Statistical Emulations of Human Operational Motions in Industrial Environments

TL;DR

This paper develops statistical models to emulate human operational motions in industrial environments by representing body shapes as stochastic processes on a Riemannian manifold, addressing nonlinearity and variability.

Contribution

It introduces a novel approach combining Riemannian geometry, time warping, and PCA to model and simulate human motions in industrial settings.

Findings

Effective emulation of human motion sequences demonstrated

Models handle variability and nonlinearity successfully

Provides comprehensive evaluation in industrial context

Abstract

This paper addresses the critical and challenging task of developing emulators for simulating human operational motions in industrial workplaces. We conceptualize human motion as a sequence of human body shapes and develop statistical generative models for sequences of (body) shapes of human workers. We model these sequences as a continuous-time stochastic process on a Riemannian shape manifold. This modeling is challenging due to the nonlinearity of the shape manifold, variability in execution rates across observations, infinite dimensionality of stochastic processes, and population variability within and across action classes. This paper proposes multiple solutions to these challenges, incorporating time warping for temporal alignment, Riemannian geometry for tackling nonlinearity, and Shape- and Functional-PCA for dimension reduction. It imposes a Gaussian model on the resulting Euclidean spaces, uses them to emulate random sequences in an industrial setting and evaluates them comprehensively.