Coregionalised Locomotion Envelopes - A Qualitative Approach

TL;DR

This paper introduces coregionalised locomotion envelopes, a qualitative method for multi-dimensional manifold regression in human locomotion data, enabling better prediction and control of movement sequences.

Contribution

It presents a novel qualitative approach for multi-dimensional manifold regression applied to human locomotion, leveraging correlations for improved prediction and control.

Findings

Provides a qualitative description of the coregionalised locomotion envelopes method.

Demonstrates potential for improved movement prediction and control.

Lays groundwork for future quantitative validation and applications.

Abstract

'Sharing of statistical strength' is a phrase often employed in machine learning and signal processing. In sensor networks, for example, missing signals from certain sensors may be predicted by exploiting their correlation with observed signals acquired from other sensors. For humans, our hands move synchronously with our legs, and we can exploit these implicit correlations for predicting new poses and for generating new natural-looking walking sequences. We can also go much further and exploit this form of transfer learning, to develop new control schemas for robust control of rehabilitation robots. In this short paper we introduce coregionalised locomotion envelopes - a method for multi-dimensional manifold regression, on human locomotion variates. Herein we render a qualitative description of this method.