MoCap-Impute: A Comprehensive Benchmark and Comparative Analysis of Imputation Methods for IMU-based Motion Capture Data

TL;DR

This paper systematically evaluates various imputation methods for IMU-based motion capture data, introduces a new dataset, and demonstrates that multivariate approaches, especially advanced models like GAIN, significantly improve data recovery accuracy.

Contribution

It provides the first comprehensive benchmark for IMU-based MoCap data imputation, including a new dataset and a comparative analysis of statistical, machine learning, and deep learning methods.

Findings

Multivariate imputation outperforms univariate methods in complex missingness scenarios.

Advanced models like GAIN achieve up to 50% error reduction in challenging cases.

The new dataset enables standardized evaluation of imputation techniques for MoCap data.

Abstract

Motion capture (MoCap) data from wearable Inertial Measurement Units (IMUs) is vital for applications in sports science, but its utility is often compromised by missing data. Despite numerous imputation techniques, a systematic performance evaluation for IMU-derived MoCap time-series data is lacking. We address this gap by conducting a comprehensive comparative analysis of statistical, machine learning, and deep learning imputation methods. Our evaluation considers three distinct contexts: univariate time-series, multivariate across subjects, and multivariate across kinematic angles. To facilitate this benchmark, we introduce the first publicly available MoCap dataset designed specifically for imputation, featuring data from 53 karate practitioners. We simulate three controlled missingness mechanisms: missing completely at random (MCAR), block missingness, and a novel value-dependent pattern at signal transition points. Our experiments, conducted on 39 kinematic variables across all subjects, reveal that multivariate imputation frameworks consistently outperform univariate approaches, particularly for complex missingness. For instance, multivariate methods achieve up to a 50% mean absolute error reduction (MAE from 10.8 to 5.8) compared to univariate techniques for transition point missingness. Advanced models like Generative Adversarial Imputation Networks (GAIN) and Iterative Imputers demonstrate the highest accuracy in these challenging scenarios. This work provides a critical baseline for future research and offers practical recommendations for improving the integrity and robustness of Mo-Cap data analysis.