Comparison of Deep Learning Approaches for Protective Behaviour Detection Under Class Imbalance from MoCap and EMG data

The AffecMove challenge organised in the context of the H2020 EnTimeMent project offers three tasks of movement classification in realistic settings and use-cases. Our team, from the EuroMov DHM laboratory participated in Task 1, for protective behaviour (against pain) detection from motion capture data and EMG, in patients suffering from pain-inducing muskuloskeletal disorders. We implemented two simple baseline systems, one LSTM system with pre-training (NTU-60) and a Transformer. We also adapted PA-ResGCN a Graph Convolutional Network for skeleton-based action classification showing state-of-the-art (SOTA) performance to protective behaviour detection, augmented with strategies to handle class-imbalance. For PA-ResGCN-N51 we explored naive fusion strategies with an EMG-only convolutional neural network that didn't improve the overall performance. Unsurprisingly, the best performing system was PA-ResGCN-N51 (w/o EMG) with a F-1 score of 53.36% on the test set for the minority class (MCC 0.4247). The Transformer baseline (MoCap + EMG) came second at 41.05% F-1 test performance (MCC 0.3523) and the LSTM baseline third at 31.16% F-1 (MCC 0.1763). On the validation set the LSTM showed performance comparable to PA-ResGCN, we hypothesize that the LSTM over-fitted on the validation set that wasn't very representative of the train/test distribution.

Automated summary

This study evaluates the performance of different baseline systems for protective behavior detection, with PA-ResGCN-N51 demonstrating the best overall performance. The lower performance of the Transformer and LSTM baseline systems could be attributed to issues with handling class imbalances.