Design techniques for smart and energy-efficient wireless body sensor networks

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Informática, Departamento de Arquitectura de Computadores y Automática, leída el 26/10/2012Las redes inalámbricas de sensores corporales (en inglés: "wireless body sensor networks" o WBSNs) para monitorización, diagnóstico y detección de emergencias, están ganando popularidad y están llamadas a cambiar profundamente la asistencia sanitaria en los próximos años. El uso de estas redes permite una supervisión continua, contribuyendo a la prevención y el diagnóstico precoz de enfermedades, al tiempo que mejora la autonomía del paciente con respecto a otros sistemas de monitorización actuales. Valiéndose de esta tecnología, esta tesis propone el desarrollo de un sistema de monitorización de electrocardiograma (ECG), que no sólo muestre continuamente el ECG del paciente, sino que además lo analice en tiempo real y sea capaz de dar información sobre el estado del corazón a través de un dispositivo móvil. Esta información también puede ser enviada al personal médico en tiempo real. Si ocurre un evento peligroso, el sistema lo detectará automáticamente e informará de inmediato al paciente y al personal médico, posibilitando una rápida reacción en caso de emergencia. Para conseguir la implementación de dicho sistema, se desarrollan y optimizan distintos algoritmos de procesamiento de ECG en tiempo real, que incluyen filtrado, detección de puntos característicos y clasificación de arritmias. Esta tesis también aborda la mejora de la eficiencia energética de la red de sensores, cumpliendo con los requisitos de fidelidad y rendimiento de la aplicación. Para ello se proponen técnicas de diseño para reducir el consumo de energía, que permitan buscar un compromiso óptimo entre el tamaño de la batería y su tiempo de vida. Si el consumo de energía puede reducirse lo suficiente, sería posible desarrollar una red que funcione permanentemente. Por lo tanto, el muestreo, procesamiento, almacenamiento y transmisión inalámbrica tienen que hacerse de manera que se suministren todos los datos relevantes, pero con el menor consumo posible de energía, minimizando así el tamaño de la batería (que condiciona el tamaño total del nodo) y la frecuencia de recarga de la batería (otro factor clave para su usabilidad). Por lo tanto, para lograr una mejora en la eficiencia energética del sistema de monitorización y análisis de ECG propuesto en esta tesis, se estudian varias soluciones a nivel de control de acceso al medio y sistema operativo.Depto. de Arquitectura de Computadores y AutomáticaFac. de InformáticaTRUEunpu

Factores de riesgo asociados al aumento de los embarazos en adolescentes del municipio de Altagracia, departamento de Rivas. Enero-Diciembre del 2020

Con el Objetivo de identificar los factores de riesgo asociados al aumento de los embarazos en adolescentes del municipio de Altagracia, departamento de Rivas, se realizó un estudio del 1° de Enero 2017 al 31 de Diciembre del 2020. Diseño metodológico: se efectuó un estudio epidemiológico de casos y controles, en el municipio de Altagracia ubicado en la Isla de Ometepe, con una muestra de 42 adolescente que cumplieron con los criterios de inclusión y exclusión tanto de casos como de controles. Se aplicará la Razón de Probabilidad (Odds Ratio), para la categoría de mayor prevalencia de casos en relación a la categoría con menor prevalencia de casos; así como el intervalo de confianza al 95% asociados a la estimación puntual del riesgo relativo. A través de la tabla de 2x2. También fueron analizadas mediante la prueba de significación estadística X2, Valor de P, si hubo diferencias estadísticas significativas para las diferentes categorías de las variables independientes. Resultados: Los factores de riesgo con significancia estadísticas asociados al incremento de embarazos en la adolescencia son; religión católica; procedencia rural, estado civil soltera; hábitos tóxicos; aborto y embarazo anterior, uso de condones, riesgos individuales; riesgo culturales/familiares /comunidad.Conclusiones: se acepta la hipótesis alternativa “Los factores de riesgo asociados al incremento de los embarazos en la adolescencia son prevenible” y se rechaza la nula Palabras Claves: Factores de Riesgos, Riesgos sociales; individuales; culturales y psicológico; Adolescentes; Embaraz

