Human Movement Forecasting with Loose Clothing

TL;DR

This paper demonstrates that sensors attached to loose clothing can outperform rigid body-attached sensors in human motion prediction, achieving higher accuracy with less historical data in both simulated and real scenarios.

Contribution

It provides the first comprehensive comparison showing clothing-attached sensors can significantly improve motion prediction accuracy over traditional rigid sensors.

Findings

Fabric-attached sensors improve prediction accuracy up to 40%.

They require 80% less past data for 95% accuracy.

Performance is validated on both simulated and real human movements.

Abstract

Human motion prediction and trajectory forecasting are essential in human motion analysis. Nowadays, sensors can be seamlessly integrated into clothing using cutting-edge electronic textile (e-textile) technology, allowing long-term recording of human movements outside the laboratory. Motivated by the recent findings that clothing-attached sensors can achieve higher activity recognition accuracy than body-attached sensors. This work investigates the performance of human motion prediction using clothing-attached sensors compared with body-attached sensors. It reports experiments in which statistical models learnt from the movement of loose clothing are used to predict motion patterns of the body of robotically simulated and real human behaviours. Counterintuitively, the results show that fabric-attached sensors can have better motion prediction performance than rigid-attached sensors. Specifically, The fabric-attached sensor can improve the accuracy up to 40% and requires up to 80% less duration of the past trajectory to achieve high prediction accuracy (i.e., 95%) compared to the rigid-attached sensor.