Precisión del posicionamiento en interiores utilizando Bluetooth con diferentes técnicas de reconocimiento de patrones

Object indoor location is a field that receives much research effort but that is lacking enough maturity for its integration in popular devices like mobile phones. This paper describes the results of an experiment carried out to compare different pattern recognition algorithms in order to process the information from a set of Bluetooth transmitters, located in fixed positions, with the aim of locating an object in a precise position. Our conclusion is that the best algorithms, among the five we tested, are random forests and model-based clustering, which gave accuracies around 90%. We have also conducted experiments to analyse the influence of the number of Bluetooth transmitters and to determine the sets of features with better performance. The proposed approach is simple and gives 90% of accuracy for locating objects with 1 m precision, making it suitable for a wide range of applications.Facultad de Informátic

Precisión del posicionamiento en interiores utilizando Bluetooth con diferentes técnicas de reconocimiento de patrones

Object indoor location is a field that receives much research effort but that is lacking enough maturity for its integration in popular devices like mobile phones. This paper describes the results of an experiment carried out to compare different pattern recognition algorithms in order to process the information from a set of Bluetooth transmitters, located in fixed positions, with the aim of locating an object in a precise position. Our conclusion is that the best algorithms, among the five we tested, are random forests and model-based clustering, which gave accuracies around 90%. We have also conducted experiments to analyse the influence of the number of Bluetooth transmitters and to determine the sets of features with better performance. The proposed approach is simple and gives 90% of accuracy for locating objects with 1 m precision, making it suitable for a wide range of applications.Facultad de Informátic

Heart of Darkness: Heart Rate Variability on Patients with Risk of Suicide

Heart Rate Variability (HRV) is an emerging research field in the study of diverse pathologies, as long as it allows considering another measurement for detecting possible aggravations. The aim of this work is to study the applicability of the analysis of HRV in order to establish if a person is at risk of suffering from suicidal ideation. This work includes the development and testing of a heart rate acquisition and automatic analysis system, with friendly software for clinicians, customized to the necessities of an emergency unit. Furthermore, it includes the analysis of the obtained data with the purpose of assessing possible correlations between HRV parameters and personality impulsive traits. 20 patients and 10 normal cases were selected to develop this pilot study. Results show significant statistical difference (p<0.05) among patients and normal cases for pNN50, IRRR, MADRR, total HRV power, Approximate Entropy and Fractal Dimension

Un sistema de diálogo para la consulta de correo electrónico en lenguaje natural

En los últimos años ha surgido un nuevo tipo de interfaces hombre-máquina que combina varias tecnologías de habla con el fin de permitir a las personas conversar con los ordenadores mediante la voz. En este artículo, presentamos uno de estos sistemas, diseñado para facilitar el acceso a un servidor de correo electrónico usando lenguaje natural.Last years have witnessed the emergence of a new class of human-computer interfaces that combine several human language technologies to enable humans to converse with computers using speech. In this paper, we describe one of these systems, devised for accessing an e-mail server using natural language.Este trabajo ha sido financiado parcialmente mediante los Proyectos CICYT TIC2000-1104-C02-01 y TIC2000-0370-C02-01

Detection of Heart Beat Positions in ECG Recordings: A Lead-Dependent Algorithm

This paper proposes a computerized heartbeat detection method in single-channel electrocardiograms (ECGs). First, the well-known Pan-Tompkins technique was implemented, and next, a channel-dependent version was developed, by adjusting threshold values and reducing false QRS detections. The algorithms were tested with the MIT-BIH Arrhythmia Database (original algorithm), and with the St. Petersburg Database (modified version). When validating the performances of the original Pan-Tompkins algorithm, we have achieved a sensitivity of <em>Se</em> = 99.81, at a positive predictivity (<em>P</em>⁺) = 99.85%. The F-Score was 0.9587, and the RMS RR Interval Error (RMSRRIE) resulted to be 4,480.46 ms. When analysing the performance of the modified algorithm, results provided an average value of <em>Se</em> = 99.92%, <em>P</em>⁺ = 99.98%, F-Score = 0.9718, and a mean value of 111.05 ms. for the RMSRRIE. In conclusion, the improved Pan-Tompkins algorithm provides higher values for sensitivity and positive predictivity, increased F-Score, and it significantly reduces the temporal error when estimating the positions of QRS complexes. Thus, it could be used as a starting point to detect heartbeat positions in more sophisticated computerized detection systems.\u

