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A Deconfounding Framework for Human Behavior Prediction: Enhancing Robotic Systems in Dynamic Environments

Wentao Gao, Cheng Zhou

TL;DR

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, and proposes a robust predictive model that integrates deconfounding techniques with advanced time series prediction methods.

Abstract

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.

A Deconfounding Framework for Human Behavior Prediction: Enhancing Robotic Systems in Dynamic Environments

TL;DR

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, and proposes a robust predictive model that integrates deconfounding techniques with advanced time series prediction methods.

Abstract

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.

Paper Structure

This paper contains 23 sections, 1 theorem, 10 equations, 6 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. Human-Robot Interaction (HRI)
  4. Predictive Modeling for Behavior Forecasting
  5. Multivariate Time Series Prediction
  6. Causality in Time Series Analysis
  7. Problem Definition
  8. Methodology
  9. Deconfounding Action Factor Model
  10. Latent Variable Inference using RNN
  11. Model Integration and Prediction
  12. Assumptions and Theoretical Foundation
  13. Sequential Kallenberg Construction
  14. Time Series Prediction Models
  15. iTransformer
  16. ...and 8 more sections

Key Result

Theorem 1

Soundness of the Learned $\mathbf{Z}_t$. If at every timestep $t$, the distribution of assigned causes $(\mathbf{A}_{t1}, \ldots, \mathbf{A}_{tk})$ admits a Sequential Kallenberg Construction from $\mathbf{Z}_t = g(\bar{\mathbf{H}}_{t-1})$ and $\mathbf{X}_t$, then the learned $\mathbf{Z}_t$ can serv

Figures (6)

  • Figure 1: Summary Causal Graph
  • Figure 2: Variables Clarification
  • Figure 3: Implementation of Deconfounding Part
  • Figure 4: Comparison results of generated confounder 1 and the predicted confounder.
  • Figure 5: R2 score over epochs, finally reach out a stable and high score.
  • ...and 1 more figures

Theorems & Definitions (2)

  • Definition 1
  • Theorem 1