Chronic migraine classification: current knowledge and future perspectives

In the field of so-called chronic daily headache, it is not easy for migraine that worsens progressively until it becomes daily or almost daily to find a precise and universally recognized place within the current international headache classification systems. In line with the 2006 revision of the second edition of the International Classification of Headache Disorders (ICHD-2R), the current prevailing opinion is that this headache type should be named chronic migraine (CM) and be characterized by the presence of at least 15 days of headache per month for at least 3 consecutive months, with headache having the same clinical features of migraine without aura for at least 8 of those 15 days. Based on much evidence, though, a CM with the above characteristics appears to be a heterogeneous entity and the obvious risk is that its definition may be extended to include a variety of different clinical entities. A proposal is advanced to consider CM a subtype of migraine without aura that is characterized by a high frequency of attacks (10–20 days of headache per month for at least 3 months) and is distinct from transformed migraine (TM), which in turn should be included in the classification as a complication of migraine. Therefore, CM should be removed from its current coding position in the ICHD-2 and be replaced by TM, which has more restrictive diagnostic criteria (at least 20 days of headache per month for at least 1 year, with no more than 5 consecutive days free of symptoms; same clinical features of migraine without aura for at least 10 of those 20 days)

Overview of diagnosis and management of paediatric headache. Part I: diagnosis

Headache is the most common somatic complaint in children and adolescents. The evaluation should include detailed history of children and adolescents completed by detailed general and neurological examinations. Moreover, the possible role of psychological factors, life events and excessively stressful lifestyle in influencing recurrent headache need to be checked. The choice of laboratory tests rests on the differential diagnosis suggested by the history, the character and temporal pattern of the headache, and the physical and neurological examinations. Subjects who have any signs or symptoms of focal/progressive neurological disturbances should be investigated by neuroimaging techniques. The electroencephalogram and other neurophysiological examinations are of limited value in the routine evaluation of headaches. In a primary headache disorder, headache itself is the illness and headache is not attributed to any other disorder (e.g. migraine, tension-type headache, cluster headache and other trigeminal autonomic cephalgias). In secondary headache disorders, headache is the symptom of identifiable structural, metabolic or other abnormality. Red flags include the first or worst headache ever in the life, recent headache onset, increasing severity or frequency, occipital location, awakening from sleep because of headache, headache occurring exclusively in the morning associated with severe vomiting and headache associated with straining. Thus, the differential diagnosis between primary and secondary headaches rests mainly on clinical criteria. A thorough evaluation of headache in children and adolescents is necessary to make the correct diagnosis and initiate treatment, bearing in mind that children with headache are more likely to experience psychosocial adversity and to grow up with an excess of both headache and other physical and psychiatric symptoms and this creates an important healthcare problem for their future life

Formal methods for semi-autonomous driving

We give an overview of the main challenges in the specification, design, and verification of human cyber-physical systems, with a special focus on semi-autonomous vehicles. We identify unique characteristics of formal modeling, specification, verification and synthesis in this domain. Some initial results and design princIPles are presented along with directions for future work

Verifying Robustness of Human-Aware Autonomous Cars

As human-robot systems make their ways into our every day life, safety has become a core concern of the learning algorithms used by such systems. Examples include semi-autonomous vehicles such as automobiles and aircrafts. The robustness of controllers in such systems relies on the accuracy of models of human behavior. In this paper, we propose a systematic methodology for analyzing the robustness of learning-based control of human-cyber-physical systems. We focus on the setting where human models are learned from data, with humans modeled as approximately rational agents optimizing their reward functions. In this setting, we provide a novel optimization-driven approach to find small deviations in learned human behavior that lead to violation of desired (safety) objectives. Our approach is experimentally validated via simulation for the application of autonomous driving

Formal methods for semi-autonomous driving

We give an overview of the main challenges in the specification, design, and verification of human cyber-physical systems, with a special focus on semi-autonomous vehicles. We identify unique characteristics of formal modeling, specification, verification and synthesis in this domain. Some initial results and design princIPles are presented along with directions for future work

Verifying Robustness of Human-Aware Autonomous Cars

As human-robot systems make their ways into our every day life, safety has become a core concern of the learning algorithms used by such systems. Examples include semi-autonomous vehicles such as automobiles and aircrafts. The robustness of controllers in such systems relies on the accuracy of models of human behavior. In this paper, we propose a systematic methodology for analyzing the robustness of learning-based control of human-cyber-physical systems. We focus on the setting where human models are learned from data, with humans modeled as approximately rational agents optimizing their reward functions. In this setting, we provide a novel optimization-driven approach to find small deviations in learned human behavior that lead to violation of desired (safety) objectives. Our approach is experimentally validated via simulation for the application of autonomous driving

Information gathering actions over human internal state

Much of estimation of human internal state (goal, intentions, activities, preferences, etc.) is passive: an algorithm observes human actions and updates its estimate of human state. In this work, we embrace the fact that robot actions affect what humans do, and leverage it to improve state estimation. We enable robots to do active information gathering, by planning actions that probe the user in order to clarify their internal state. For instance, an autonomous car will plan to nudge into a human driver's lane to test their driving style. Results in simulation and in a user study suggest that active information gathering significantly outperforms passive state estimation

Robust subspace system identification via weighted nuclear norm optimization

Subspace identification is a classical and very well studied problem in system identification. The problem was recently posed as a convex optimization problem via the nuclear norm relaxation. Inspired by robust PCA, we extend this framework to handle outliers. The proposed framework takes the form of a convex optimization problem with an objective that trades off fit, rank and sparsity. As in robust PCA, it can be problematic to find a suitable regularization parameter. We show how the space in which a suitable parameter should be sought can be limited to a bounded open set of the two-dimensional parameter space. In practice, this is very useful since it restricts the parameter space that is needed to be surveyed

Planning for cars that coordinate with people: leveraging effects on human actions for planning and active information gathering over human internal state

Traditionally, autonomous cars treat human-driven vehicles like moving obstacles. They predict their future trajectories and plan to stay out of their way. While physically safe, this results in defensive and opaque behaviors. In reality, an autonomous car’s actions will actually affect what other cars will do in response, creating an opportunity for coordination. Our thesis is that we can leverage these responses to plan more efficient and communicative behaviors. We introduce a formulation of interaction with human-driven vehicles as an underactuated dynamical system, in which the robot’s actions have consequences on the state of the autonomous car, but also on the human actions and thus the state of the human-driven car. We model these consequences by approximating the human’s actions as (noisily) optimal with respect to some utility function. The robot uses the human actions as observations of her underlying utility function parameters. We first explore learning these parameters offline, and show that a robot planning in the resulting underactuated system is more efficient than when treating the person as a moving obstacle. We also show that the robot can target specific desired effects, like getting the person to switch lanes or to proceed first through an intersection. We then explore estimating these parameters online, and enable the robot to perform active information gathering: generating actions that purposefully probe the human in order to clarify their underlying utility parameters, like driving style or attention level. We show that this significantly outperforms passive estimation and improves efficiency. Planning in our model results in coordination behaviors: the robot inches forward at an intersection to see if can go through, or it reverses to make the other car proceed first. These behaviors result from the optimization, without relying on hand-coded signaling strategies. Our user studies support the utility of our model when interacting with real users