Constrained by the lack of model interpretability and a deep understanding of human movement in traditional movement recognition machine learning methods, this study introduces a novel representation learning method based on causal inference to better understand human joint dynamics and complex behaviors. We propose a two-stage framework that combines the Peter-Clark (PC) algorithm and Kullback-Leibler (KL) divergence to identify and quantify causal relationships between joints. Our method effectively captures interactions and produces interpretable, robust representations. Experiments on the EmoPain dataset show that our causal GCN outperforms traditional GCNs in accuracy, F1 score, and recall, especially in detecting protective behaviors. The model is also highly invariant to data scale changes, enhancing its reliability in practical applications. Our approach advances human motion analysis and paves the way for more adaptive intelligent healthcare solutions. 

pdf/causkelnet-causal-representation-learning-for-human-7lh6ex137nua.pdf

CauSkelNet: Causal Representation Learning for Human Behaviour Analysis

Understanding the biomechanics of the human foot during each stage of walking is important for the objective evaluation of movement dysfunction, accuracy of diagnosis, and prediction of foot impairment. Extracting causal relations from amongst the muscle activities, toe trajectories, and plantar pressures during walking assists in recognizing several disease conditions, and understanding the hidden complexity of human foot functions, thus, facilitating appropriate therapy and treatment. To extract these relations, we applied the Bayesian Network (BN) model to data collected in the stance phase of walking. For a better understanding of foot function, the experimental data were divided into three stages (initial contact, loading response to mid-stance, and terminal stance to pre-swing). BNs were constructed for these three stages of data for normal walking and simulated hemiplegic walking, then compared and analyzed. Results showed that BNs extracted could express the underlying mechanism of foot function.

Understanding Foot Function During Stance Phase by Bayesian Network Based Causal Inference

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Understanding Foot Function During Stance Phase by Bayesian Network Based Causal Inference