Accurate prediction of human behavior is crucial for effective human-robot interaction (HRI) systems, especially in dynamic environments where real-time decisions are essential. This paper addresses the challenge of forecasting future human behavior using multivariate time series data from wearable sensors, which capture various aspects of human movement. The presence of hidden confounding factors in this data often leads to biased predictions, limiting the reliability of traditional models. To overcome this, we propose a robust predictive model that integrates deconfounding techniques with advanced time series prediction methods, enhancing the model's ability to isolate true causal relationships and improve prediction accuracy. Evaluation on real-world datasets demonstrates that our approach significantly outperforms traditional methods, providing a more reliable foundation for responsive and adaptive HRI systems.

翻译：准确预测人类行为对于有效的人机交互（HRI）系统至关重要，尤其是在需要实时决策的动态环境中。本文探讨了利用来自可穿戴传感器的多元时间序列数据预测未来人类行为的挑战，这些数据捕捉了人类运动的各个方面。数据中存在的隐藏混杂因素常常导致预测偏差，限制了传统模型的可靠性。为克服这一问题，我们提出了一种鲁棒的预测模型，该模型将去混淆技术与先进的时间序列预测方法相结合，增强了模型分离真实因果关系并提高预测准确性的能力。在真实世界数据集上的评估表明，我们的方法显著优于传统方法，为响应式和自适应的人机交互系统提供了更可靠的基础。