Human Movement Representation on Multivariate Time Series for Recognition of Professional Gestures and Forecasting Their Trajectories - Manitsaris, Sotiris; Senteri, Gavriela; Makrygiannis, Dimitrios; Glushkova, Alina (Frontiers in Robotics and AI, 2020)
Human-Centered Artificial Intelligence is increasingly deployed in professional workplaces in Industry 4.0 to address various challenges related to the collaboration between the operators and the machines, the augmentation of their capabilities or the improvement of the quality of their work and life in general. Intelligent systems and autonomous machines need to continuously recognize and follow the professional actions and gestures of the operators in order to collaborate with them and anticipate their trajectories for avoiding potential collisions and accidents. Nevertheless, the recognition of patterns of professional gestures is a very challenging task for both research and the industry. There are various types of human movements that the intelligent systems need to perceive, e.g., gestural commands to machines, professional actions with or without the use of tools etc. Moreover, the inter- and intra- class spatiotemporal variances together with the very limited access to annotated human motion data constitute a major research challenge. In this paper, we introduce the Gesture Operational Model which describes how gestures are performed based on assumptions that focus on the dynamic association of body entities, their synergies, and their serial and non-serial mediations, as well as, their transitioning over time from one state to another. Then, the coefficients of the Gesture Operational Model are estimated through a stochastic State-Space representation and Maximum Likelihood Estimation that provide with a number of equations that describe the human movement in space for each body entity with regard to its related entities. The dynamic simulation of the model generates a confidence-bounding box for every entity that describes the tolerance of its spatial variance over time. The contribution of our approach is demonstrated both for recognizing gestures and forecasting human motion trajectories. In recognition, it is combined with continuous Hidden Markov Models to boost the recognition accuracy when the likelihoods are not confident. In forecasting, a motion trajectory can be estimated by taking as minimum input two observations only. The performance of the algorithm has been evaluated using three industrial datasets from a TV assembly line, the glassblowing industry and gestural commands to Automated Guided Vehicles.