Digital intimate partner violence among peruvian youths : validation of an instrument and a theoretical proposal

The present study presents psychometric information on a new instrument, the Digital Intimate Partner Violence Questionnaire (DIPVQ), and explores the similitudes and differences between in-person and digital-based abuses (those that involve the use of information and communication technologies [ICTs]). In all, 449 Peruvian students took part in the study (X = 21.2 years; SD = 4.3 years; 73% women). DIPVQ structure was determined by carrying out an exploratory factor analysis (EFA) with polychoric correlation matrices and oblique rotation. In-person violence was assessed using the Dating Violence Questionnaire (DVQ) and self-labeling questions (e.g., feeling trapped, afraid, and abused). Relationship satisfaction was assessed using the Perceived Relationship Quality Components–Short Form (PRQC-SF). EFA showed a two-scale structure for the DIPVQ: control-centered cyberabuse (N = 5; control, monitoring, and identity theft; EAP alpha = .96) and damage-centered cyberabuse (N = 7; unwanted sexual contents, blackmailing, and causing debts throughout ICT; Expected-A-Posteriori alpha = .97). DIPVQ had direct relationship to DVQ and self-labeling (p < .001; d = 0.38-1.18), and inverse to PRQC-SF (p = .11; d = .22-.33). Behaviors such as impersonation and monitoring were reported by more than 20% of participants. Online and offline victimization coexist in 42% of cases, while 3.6% of aggressions happened exclusively via ICT. DIPVQ is a valid and reliable measure of digital victimization. The controlcentered scale had a higher frequency, although the damage-centered scale had stronger relationship to feeling afraid and abused. While previous literature has classified online aggressions regarding their aesthetic appearance, it seems that their functional value (control vs. hurting) could provide a better framework for understanding these aggressions

A Wearable Device For Physical and Emotional Health Monitoring

Personal health monitoring systems are emerging as promising solutions to develop ultra-small, portable devices that can continuously monitor and process several vital body parameters. In this work, we present a wearable device for physical and emotional health monitoring. The device obtains user’s key physiological signals: ECG, respiration, Impedance Cardiogram (ICG), blood pressure and skin conductance and derives the user’s emotion states as well. We have developed embedded algorithms that process the bio-signals in real-time to detect any abnormalities (cardiac arrhythmias and morphology changes) in the ECG and to detect key parameters (such as the Pre- Ejection Period and fluid status level) from the ICG. We present a novel method to detect continuous beat-by-beat blood pressure from the ECG and ICG signals, as well as a realtime embedded emotion classifier that computes the emotion levels of the user. Emotions are classified according to their attractiveness (positive valence) or their averseness (negative valence) in the horizontal valence dimension. The excitement level induced by the emotions is represented by high to low positions in the vertical arousal dimension of the valence-arousal space. The signals are measured either intermittently by touching the metal electrodes on the device (for point-of-care testing) or continuously, using a chest strap for long term monitoring. The processed data from device is sent to a mobile phone using a Bluetooth Low Energy protocol. Our results show that the device can monitor the signals continuously, providing accurate detection of the motion state, for over 72 hours on a single battery charge

Energy-Aware Embedded Classifier Design for Real-Time Emotion Analysis

Detection and classification of human emotions from multiple bio-signals has a wide variety of applications. Though electronic devices are available in the market today that acquire multiple body signals, the classification of human emotions in real-time, adapted to the tight energy budgets of wearable embedded systems is a big challenge. In this paper we present an embedded classifier for real-time emotion classification. We propose a system that operates at different energy budgeted modes, depending on the available energy, where each mode is constrained by an operating energy bound. The classifier has an offline training phase where feature selection is performed for each operating mode, with an energy-budget aware algorithm that we propose. Across the different operating modes, the classification accuracy ranges from 95% - 75% and 89% - 70% for arousal and valence respectively. The accuracy is traded off for less power consumption, which results in an increased battery life of up to 7.7 times (from 146.1 to 1126.9 hours)

