Critically ill patients are exposed to many sources of discomfort and traumatic experiences, especially if they require invasive mechanical ventilation (IMV). Dyspnea, or sensation of not getting enough air - suffocation is the most common and distressing symptom experienced by IMV patients, far more unpleasant than pain. But, contrarily to pain, dyspnea has received only little attention and is still markedly under-recognized in IMV patients. Moreover, given the deleterious short- and long-terms consequences of letting IMV patients with dyspnea, its assessment and treatment figures among the main next great cause in critical healthcare. However, dyspnea assessment in IMV patients is a challenge since many of them cannot express their suffering (e.g. sedative drugs, mouth tubes). Dyspnea observation scales (DOS) are promising alternatives that allow to strongly suspecting dyspnea. These scales encompass the dyspnea multidimensionality assessing the respiratory drive (respiratory rate, excessive use neck muscle, nasal flaring), neurovegetative signs (heart rate) and emotions (fearful face). DOS allows calculation of scores strongly correlated with dyspnea in IMV communicative patients and responsive to dyspnea treatment even in noncommunicative ones. However these scales (1) require human resources, (2) still elicit caregivers' subjectivity (fearful face), and (3) are discontinuous, whereas dyspnea is unpredictable, and thus may lead to false appreciation of clinical deterioration. Thus there is an urgent unmet need for technology-enhanced clinical surveillance tools that reliably detect dyspnea in IMV patients and tailor its relief. Infrared thermal imaging (IRTI) offers a unique opportunity to automatically and continuously compute DOS. Indeed, it has been demonstrated as reliable to measure heart and respiratory rate in patients and detect facial expressions even during surgical intervention. The study goal is to prove the concept that IRTI camera device is feasible and reliable to strongly suspect dyspnea, based on the calculation of DOS including heart and respiratory rate, facial expression of fear, and activation of Alae nasi muscle, in IMV patients experiencing an asphyxial threat during a spontaneous breathing trial. The second study goal is to assess the performance of of this multidimensional video taped monitoring to predict the outcome of the spontaneous breathing trial. This project deals with the perspective that artificial intelligence and the development of autonomous patient-machine interfaces will give access to patients' emotions by the analysis of behaviors including facial expressions, in order to improve comfort and reduce traumatic memories of the ICU stay.