VARVI : a software tool for analyzing the variability of the heart rate in response to visual stimuli

This paper describes a free software tool (VARVI) developed in our research group to facilitate the analysis of heart rate variability (HRV) in response to different visual stimuli. This tool was developed after the realization that this type of studies are becoming to be used in fields such as psychiatry, psychology and marketing and that there are no specific tools for this purpose

Specific features of the Galician language and implications for speech technology development

International audienc

VARSE: Android app for real-time acquisition and analysis of heart rate signals

Financiado para publicación en acceso aberto: Universidade de Vigo/CISUGBackground and objective: nowadays, numerous mobile applications capable of measuring the Heart Rate (HR) are continuously being launched. However, these tools do not allow to record and label the acquired HR signals while users are doing activities such as practising sports or viewing images. They do not allow to perform simultaneous HR acquisition and real-time HRV analysis, either. VARSE is an app for Android mobile devices capable of acquiring and labelling HR signals and of performing real-time HRV analysis.Methods: VARSE was developed for Android devices. It includes functionalities to acquire HR signals from any Bluetooth device that implements the Heart Rate Profile specification (such as a chest strap), while labelling segments of the HR data in different situations. Not only can these signals be stored, but also time and frequency HRV analyses can be carried out over them. The application is distributed under the MIT license [1], and it is also available to be installed via Google Play [2]. Functionality, ease-of-use and friendliness of VARSE were evaluated employing an User Experience Questionnaire (UEQ). Its reliability was proven by comparative studies against other existing acquisition (gVARVI) or HRV analysis (RHRV) software.Results: high values were obtained for all the dimensions evaluated in the UEQ, proving the quality of the application, as well as its ease-of-use and efficiency. Both HR signal acquisition and HRV analysis yielded results similar to the ones obtained using other applications.Conclusions: VARSE is a tool with complete functionality, that can be easily downloaded or installed on Android mobile devices. It can be used by anyone who wishes to record HR signals while performing different activities, and also by the medical scientific community to perform real-time HRV analyses easily. Future versions will improve its capabilities and allow its integration with other open source applications.Xunta de Galicia | Ref. ED431E 2018/0

Heart of Darkness : Heart Rate Variability on patients with risk of suicide

Heart Rate Variability (HRV) is an emerging research field in the study of diverse pathologies, as long as it allows considering another measurement for detecting possible aggravations. The aim of this work is to study the applicability of the analysis of HRV in order to establish if a person is at risk of suffering from suicidal ideation. This work includes the development and testing of a heart rate acquisition and automatic analysis system, with friendly software for clinicians, customized to the necessities of an emergency unit. Furthermore, it includes the analysis of the obtained data with the purpose of assessing possible correlations between HRV parameters and personality impulsive traits. 20 patients and 10 normal cases were selected to develop this pilot study. Results show significant statistical difference (p<0.05) among patients and normal cases for pNN50, IRRR, MADRR, total HRV power, Approximate Entropy and Fractal Dimension

Heart rate variability analysis with the R package RHRV

This book introduces readers to the basic concepts of Heart Rate Variability (HRV) and its most important analysis algorithms using a hands-on approach based on the open-source RHRV software. HRV refers to the variation over time of the intervals between consecutive heartbeats. Despite its apparent simplicity, HRV is one of the most important markers of the autonomic nervous system activity and it has been recognized as a useful predictor of several pathologies. The book discusses all the basic HRV topics, including the physiological contributions to HRV, clinical applications, HRV data acquisition, HRV data manipulation and HRV analysis using time-domain, frequency-domain, time-frequency, nonlinear and fractal techniques. Detailed examples based on real data sets are provided throughout the book to illustrate the algorithms and discuss the physiological implications of the results. Offering a comprehensive guide to analyzing beat information with RHRV, the book is intended for masters and Ph.D. students in various disciplines such as biomedical engineering, human and veterinary medicine, biology, and pharmacy, as well as researchers conducting heart rate variability analyses on both human and animal data