System for robot control inspired by the functionality and organisation of the human nervous system

This paper presents a model of a control system for robot systems inspired by the functionality and organisation of human neuroregulatory system. Our model was specified using software agents within a formal framework and implemented through Web Services. This approach allows the implementation of the control logic of a robot system with relative ease, in an incremental way, using the addition of new control centres to the system as its behaviour is observed or needs to be detailed with greater precision, without the need to modify existing functionality. The tests performed verify that the proposed model has the general characteristics of biological systems together with the desirable features of software, such as robustness, flexibility, reuse and decoupling.

A low cost system for the prediagnosis of the human tremor

Human tremor can be defined as a somewhat rhythmic and quick movement of one or more body parts. In some people, it is a symptom of a neurological disorder. From the mathematical point of view, human tremor can be defined as a weighted contribution of different sinusoidal signals which causes oscillations of some parts of the body. This sinusoidal is repeated over time, but its amplitude and frequency change slowly. This is why amplitude and frequency are considered important factors in the tremor characterization, and thus for its diagnosis. In this paper, a tool for the prediagnosis of the human tremor is presented. This tool uses a low cost device (<$40) and allows to compute the main factors of the human tremor accurately. Real cases have been tested using the algorithms developed in this investigation. The patients suffered from different tremor severities, and the components of amplitude and frequency were computed using a series of tests. These additional measures will help the experts to make better diagnoses allowing them to focus on specific stages of the test or get an overview of these tests. From the experimental, we stated that not all tests are valid for every patient to give a diagnosis. Guided by years of experience, the expert will decide which test or set of tests are the most appropriate for a patient.

A Novel Prediction Method for Early Recognition of Global Human Behaviour in Image Sequences

Human behaviour recognition has been, and still remains, a challenging problem that involves different areas of computational intelligence. The automated understanding of people activities from video sequences is an open research topic in which the computer vision and pattern recognition areas have made big efforts. In this paper, the problem is studied from a prediction point of view. We propose a novel method able to early detect behaviour using a small portion of the input, in addition to the capabilities of it to predict behaviour from new inputs. Specifically, we propose a predictive method based on a simple representation of trajectories of a person in the scene which allows a high level understanding of the global human behaviour. The representation of the trajectory is used as a descriptor of the activity of the individual. The descriptors are used as a cue of a classification stage for pattern recognition purposes. Classifiers are trained using the trajectory representation of the complete sequence. However, partial sequences are processed to evaluate the early prediction capabilities having a specific observation time of the scene. The experiments have been carried out using the three different dataset of the CAVIAR database taken into account the behaviour of an individual. Additionally, different classic classifiers have been used for experimentation in order to evaluate the robustness of the proposal. Results confirm the high accuracy of the proposal on the early recognition of people behaviours.