Human Movement Representation on Multivariate Time Series for Recognition of Professional Gestures and Forecasting Their Trajectories

The work presented in this paper proposes a gesture operational model (GOM) that describes how the body parts cooperate, to perform a situated professional gesture. The model is built upon several assumptions that determine the dynamic relationship between the body entities within the execution of the human movement. The model is based on the state-space (SS) representation, as a simultaneous equation system for all the body entities is generated, composed of a set of first-order differential equations. The coefficients of the equation system are estimated using the maximum likelihood estimation (MLE) method, and its dynamic simulation generates a dynamic tolerance of the spatial variance of the movement over time. The scientific evidence of the GOM is evaluated through its ability to improve the recognition accuracy of gestural time series that are modeled using continuous hidden Markov models (HMMs) in 5 different use cases.