Improving work conditions in industry is a major challenge that can be addressed with new emerging technologies such as collaborative robots. Machine learning techniques can improve the performance ...of those robots, by endowing them with a degree of awareness of the human state and ergonomics condition. The availability of appropriate datasets to learn models and test prediction and control algorithms, however, remains an issue. This article presents a dataset of human motions in industry-like activities, fully labeled according to the ergonomics assessment worksheet EAWS, widely used in industries such as car manufacturing. Thirteen participants performed several series of activities, such as screwing and manipulating loads under different conditions, resulting in more than 5 hours of data. The dataset contains the participants’ whole-body kinematics recorded both with wearable inertial sensors and marker-based optical motion capture, finger pressure force, video recordings, and annotations by three independent annotators of the performed action and the adopted posture following the EAWS postural grid. Sensor data are available in different formats to facilitate their reuse. The dataset is intended for use by researchers developing algorithms for classifying, predicting, or evaluating human motion in industrial settings, as well as researchers developing collaborative robotics solutions that aim at improving the workers’ ergonomics. The annotation of the whole dataset following an ergonomics standard makes it valuable for ergonomics-related applications, but we expect its use to be broader in the robotics, machine learning, and human movement communities.
In critical operational context such as Mine Warfare, Automatic Target Recognition (ATR) algorithms are still hardly accepted. The complexity of their decision-making hampers understanding of ...predictions despite performances approaching human expert ones. Much research has been done in Explainability Artificial Intelligence (XAI) field to avoid this “black box” effect. This field of research attempts to provide explanations for the decision-making of complex networks to promote their acceptability. Most of the explanation methods applied on image classifier networks provide heat maps. These maps highlight pixels according to their importance in decision-making.In this work, we first implement different XAI methods for the automatic classification of Synthetic Aperture Sonar (SAS) images by convolutional neural networks (CNN). These different methods are based on a post hoc approach. We study and compare the different heat maps obtained.Second, we evaluate the benefits and the usefulness of explainability in an operational framework for collaboration. To do this, different user tests are carried out with different levels of assistance, ranging from classification for an unaided operator to classification with explained ATR. These tests allow us to study whether heat maps are useful in this context.The results obtained show that the heat maps explanation has a disputed utility according to the operators. Heat map presence does not increase the quality of the classifications. On the contrary, it even increases the response time. Nevertheless, half of operators see a certain usefulness in heat maps explanation.
The purpose was to evaluate the effect of a hydrophilic amide monomer on μTBS of one-step adhesive to dentin at different application times. Clearfil Universal Bond Quick (UBQ), experimental adhesive ...(UBQexp; same compositions as UBQ but hydrophilic amide monomer was replaced with 2-hydroxyethyl methacrylate), Clearfil SE ONE and Clearfil SE Bond were applied to midcoronal dentin prepared with 600-grit SiC at different application time (0, 10, 20 and 40 s). Water sorption (Wsp) and the ultimate tensile strength (UTS) of polymerized adhesives were also measured. UBQ showed significantly lower Wsp and higher UTS than UBQexp. At each application time, UBQ exhibited significantly higher μTBSs than UBQexp. UBQ showed the highest μTBS at 0 s application time among all the adhesives. When the application time was prolonged from 0 s to 10 s, only UBQ showed no significant difference. The hydrophilic amide monomer increased μTBS with reduction in Wsp and increase in the UTS and may allow a shortened application time.
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