The automatic assessment of the level of independence of a person, based on the recognition of a set of Activities of Daily Living, is among the most challenging research fields in Ambient Intelligence. The article proposes a framework for the recognition of motion primitives, relying on Gaussian Mixture Modeling and Gaussian Mixture Regression for the creation of activity models. A recognition procedure based on Dynamic Time Warping and Mahalanobis distance is found to: (i) ensure good classification results; (ii) exploit the properties of GMM and GMR modeling to allow for an easy run-time recognition; (iii) enhance the consistency of the recognition via the use of a classifier allowing unknown as an answer
The Industry 4.0 paradigm emphasizes the crucial benefits that collaborative robots, i.e., robots able to work alongside and together with humans, could bring to the whole production process. In this context, an enabling technology yet unreached is the design of flexible robots able to deal at all levels with humans' intrinsic variability, which is not only a necessary element for a comfortable working experience for the person, but also a precious capability for efficiently dealing with unexpected events. In this paper, a sensing, representation, planning and control architecture for flexible human-robot cooperation, referred to as FlexHRC, is proposed. FlexHRC relies on wearable sensors for human action recognition, AND/OR graphs for the representation of and reasoning upon cooperation models, and a Task Priority framework to decouple action planning from robot motion planning and control.
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Integrating computer science (CS) into school curricula has become a worldwide preoccupation. Therefore, we present a CS and Robotics integration model and its validation through a large-scale pilot study in the administrative region of the Canton Vaud in Switzerland. Approximately 350 primary school teachers followed a mandatory CS continuing professional development program (CPD) of adapted format with a curriculum scaffolded by instruction modality. This included CS Unplugged activities that aim to teach CS concepts without the use of screens, and Robotics Unplugged activities that employed physical robots, without screens, to learn about robotics and CS concepts. Teachers evaluated positively the CPD and their representation of CS improved. Voluntary adoption rates reached 97% during the CPD and 80% the following year. These results combined with the underpinning literature support the generalisability of the model to other contexts.
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