Traditional crafts exhibit tangible and intangible dimensions. Intangible dimensions include the practitioner’s gestural know-how in craft practice and have received smaller attention than tangible dimensions in digitization projects. This work presents the process of representation and presentation of the glasswork and is exemplified in the re-creation of a historical object. Following an articulated pipeline approach for data collection, annotation, the crafting process is represented visually and semantically in a way that can be meaningfully presented and utilized in craft training and preservation. The outcomes of the proposed approach were used to implement a Mixed Reality installation. The installation targets craft presentation through an exploration of the workspace, as well as craft training through an interactive experience where users re-enact gestures of a glass master holding a tool and receiving audiovisual feedback on the accuracy of their performance. Preliminary evaluation results show high acceptance of the installation and increased user interest.

In this paper, a representation based on digital assets and semantic annotations is established for Traditional Craft instances, in a way that captures their socio-historic context and preserves both their tangible and intangible Cultural Heritage dimensions. These meaningful and documented experiential presentations are delivered to the target audience through narratives that address a range of uses, including personalized storytelling, interactive Augmented Reality (AR), augmented physical artifacts, Mixed Reality (MR) exhibitions, and the Web. The provided engaging cultural experiences have the potential to have an impact on interest growth and tourism, which can support Traditional Craft communities and institutions. A secondary impact is the attraction of new apprentices through training and demonstrations that guarantee long-term preservation. The proposed approach is demonstrated in the context of textile manufacturing as practiced by the community of the Haus der Seidenkultur, a former silk factory that was turned into a museum where the traditional craft of Jacquard weaving is still practiced.

A roadmap is proposed that defines a systematic approach for craft preservation and its evaluation. The proposed roadmap aims to deepen craft understanding so that blueprints of appropriate tools that support craft documentation, education, and training can be designed while achieving preservation through the stimulation and diversification of practitioner income. In addition to this roadmap, an evaluation strategy is proposed to validate the efficacy of the developed results and provide a benchmark for the efficacy of craft preservation approaches. The proposed contribution aims at the catalyzation of craft education and training with digital aids, widening access and engagement to crafts, economizing learning, increasing exercisability, and relaxing remoteness constraints in craft learning.

This work regards the representation of handicrafts for craft training and demonstration in the environment of an ethnographic heritage museum. The craft of mastic cultivation is chosen as a use case. This paper presents the process of representation and presentation of this craft, following an articulated pipeline approach for data collection, annotation, and semantic representation. The outcomes were used to implement an exhibition that targets the presentation of craft context and craft training, through interactive experiences, mobile applications, and a hands-on training where users reenact the gestures of a mastic cultivator. Preliminary evaluation results show high acceptance for the installation and increased user interest.

Collaborative robots are currently deployed in professional environments, in collaboration with professional human operators, helping to strike the right balance between mechanization and manual intervention in manufacturing processes required by Industry 4.0. In this paper, the contribution of gesture recognition and pose estimation to the smooth introduction of cobots into an industrial assembly line is described, with a view to performing actions in parallel with the human operators and enabling interaction between them. The proposed active vision system uses two RGB-D cameras that record different points of view of gestures and poses of the operator, to build an external perception layer for the robot that facilitates spatiotemporal adaptation, in accordance with the human's behavior. The use-case of this work is concerned with LCD TV assembly of an appliance manufacturer, comprising of two parts. The first part of the above-mentioned operation is assigned to a robot, strengthening the assembly line. The second part is assigned to a human operator. Gesture recognition, pose estimation, physical interaction, and sonic notification, create a multimodal human-robot interaction system. Five experiments are performed, to test if gesture recognition and pose estimation can reduce the cycle time and range of motion of the operator, respectively. Physical interaction is achieved using the force sensor of the cobot. Pose estimation through a skeleton-tracking algorithm provides the cobot with human pose information and makes it spatially adjustable. Sonic notification is added for the case of unexpected incidents. A real-time gesture recognition module is implemented through a Deep Learning architecture consisting of Convolutional layers, trained in an egocentric view and reducing the cycle time of the routine by almost 20%. This constitutes an added value in this work, as it affords the potential of recognizing gestures independently of the anthropometric characteristics and the background. Common metrics derived from the literature are used for the evaluation of the proposed system. The percentage of spatial adaptation of the cobot is proposed as a new KPI for a collaborative system and the opinion of the human operator is measured through a questionnaire that concerns the various affective states of the operator during the collaboration.

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, for example, gestural commands to machines and professional actions with or without the use of tools. Moreover, theinterclass andintraclass 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 assumptions of the Gesture Operational Model are translated into a simultaneous equation system for each body entity through State-Space modeling. The coefficients of the equation are computed using the Maximum Likelihood Estimation method. The 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 for both 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 four industrial datasets that contain gestures and actions from a TV assembly line, the glassblowing industry, the gestural commands to Automated Guided Vehicles as well as the Human–Robot Collaboration in the automotive assembly lines. The hybrid approach State-Space and HMMs outperforms standard continuous HMMs and a 3DCNN-based end-to-end deep architecture.