Statistical Emulations of Human Operational Motions in Industrial Environments

Abstract

This paper tackles the challenging problem of developing emulators for human operational motions in industrial workplaces. We represent human motion as time-indexed sequences of body shapes and formulate a statistical generative model for these shape sequences. The sequences are modeled as continuous-time stochastic processes on a Riemannian shape manifold. Key challenges include the manifold's nonlinearity, variability in motion execution rates, the infinite-dimensional nature of the processes, and population-level variability across action classes. Deep learning methods are ineffective due to the small training samples typically available in this domain. To address these issues, we integrate a number of tools: temporal alignment via time warping, Riemannian geometry for handling nonlinearities, and shape- and functional-PCA for dimensionality reduction. A Gaussian model is then imposed on the reduced Euclidean spaces to emulate random motion sequences, which are then evaluated in representative industrial scenarios. We utilize a number of metrics to validate randomly generated shape sequences.