Fast QRS Detection and ECG Compression Based on Signal Structural Analysis.

OBJECTIVE: This paper presents a fast approach to detect QRS complexes based on a simple analysis of the temporal ECG structure. METHODS: The ECG is processed through several steps involving noise removal, feature detection, and feature analysis. The obtained feature set, which holds most of the ECG information while requiring low data storage, constitutes a lossy compressed version of the ECG. RESULTS: The experiments, performed using 12 different ECG databases, emphasize the advantages of our proposal. For example, 130-min ECG recordings are processed in average in 0.77 s. Also, sensitivities and positive predictions surpass 99.9% in some databases, and a global data saving of 90.35% is achieved. CONCLUSION AND SIGNIFICANCE: When compared to other approaches, this study offers a parameterless and computationally efficient alternative for QRS complex detection and lossy ECG compression. Moreover, some of the presented techniques are general enough to be used by other ECG analysis tools. Finally, the documented source code corresponding to this study is publicly available.

High-Resolution Underwater Mapping Using Side-Scan Sonar.

The goal of this study is to generate high-resolution sea floor maps using a Side-Scan Sonar(SSS). This is achieved by explicitly taking into account the SSS operation as follows. First, the raw sensor data is corrected by means of a physics-based SSS model. Second, the data is projected to the sea-floor. The errors involved in this projection are thoroughfully analysed. Third, a probabilistic SSS model is defined and used to estimate the probability of each sea-floor region to be observed. This probabilistic information is then used to weight the contribution of each SSS measurement to the map. Because of these models, arbitrary map resolutions can be achieved, even beyond the sensor resolution. Finally, a geometric map building method is presented and combined with the probabilistic approach. The resulting map is composed of two layers. The echo intensity layer holds the most likely echo intensities at each point in the sea-floor. The probabilistic layer contains information about how confident can the user or the higher control layers be about the echo intensity layer data. Experimental results have been conducted in a large subsea region.

Trajectory-based visual localization in underwater surveying missions.

We present a new vision-based localization system applied to an autonomous underwater vehicle (AUV) with limited sensing and computation capabilities. The traditional EKF-SLAM approaches are usually expensive in terms of execution time; the approach presented in this paper strengthens this method by adopting a trajectory-based schema that reduces the computational requirements. The pose of the vehicle is estimated using an extended Kalman filter (EKF), which predicts the vehicle motion by means of a visual odometer and corrects these predictions using the data associations (loop closures) between the current frame and the previous ones. One of the most important steps in this procedure is the image registration method, as it reinforces the data association and, thus, makes it possible to close loops reliably. Since the use of standard EKFs entail linearization errors that can distort the vehicle pose estimations, the approach has also been tested using an iterated Kalman filter (IEKF). Experiments have been conducted using a real underwater vehicle in controlled scenarios and in shallow sea waters, showing an excellent performance with very small errors, both in the vehicle pose and in the overall trajectory estimates.

The uspIC: performing scan matching localization using an imaging sonar.

This paper presents a novel approach to localize an underwater mobile robot based on scan matching using a Mechanically Scanned Imaging Sonar (MSIS). When used to perform scan matching, this sensor presents some problems such as significant uncertainty in the measurements or large scan times, which lead to a motion induced distortion. This paper presents the uspIC, which deals with these problems by adopting a probabilistic scan matching strategy and by defining a method to strongly alleviate the motion induced distortion. Experimental results evaluating our approach and comparing it to previously existing methods are provided.

ACTYBOSS: activity, behavioral therapy in young subjects--after-school intervention pilot project on obesity prevention.

AIMS: To test the feasibility of a school-based intervention, which combines an incentive-driven physical activity program with lifestyle lectures, and its potential beneficial outcome on children's metabolic parameters. METHODS: We conducted a 6-month pilot intervention in two high schools in Mallorca, Spain, consisting of a program which involved free supervised exercise sessions and nutritional lectures, where children received credit points as a reward for the hours spent exercising and attendance to the lectures. The credit-earned points obtained were exchanged for gifts. We developed personalized cards and a web application for the participants to check the gifts they were eligible for (www.actyboss.com). Percentage body fat, percentage of fat-free mass and BMI were measured. Secondary measures included fitness parameters, blood pressure and blood lipids levels. 90 children signed up the consent form and 56 completed the program until the endpoint. RESULTS: We found a beneficial effect on body composition, fitness parameters, and systolic blood pressure in children who participated in ACTYBOSS compared to children who did not start the intervention. CONCLUSIONS: We describe the incentive-driven, after-school intervention pilot program to promote physical activity and a healthy lifestyle. The program had a positive effect on anthropometric measurements. A larger incentive-driven healthy lifestyle program is now ongoing.

Sonar sensor models and their application to mobile robot localization.

This paper presents a novel approach to mobile robot localization using sonar sensors. This approach is based on the use of particle filters. Each particle is augmented with local environment information which is updated during the mission execution. An experimental characterization of the sonar sensors used is provided in the paper. A probabilistic measurement model that takes into account the sonar uncertainties is defined according to the experimental characterization. The experimental results quantitatively evaluate the presented approach and provide a comparison with other localization strategies based on both the sonar and the laser. Some qualitative results are also provided for visual inspection.