Shared Representation for 3D Pose Estimation, Action Classification, and Progress Prediction from Tactile Signals

TL;DR

This paper introduces SCOTTI, a shared convolutional transformer model that simultaneously estimates 3D human pose, classifies actions, and predicts movement progress using tactile signals, outperforming separate task models.

Contribution

The study presents the first multi-task tactile inference model for pose, action classification, and progress prediction, demonstrating improved performance through shared representation learning.

Findings

SCOTTI outperforms existing methods on all three tasks.

First to explore action progress prediction with foot tactile signals.

Introduces a new dataset with 7 hours of tactile activity data from 15 participants.

Abstract

Estimating human pose, classifying actions, and predicting movement progress are essential for human-robot interaction. While vision-based methods suffer from occlusion and privacy concerns in realistic environments, tactile sensing avoids these issues. However, prior tactile-based approaches handle each task separately, leading to suboptimal performance. In this study, we propose a Shared COnvolutional Transformer for Tactile Inference (SCOTTI) that learns a shared representation to simultaneously address three separate prediction tasks: 3D human pose estimation, action class categorization, and action completion progress estimation. To the best of our knowledge, this is the first work to explore action progress prediction using foot tactile signals from custom wireless insole sensors. This unified approach leverages the mutual benefits of multi-task learning, enabling the model to achieve improved performance across all three tasks compared to learning them independently. Experimental results demonstrate that SCOTTI outperforms existing approaches across all three tasks. Additionally, we introduce a novel dataset collected from 15 participants performing various activities and exercises, with 7 hours of total duration, across eight different activities.