Design of Ultra-Low-Power Smart Wearable Systems

Latest progress in microelectronics have enabled a new generation of low cost, low power, miniaturized, yet, smart sensor nodes. This new generation of wearable sensor nodes promise to deploy automated complex bio-signals analysis. In this paper, we present INYU, a wearable sensor device for physical and emotional health monitoring. The device obtains key vital signs of the user, namely Electrocardiogram (ECG), respiration and skin conductance continuously. Using this information, INYU can deliver a novel real-time algorithm for on-line heart-beat classification and correction that relies on a probabilistic model to determine whether a heartbeat is likely to happen under certain timing conditions. Thus, using this algorithm INYU can quickly decide if a beat is occurring at an expected time or if there is a problem in the series (e.g., a skipped, an extra or a misplaced beat). This new algorithm has been integrated in the processing pipeline of automated Heart-Rate Variability (HRV) analysis, both for time-domain (RMSSD, SDNN) and frequency-domain (LF/HF) algorithms

Real-Time Probabilistic Heart Beat Classification and Correction for Embedded Systems

With the emergence of wearable and non-intrusive med- ical devices, one major challenge is the real-time analysis of the acquired signals in real-life and ambulatory con- ditions. This paper presents a lightweight algorithm for on-line heart beat classification and correction that relies on a probabilistic model to determine whether a heart beat is likely to happen under certain timing conditions or not. It can quickly decide if a beat is occurring at an expected time or if there is a problem in the series (e.g., a skipped, an extra or a misplaced beat). If an error is detected, the series is repaired accordingly. The algorithm has been carefully optimized to minimize the required processing power and memory usage in order to enable its real-time embedded implementation on a wearable sensing device. Our experimental results, based on the PhysioNet Fanta- sia database, show that the proposed algorithm achieves 99.5% sensitivity in the detection and correction of erro- neous beats. In addition, it features a fast response time when the activity level of the user changes, thus enabling its use in situations where the heart rate quickly changes

Estimation of Blood Pressure and Pulse Transit Time Using Your Smartphone

It is widely recognized today that there is an alarming rise of lifestyle-induced chronic diseases (e.g., type II diabetes) in our society. Therefore, a strong need exists for cost-effective and non-invasive devices that can measure blood pressure (BP) to monitor, diagnose and follow-up patients at risk, but also healthy population in general. One promising method for arterial BP estimation is to measure a surrogate marker of it, such as, Pulse Transit Time (PTT) and derive pressure values from it. However, current methods for measuring PTT require complex sensing and analysis circuitry and the related medical devices are expensive and inconvenient for the user to wear. In this paper, we present a new smartphone-based method to estimate PTT reliably and subsequently BP from the baseline sensors on smartphones. This new approach involves determining PTT by simultaneously measuring the time the blood leaves the heart, by recording the heart sound using the standard microphone of the phone and the time it reaches the finger, by measuring the pulse wave using the phone’s camera. Moreover, we also describe algorithms that can be executed directly on current smartphones to obtain clean and robust heart sound signals and to extract the pulse wave characteristics using smartphones. We also present methods to ensure a synchronous capture of the waveforms, which is essential to obtain reliable PTT values with inexpensive sensors. Our experiments show that the computational overhead of the proposed two-phase processing method is minimum, with the ability to reliably measure the PTT values in a fully accurate (beat-to-beat) fashion using directly state-of-the-art smartphones as medical devices

Desarrollo de una Aplicación Educativa para la asignatura de Matemática

El presente trabajo consiste en el desarrollo de una aplicación educativa para dispositivos móviles en la plataforma de App inventor 2, como apoyo al contenido Gráficas de la proporcionalidad directa b<x<c de la Unidad V: Proporcionalidad