通过去混淆技术提升可穿戴传感器下人类行为预测准确性
A Deconfounding Framework for Human Behavior Prediction: Enhancing Robotic Systems in Dynamic Environments
- 融合去混淆与时间序列模型，分离隐藏干扰因素
- 在真实数据集上显著优于传统方法，提升预测可靠性
- 适合需实时决策的机器人交互系统研发者
准确预测人类行为对有效的人机交互（HRI）系统至关重要，尤其是在需要实时决策的动态环境中。本文针对利用可穿戴传感器采集的多变量时间序列数据进行未来人类行为预测的挑战，提出一种鲁棒的预测模型。该模型结合去混淆技术与先进的时序预测方法，增强模型分离真实因果关系的能力，从而提高预测精度。在真实世界数据集上的评估表明，该方法显著优于传统模型，为响应式和自适应的HRI系统提供了更可靠的预测基础。
原文摘要 · Abstract (English)
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.
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