming the challenges of having small size publicly available child action
datasets, several learning methods such as feature extraction, fine-tuning and
curriculum learning were implemented to improve the model performance. Inspired
by the contradicting claims made on the use of transfer learning in CAR, we
conducted a detailed implementation and analysis on transfer learning together
with a study on negative transfer learning effect on CAR as it hasn't been
addressed previously. As the principal contribution, we were able to develop a
ST-GCN based CAR model which, despite the small size of the dataset, obtained
around 50% accuracy on vanilla implementations. With feature extraction and
fine-tuning methods, accuracy was improved by 20%-30% with the highest accuracy
being 82.24%. Furthermore, the results provided on activity datasets
empirically demonstrate that with careful selection of pre-train model datasets
through methods such as curriculum learning could enhance the accuracy levels.
Finally, we provide preliminary evidence on possible frame rate effect on the
accuracy of CAR models, a direction future research can explore.
