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Online recognition of gestures is critical for intuitive human-robot interaction (HRI) and further push collaborative robotics into the market, making robots accessible to more people. The problem is that it is difficult to achieve accurate gesture recognition in real unstructured environments, often using distorted and incomplete multisensory data. This paper introduces an HRI framework to classify large vocabularies of interwoven static gestures (SGs) and dynamic gestures (DGs) captured with wearable sensors. DG features are obtained by applying data dimensionality reduction to raw data from sensors (resampling with cubic interpolation and principal component analysis). Experimental tests were conducted using the UC2017 hand gesture dataset with samples from eight different subjects. The classification models show an accuracy of 95.6% for a library of 24 SGs with a random forest and 99.3% for 10 DGs using artificial neural networks. These results compare equally or favorably with different commonly used classifiers. Long short-term memory deep networks achieved similar performance in online frame-by-frame classification using raw incomplete data, performing better in terms of accuracy than static models with specially crafted features, but worse in training and inference time. The recognized gestures are used to teleoperate a robot in a collaborative process that consists in preparing a breakfast meal.

We propose a novel way of solving the issue of classification of out-of-vocabulary gestures using Artificial Neural Networks (ANNs) trained in the Generative Adversarial Network (GAN) framework. A generative model augments the data set in an online fashion with new samples and stochastic target vectors, while a discriminative model determines the class of the samples. The approach was evaluated on the UC2017 SG and UC2018 DualMyo data sets. The generative models’ performance was measured with a distance metric between generated and real samples. The discriminative models were evaluated by their accuracy on trained and novel classes. In terms of sample generation quality, the GAN is significantly better than a random distribution (noise) in mean distance, for all classes. In the classification tests, the baseline neural network was not capable of identifying untrained gestures. When the proposed methodology was implemented, we found that there is a trade-off between the detection of trained and untrained gestures, with some trained samples being mistaken as novelty. Nevertheless, a novelty detection accuracy of 95.4% or 90.2% (depending on the data set) was achieved with just 5% loss of accuracy on trained classes.

International audience; Enterprises now face a global, dynamic, and unpredictable economic environment. In response to quick changes of the demand, the production needs to have the ability to adapt rapidly to meet the production requirements. The Reconfigurable Manufacturing System (RMS) paradigm enables such capabilities. However, defining design and reconfiguration rules is highly challenging, as it requires a broad knowledge encompassing the inclusion of technological, production, and economic metrics, as well as an understanding of a reconfiguration strategy, determining the necessary reconfiguration frequency of the system. For now, no global methodology taking all those aspects into account has been proposed. This article presents an original framework for the design, evaluation, and reconfiguration of the Reconfigurable Production System (RPS). New metrics to measure reconfigurability are defined. The design approach consists in three main steps which are individually developed. The selection of the appropriated production system is based on the comparison of reconfigurability and productivity indicators. Finally, the reconfiguration strategy is presented. The methodology is applied on a case study from the automotive industry.