BioGlass: Physiological Parameter Estimation Using a Head-mounted Wearable Device

This work explores the feasibility of using sensors embedded in Google Glass, a head-mounted wearable device, to measure physiological signals of the wearer. In particular, we develop new methods to use Glass’s accelerometer, gyroscope, and camera to extract pulse and respiratory rates of 12 participants during a controlled experiment. We show it is possible to achieve a mean absolute error of 0.83 beats per minute (STD: 2.02) for heart rate and 1.18 breaths per minute (STD: 2.04) for respiration rate when considering different combinations of sensors. These results included testing across sitting, supine, and standing still postures before and after physical exercise

BioWatch: Estimation of Heart and Breathing Rates from Wrist Motions

Continued developments of sensor technology including hardware miniaturization and increased sensitivity have enabled the development of less intrusive methods to monitor physiological parameters during daily life. In this work, we present methods to recover cardiac and respiratory parameters using accelerometer and gyroscope sensors on the wrist. We demonstrate accurate measurements in a controlled laboratory study where participants (n = 12) held three different positions (standing up, sitting down and lying down) under relaxed and aroused conditions. In particular, we show it is possible to achieve a mean absolute error of 1.27 beats per minute (STD: 3.37) for heart rate and 0.38 breaths per minute (STD: 1.19) for breathing rate when comparing performance with FDA-cleared sensors. Furthermore, we show comparable performance with a state-of-the-art wrist-worn heart rate monitor, and when monitoring heart rate of three individuals during two consecutive nights of in-situ sleep measurements.National Science Foundation (U.S.) (CCF-1029585)Samsung (Firm). Think Tank TeamMIT Media Lab Consortiu

Análisis de aplicaciones móviles para el control del ritmo cardíaco

Este trabajo fin de grado (TFG) tiene como objetivo realizar un análisis del estado actual de las aplicaciones móviles destinadas al control del ritmo cardíaco. Para este propósito, se ha hecho un estudio en dos campos. En primer lugar se han revisado las fuentes literarias que versan sobre todo lo relacionado con lo anteriormente expuesto: control del ritmo cardiaco con dispositivos móviles, técnicas de extracción del ritmo cardíaco, algoritmos de procesamiento de la señal para la obtención del ritmo cardíaco, comparativas de aplicaciones comerciales destinadas a tal propósito, comparativas y validez de estas técnicas frente a técnicas tradicionales de medición del ritmo cardíaco, etc. En segundo lugar, de igual modo, se ha realizado también una búsqueda de las aplicaciones móviles disponibles para este cometido en las dos principales tiendas de aplicaciones hoy en día, Google Play eiTunes Store, las cuales hacen referencia a sistemas Android e iOS respectivamente. También, aunque ya en desuso, se ha incluido el sistema operativo Windows Phone.Grado en Ingeniería de Tecnologías de Telecomunicació

A deep learning approach for lower back-pain risk prediction during manual lifting

Occupationally-induced back pain is a leading cause of reduced productivity in industry. Detecting when a worker is lifting incorrectly and at increased risk of back injury presents significant possible benefits. These include increased quality of life for the worker due to lower rates of back injury and fewer workers' compensation claims and missed time for the employer. However, recognizing lifting risk provides a challenge due to typically small datasets and subtle underlying features in accelerometer and gyroscope data. A novel method to classify a lifting dataset using a 2D convolutional neural network (CNN) and no manual feature extraction is proposed in this paper; the dataset consisted of 10 subjects lifting at various relative distances from the body with 720 total trials. The proposed deep CNN displayed greater accuracy (90.6%) compared to an alternative CNN and multilayer perceptron (MLP). A deep CNN could be adapted to classify many other activities that traditionally pose greater challenges in industrial environments due to their size and complexity.Comment: 21 pages, 10 figure

Encapsulation of implantable microsensors

Heart function monitoring by attaching an accelerometer directly to the heart ventricle has been established as an effective way of diagnosing ischemia. The method holds a number of advantages over conventional monitoring techniques: high specificity and accuracy surpassing that of electrocardiography, and the ability to conduct non-stop monitoring unlike x-ray imaging. To this date, the drawback has been that the accelerometer-based devices have been too large to be used in the postoperative period, when the patient’s chest is closed. This period is of great interest.The PhD project has focused on developing a heart monitoring device intended to be used on patients recovering from a Coronary Artery Bypass Graft. The device is intended to be used during surgery and for the subsequent recovery period (3-5 days). The project has employed commercial 3-axis accelerometers.This PhD project has contributed to four different generations of devices, each one featuring incremental improvements. The first generation validated the concept, the second outlined the form factor of the device, and the third added extra functionality and revised the form of the implant. The fourth generation device also featured a newer, more compact sensor, which in turn, allowed to further miniaturize the device and evaluate different implant shapes. This evolutionary approach allowed us to formulate testing methodology for the devices. The latest generation devices underwent tests of: leakage current according to IEC60601 standard (current below 0.01 mA), including after cyclical loading of the capsule-cable joint, pull-out force measurements, implant stability evaluation that yielded tilt of no more than 4 degrees

