Incremental and online learning algorithms are more relevant in the data mining context because of the increasing necessity to process data streams. In this context, the target function may change over time, an inherent problem of online learning (known as concept drift). In order to handle concept drift regardless of the learning model, we propose new methods to monitor the performance metrics measured during the learning process, to trigger drift signals when a significant variation has been detected. To monitor this performance, we apply some probability inequalities that assume only independent, univariate and bounded random variables to obtain theoretical guarantees for the detection of such distributional changes. Some common restrictions for the online change detection as well as relevant types of change (abrupt and gradual) are considered. Two main approaches are proposed, the first one involves moving averages and is more suitable to detect abrupt changes. The second one follows a widespread intuitive idea to deal with gradual changes using weighted moving averages. The simplicity of the proposed methods, together with the computational efficiency make them very advantageous. We use a Naïve Bayes classifier and a Perceptron to evaluate the performance of the methods over synthetic and real data.
Objective: This study aims to analyse the number and characteristics of calls made to the Málaga Prehospital Emergency Service (PES) for suicidal behavior based on sociodemographic, temporal, and health care variables.Method: This is a retrospective, descriptive study that records all calls made to the PES due to suicidal behavior (suicide attempts and completed suicides) in 2014. Sociodemographic variables (age, sex, and health district), variables related to the calls (time-slot, degree of sunlight, type of day, month, season of the year, prioritization, and number of resources mobilized) were extracted from these calls. The number of cases and percentages were presented for the qualitative variables. The rates per 100,000 were calculated by sex and health district and presented with the corresponding 95% confidence interval (CI).Results: Of the total valid calls to PES (n = 181,824), 1,728 calls were made due to suicidal behavior (0.9%). The mean age was 43.21 (±18) years, 57.4% were women, and the rate was 112.1 per 100,000 inhabitants. The calls due to suicidal behavior were in the younger-middle age segment, in the time-slot between 16 and 23 h and during daylight hours, on bank holidays, in spring and summer in comparison with winter, and with a peak of calls in August. The majority of these calls were classified as undelayable emergencies and mobilized one health resource.Conclusions: Prehospital emergency services are the first contact to the sanitary services of persons or families with suicide attempts. This information should be a priority to offer a complete overview of the suicide behavior.
Shared control is a strategy used in assistive platforms to combine human and robot orders to achieve a goal. Collaborative control is a specific shared control approach in which user's and robot's commands are merged into an emergent one in a continuous way. Robot commands tend to improve efficiency and safety. However, sometimes assistance can be rejected by users when their commands are too altered. This provokes frustration and stress and, usually, decreases emergent efficiency. To improve acceptance, robot navigation algorithms can be adapted to mimic human behavior when possible. We propose a novel variation of the well known Dynamic Window Approach (DWA) that we call Biomimetical DWA (BDWA). BDWA relies on a reward function extracted from real traces from volunteers presenting different motor disabilities navigating in a hospital environment using a rollator for support. We have compared BDWA with other reactive algorithms in terms of similarity to paths completed by people with disabilities using a robotic rollator in a rehabilitation hospital unit. BDWA outperforms all tested algorithms in terms of likeness to human paths and success rate.
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