Although the concept of industrial cobots dates back to 1999, most present day hybrid human-machine assembly systems are merely weight compensators. Here, we present results on the development of a collaborative human-robot manufacturing cell for homokinetic joint assembly. The robot alternates active and passive behaviours during assembly, to lighten the burden on the operator in the first case, and to comply to his/her needs in the latter. Our approach can successfully manage direct physical contact between robot and human, and between robot and environment. Furthermore, it can be applied to standard position (and not torque) controlled robots, common in the industry. The approach is validated in a series of assembly experiments. The human workload is reduced, diminishing the risk of strain injuries. Besides, a complete risk analysis indicates that the proposed setup is compatible with the safety standards, and could be certified.

We propose a set of control strategies for performing two arm manipulation with the goal of simplifying the task definition. In order to develop these strategies we propose a new representation, derived from the cooperative task-space, in the dual quaternion domain. The result is a compact and "singularity free" representation for two arm systems, named cooperative dual task-space. All the proposed control strategies share the same general scheme and are derived by using an analytical approach. Moreover, the mathematical treatment is given in a coherent and systematic fashion, and thus other strategies may be derived using the same argument. Experimental results show the effectiveness and usefulness of the cooperative dual task-space framework and the proposed control strategies.

Deforming a cable to a desired (reachable) shape is a trivial task for a human to do without even knowing the internal dynamics of the cable. This paper proposes a framework for cable shapes manipulation with multiple robot manipulators. The shape is parameterized by a Fourier series. A local deformation model of the cable is estimated on-line with the shape parameters. Using the deformation model, a velocity control law is applied on the robot to deform the cable into the desired shape. Experiments on a dual-arm manipulator are conducted to validate the framework.

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What is already known about this topic? Patients with asthma are rare in epidemiological studies of severe acute respiratory syndrome coronavirus 2 pneumonia.What does this article add to our knowledge? Being asthmatic is not a risk factor for severe acute respiratory syndrome coronavirus 2.How does this study impact current management guidelines? Severe acute respiratory syndrome coronavirus 2 pneumonia may not induce severe asthma exacerbation.BACKGROUND: Viral infections are known to exacerbate asthma in adults. Previous studies have found few patients with asthma among severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pneumonia cases. However, the relationship between SARS-CoV-2 infection and severe asthma exacerbation is not known. OBJECTIVE: To assess the frequency of asthma exacerbation in patients with asthma hospitalized for SARS-CoV-2 pneumonia and compare symptoms and laboratory and radiological findings in patients with and without asthma with SARS-CoV-2 pneumonia.METHODS: We included 106 patients between March 4 and April 6, 2020, who were hospitalized in the Chest Diseases Department of Strasbourg University Hospital; 23 had asthma. To assess the patients' asthma status, 3 periods were defined: the last month before the onset of COVID-19 symptoms (p1), prehospitalization (p2), and during hospitalization (p3). Severe asthma exacerbations were defined according to Global INitiative for Asthma guidelines during p1 and p2. During p3, we defined severe asthma deterioration as the onset of breathlessness and wheezing requiring systemic corticosteroids and inhaled b2 agonist. RESULTS: We found no significant difference between patients with and without asthma in terms of severity (length of stay, maximal oxygen flow needed, noninvasive ventilation requirement, and intensive care unit transfer); 52.2% of the patients with asthma had Global INitiative for Asthma step 1 asthma. One patient had a severe exacerbation during p1, 2 patients during p2, and 5 patients were treated with systemic corticosteroids and inhaled b2 agonist during p3. CONCLUSIONS: Our results demonstrate that patients with asthma appeared not to be at risk for severe SARS-CoV-2 pneumonia. Moreover, SARS-CoV-2 pneumonia did not induce severe asthma exacerbation.

This study investigates a torque estimation method for muscle fatigue tracking, using stimulus evoked electromyography (eEMG) in the context of a functional electrical stimulation (FES) rehabilitation system. Although FES is able to effectively restore motor function in spinal cord injured (SCI) individuals, its application is inevitably restricted by muscle fatigue. In addition, the sensory feedback indicating fatigue is missing in such patients. Therefore, torque estimation is essential to provide feedback or feedforward signal for adaptive FES control. In this work, a fatigue-inducing protocol is conducted on five SCI subjects via transcutaneous electrodes under isometric condition, and eEMG signals are collected by surface electrodes. A myoelectrical mechanical muscle model based on the Hammerstein structure with eEMG as model input is employed to capture muscle contraction dynamics. It is demonstrated that the correlation between eEMG and torque is time-varying during muscle fatigue. Compared to conventional fixed-parameter models, the adaptedparameter model shows better torque prediction performance in fatiguing muscles. It motivates us to use a Kalman filter with forgetting factor for estimating the time-varying parameters and for tracking muscle fatigue. The assessment with experimental data reveals that the identified eEMG-to-torque model properly predicts fatiguing muscle behavior. Furthermore, the performance of the time-varying parameter estimation is efficient, suggesting that real-time tracking is feasible with a Kalman filter and driven by eEMG sensing in the application of FES.

TB diagnostic delay needs to be reduced in France. This may be achieved through improved access to care, earlier hospital referral, and less use of empirical antibiotic treatment.