Diseño, caracterización y evaluación de electrodos capacitivos para la medida de ECG y EEG

Este trabajo consiste en el diseño de unos electrodos capacitivos para poder medir el ECG y EEG mediante un método lo menos invasivo posible. En la memoria se encuentra una descripción detallada de los cálculos realizados y las características de estos electrodos. Para poder diseñarlos, se ha hecho un breve resumen de los posibles circuitos que se pueden utilizar, de los cuales se han seleccionado los dos que se ajustan mejor al estudio (amplificador de transimpedancia y amplificador seguidor). A continuación, se ha realizado el cálculo del ruido de estos circuitos simulándolos con tres amplificadores operacionales diferentes (LT1056, AD8605 y OPA140). Finalmente, el circuito de amplificador seguidor basado en el OPA140 ha sido el circuito con menos ruido, con un filtro pasa-banda y un buffer de salida. Además, junto a las placas del electrodo se ha diseñado, asimismo, la de control. Esta placa permite procesar la medida de los electrodos y enviarlas a un ordenador personal. La comunicación con el ordenador personal está aislada. Desafortunadamente, han surgido varios problemas con la empresa proveedora de los componentes y de las placas y estas pruebas no podrán hacerse hasta pasada la fecha de entrega de esta memoria, debido a que los componentes llegan más tarde de dicha fecha. En la defensa oral de este trabajo se defenderá su orientación y resultados y se podrá aportar la verificación señalada

Unobtrusive Monitoring of Heart Rate and Respiration Rate during Sleep

Sleep deprivation has various adverse psychological and physiological effects. The effects range from decreased vigilance causing an increased risk of e.g. traffic accidents to a decreased immune response causing an increased risk of falling ill. Prevalence of the most common sleep disorder, insomnia can be, depending on the study, as high as 30 % in adult population. Physiological information measured unobtrusively during sleep can be used to assess the quantity and the quality of sleep by detecting sleeping patterns and possible sleep disorders. The parameters derived from the signals measured with unobtrusive sensors may include all or some of the following: heartbeat intervals, respiration cycle lengths, and movements. The information can be used in wellness applications that include self-monitoring of the sleep quality or it can also be used for the screening of sleep disorders and in following-up of the effect of a medical treatment. Unobtrusive sensors do not cause excessive discomfort or inconvenience to the user and are thus suitable for long-term monitoring. Even though the monitoring itself does not solve the sleeping problems, it can encourage the users to pay more attention on their sleep. While unobtrusive sensors are convenient to use, their common drawback is that the quality of the signals they produce is not as good as with conventional measurement methods. Movement artifacts, for example, can make the detection of the heartbeat intervals and respiration impossible. The accuracy and the availability of the physiological information extracted from the signals however depend on the measurement principle and the signal analysis methods used. Three different measurement systems were constructed in the studies included in the thesis and signal processing methods were developed for detecting heartbeat intervals and respiration cycle lengths from the measured signals. The performance of the measurement systems and the signal analysis methods were evaluated separately for each system with healthy young adult subjects. The detection of physiological information with the three systems was based on the measurement of ballistocardiographic and respiration movement signals with force sensors placed under the bedposts, the measurement of electrocardiographic (ECG) signal with textile electrodes attached to the bed sheet, and the measurement of the ECG signal with non-contact capacitive electrodes. Combining the information produced by different measurement methods for improving the detection performance was also tested. From the evaluated methods, the most accurate heartbeat interval information was obtained with contact electrodes attached to the bed sheet. The same method also provided the highest heart rate detection coverage. This monitoring method, however, has a limitation that it requires a naked upper body, which is not necessarily acceptable for everyone. For respiration cycle length detection, better results were achieved by using signals recorded with force sensors placed under a bedpost than when extracting the respiration information from the ECG signal recorded with textile bed sheet electrodes. From the data quality point of view, an ideal night-time physiological monitoring system would include a contact ECG measurement for the heart rate monitoring and force sensors for the respiration monitoring. The force sensor signals could also be used for movement detection