Human Movement and Ergonomics: an Industry-Oriented Dataset for Collaborative Robotics

International audienceImproving 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 paper 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 in 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 3 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

Canagliflozin and renal outcomes in type 2 diabetes and nephropathy

BACKGROUND Type 2 diabetes mellitus is the leading cause of kidney failure worldwide, but few effective long-term treatments are available. In cardiovascular trials of inhibitors of sodium–glucose cotransporter 2 (SGLT2), exploratory results have suggested that such drugs may improve renal outcomes in patients with type 2 diabetes. METHODS In this double-blind, randomized trial, we assigned patients with type 2 diabetes and albuminuric chronic kidney disease to receive canagliflozin, an oral SGLT2 inhibitor, at a dose of 100 mg daily or placebo. All the patients had an estimated glomerular filtration rate (GFR) of 30 to <90 ml per minute per 1.73 m2 of body-surface area and albuminuria (ratio of albumin [mg] to creatinine [g], >300 to 5000) and were treated with renin–angiotensin system blockade. The primary outcome was a composite of end-stage kidney disease (dialysis, transplantation, or a sustained estimated GFR of <15 ml per minute per 1.73 m2), a doubling of the serum creatinine level, or death from renal or cardiovascular causes. Prespecified secondary outcomes were tested hierarchically. RESULTS The trial was stopped early after a planned interim analysis on the recommendation of the data and safety monitoring committee. At that time, 4401 patients had undergone randomization, with a median follow-up of 2.62 years. The relative risk of the primary outcome was 30% lower in the canagliflozin group than in the placebo group, with event rates of 43.2 and 61.2 per 1000 patient-years, respectively (hazard ratio, 0.70; 95% confidence interval [CI], 0.59 to 0.82; P=0.00001). The relative risk of the renal-specific composite of end-stage kidney disease, a doubling of the creatinine level, or death from renal causes was lower by 34% (hazard ratio, 0.66; 95% CI, 0.53 to 0.81; P<0.001), and the relative risk of end-stage kidney disease was lower by 32% (hazard ratio, 0.68; 95% CI, 0.54 to 0.86; P=0.002). The canagliflozin group also had a lower risk of cardiovascular death, myocardial infarction, or stroke (hazard ratio, 0.80; 95% CI, 0.67 to 0.95; P=0.01) and hospitalization for heart failure (hazard ratio, 0.61; 95% CI, 0.47 to 0.80; P<0.001). There were no significant differences in rates of amputation or fracture. CONCLUSIONS In patients with type 2 diabetes and kidney disease, the risk of kidney failure and cardiovascular events was lower in the canagliflozin group than in the placebo group at a median follow-up of 2.62 years

Global patient outcomes after elective surgery: prospective cohort study in 27 low-, middle- and high-income countries.

BACKGROUND: As global initiatives increase patient access to surgical treatments, there remains a need to understand the adverse effects of surgery and define appropriate levels of perioperative care. METHODS: We designed a prospective international 7-day cohort study of outcomes following elective adult inpatient surgery in 27 countries. The primary outcome was in-hospital complications. Secondary outcomes were death following a complication (failure to rescue) and death in hospital. Process measures were admission to critical care immediately after surgery or to treat a complication and duration of hospital stay. A single definition of critical care was used for all countries. RESULTS: A total of 474 hospitals in 19 high-, 7 middle- and 1 low-income country were included in the primary analysis. Data included 44 814 patients with a median hospital stay of 4 (range 2-7) days. A total of 7508 patients (16.8%) developed one or more postoperative complication and 207 died (0.5%). The overall mortality among patients who developed complications was 2.8%. Mortality following complications ranged from 2.4% for pulmonary embolism to 43.9% for cardiac arrest. A total of 4360 (9.7%) patients were admitted to a critical care unit as routine immediately after surgery, of whom 2198 (50.4%) developed a complication, with 105 (2.4%) deaths. A total of 1233 patients (16.4%) were admitted to a critical care unit to treat complications, with 119 (9.7%) deaths. Despite lower baseline risk, outcomes were similar in low- and middle-income compared with high-income countries. CONCLUSIONS: Poor patient outcomes are common after inpatient surgery. Global initiatives to increase access to surgical treatments should also address the need for safe perioperative care. STUDY REGISTRATION: ISRCTN5181700

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Explicability of SAS image classification algorithms in mine warfare

Qu’elles soient historiques, vestiges d’anciens conflits ou modernes, les mines sous-marines sont une menace constante pour les marines. Malgré l’amélioration des images fournies par les sonars, la reconnaissance de mine reste une tâche complexe même pour un opérateur expérimenté. Afin d’améliorer les performances de classification d’images sonar, des techniques de Reconnaissance Automatique de Cible (ATR) sont utilisées. Celles à base d’apprentissage profond (Deep Learning) permettent notamment d’obtenir des performances en classification d’images jusqu’alors inégalées. Cependant ils possèdent l’inconvénient majeur d’être très peu compréhensibles. Cette incompréhension limite la confiance qui leur est accordée, notamment dans des domaines à risque, et freine donc leur implémentation. Dans un but de compréhension, le domaine de l’Explicabilité des Intelligences Artificielles (XAI) se développe de façon conséquente. Dans cette thèse, une étude des différentes méthodes d’XAI en fonction de l’utilisateur final, de ses attentes et ses tâches est proposée. Cette étude se place à la frontière entre la compréhension de l’humain, de ses attentes et ses propres processus cognitifs ; et l’explication de réseaux profonds convolutifs (CNN). Pour ce faire, différentes approches sont proposées allant de l’explication de réseaux existants (SHAP, LIME, etc.) à la création de nouvelles architectures explicables (Extraction de caractéristiques). L’utilité des explications est étudiée sur différents profils avec des besoins propres comme les développeurs pour vérifier la cohérence du réseau, ou l’utilisateur final pour comprendre la prédiction. Pour ce faire, des tests utilisateurs sont menés.Whether historical, remnants of past conflicts or modern, underwater mines are a constant threat to navies. Despite the improvement of sonar images, mine recognition remains a complex task even for an experienced operator. In order to improve sonar image classification performances, Automatic Target Recognition (ATR) techniques are used. Deep Learning based techniques allow to obtain unprecedented performances in image classification. However, theyhave the major drawback of being very difficult to understand. This lack of understanding limits the confidence that is placed in them, especially in risky areas, and therefore hinders their implementation. In order to improve understanding, Explainable Artificial Intelligence (XAI) is developing in a consistent In this thesis, a study of the different XAI methods according to the end-user, his expectations and his tasks is proposed. This study is placed at the frontier between theunderstanding of the human, his expectations and his own cognitive processes; and the explanation of deep convolutional networks (CNN). To do so, different approaches are proposed ranging from the explanation of existing networks (SHAP, LIME, etc.) to the creation of new explainable architectures (Feature Extraction). The usefulness of the explanations is studied on different profiles with specific needs such as developers to check the coherence of the network, or the end user to understand the prediction. To do this, user tests are conducted

AI Explainability and Acceptance: A Case Study for Underwater Mine Hunting

International audienceIn 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