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. Leveraging statistical shape theory, we develop statistical generative models for sequences of human (body) shapes of human workers in workplaces. Our goal is to create emulators that generate random, realistic human motions statistically consistent with past work performances, essential for simulating industrial operations involving human labor. We derive the space of shapes as a Riemannian shape manifold, modeling human motion as a continuous-time stochastic process on this manifold. Representing such processes 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 studies multiple solutions to these problems. This paper proposes multiple solutions to these challenges, presenting a comprehensive framework that incorporates (1) time warping for temporal alignment of training data, (2) Riemannian geometry for tackling manifold nonlinearity, and (3) Shape- and Functional-PCA for dimension reduction leading to traditional finite-dimensional Euclidean representations. In particular, it develops the transported velocity field representation for motion sequences and imposes a Gaussian model on the resulting Euclidean spaces. It then applies these models to emulate human shape sequences from an industrial operation dataset and evaluates these simulations in multiple ways.