Thomas Hoyoux

Tests of a New Drowsiness Characterization and Monitoring System Based on Ocular Parameters

Drowsiness is the intermediate state between wakefulness and sleep. It is characterized by impairments of performance, which can be very dangerous in many activities and can lead to catastrophic accidents in transportation or in industry. There is thus an obvious need for systems that are able to continuously, objectively, and automatically estimate the level of drowsiness of a person busy at a task. We have developed such a system, which is based on the physiological state of a person, and, more specifically, on the values of ocular parameters extracted from images of the eye (photooculography), and which produces a numerical level of drowsiness. In order to test our system, we compared the level of drowsiness determined by our system to two references: (1) the level of drowsiness obtained by analyzing polysomnographic signals; and (2) the performance of individuals in the accomplishment of a task. We carried out an experiment in which 24 participants were asked to perform several Psychomotor Vigilance Tests in different sleep conditions. The results show that the output of our system is well correlated with both references. We determined also the best drowsiness level threshold in order to warn individuals before they reach dangerous situations. Our system thus has significant potential for reliably quantifying the level of drowsiness of individuals accomplishing a task and, ultimately, for preventing drowsiness-related accidents.

A new computer vision-based system to help clinicians objectively assess visual pursuit with the moving mirror stimulus for the diagnosis of minimally conscious state

Minimally conscious state (MCS) is a neurological syndrome in which the patient shows signs of partial consciousness after having emerged from unresponsive wakefulness syndrome (UWS), which itself follows a state of coma. Distinguishing between MCS and UWS is complex and has major impact on the clinical management and prognosis of affected patients. Research on disorders of consciousness (DoC) has revealed that (1) visual pursuit, i.e. the ability of a patient to track a moving stimulus, is one of the most decisive clinical signs for establishing the MCS/UWS distinction, and that (2) the most effective moving stimulus for visual pursuit assessment is a mirror where the patient can see his/her own face. In clinical practice, while this guidance is widely followed, the visual pursuit ability is typically assessed on the basis of the clinicians opinion only, i.e. in a subjective thus biased manner. In this paper, we present a new system using cameras and computer vision techniques, which helps clinicians to objectify the assessment of visual pursuit. Our system is specifically designed to work with the moving mirror stimulus in order to follow the recommended, well-established clinical setup. We validate our system on healthy control subjects and give preliminary results obtained with DoC patients.

Objective assessment of visual pursuit in patients with disorders of consciousness: an exploratory study

Visual pursuit is a key marker of residual consciousness in patients with disorders of consciousness (DOC). Currently, its assessment relies on subjective clinical decisions. In this study, we explore the variability of such clinical assessments, and present an easy-to-use device composed of cameras and video processing algorithms that could help the clinician to improve the detection of visual pursuit in a clinical context. Visual pursuit was assessed by an experienced research neuropsychologist on 31 patients with DOC and on 23 healthy subjects, while the device was used to simultaneously record videos of both one eye and the mirror. These videos were then scored by three researchers: the experienced research neuropsychologist who did the clinical assessment, another experienced research neuropsychologist, and a neurologist. For each video, a consensus was decided between the three persons, and used as the gold standard of the presence or absence of visual pursuit. Almost 10% of the patients were misclassified at the bedside according to their consensus. An automatic classifier analyzed eye and mirror trajectories, and was able to identify patients and healthy subjects with visual pursuit, in total agreement with the consensus on video. In conclusion, our device can be used easily in patients with DOC while respecting the current guidelines of visual pursuit assessment. Our results suggest that our material and our classification method can identify patients with visual pursuit, as well as the three researchers based on video recordings can.

Can computer vision problems benefit from structured hierarchical classification

Research in the field of supervised classification has mostly focused on the standard, so-called “flat” classification approach, where the problem classes live in a trivial, one-level semantic space. There is however an increasing interest in the hierarchical classification approach, where a performance gain is expected by incorporating prior taxonomic knowledge about the classes into the learning process. Intuitively, the hierarchical approach should be beneficial in general for the classification of visual content, as suggested by the fact that humans seem to organize objects into hierarchies based on visually perceived similarities. In this paper, we provide an analysis that aims to determine the conditions under which the hierarchical approach can consistently give better performances than the flat approach for the classification of visual content. In particular, we (1) show how hierarchical methods can fail to outperform flat methods when applied to real vision-based classification problems, and (2) investigate the underlying reasons for the lack of improvement, by applying the same methods to synthetic datasets in a simulation. Our conclusion is that the use of high-level hierarchical feature representations is crucial for obtaining a performance gain with the hierarchical approach, and that poorly chosen prior taxonomies hinder this gain even though proper high-level features are used.