BioInsights: Extracting personal data from "Still" wearable motion sensors

During recent years a large variety of wearable devices have become commercially available. As these devices are in close contact with the body, they have the potential to capture sensitive and unexpected personal data even when the wearer is not moving. This work demonstrates that wearable motion sensors such as accelerometers and gyroscopes embedded in head-mounted and wrist-worn wearable devices can be used to identify the wearer (among 12 participants) and his/her body posture (among 3 positions) from only 10 seconds of “still” motion data. Instead of focusing on large and apparent motions such as steps or gait, the proposed methods amplify and analyze very subtle body motions associated with the beating of the heart. Our findings have the potential to increase the value of pervasive wearable motion sensors but also raise important privacy concerns that need to be considered.National Science Foundation (U.S.). (CCF-1029585

High-Performance Accelerometer Based On Asymmetric Gapped Cantilevers For Physiological Acoustic Sensing

Continuous or mobile monitoring of physiological sounds is expected to play important role in the emerging mobile healthcare field. Because of the miniature size, low cost, and easy installation, accelerometer is an excellent choice for continuous physiological acoustic signal monitoring. However, in order to capture the detailed information in the physiological signals for clinical diagnostic purpose, there are more demanding requirements on the sensitivity/noise performance of accelerometers. In this thesis, a unique piezoelectric accelerometer based on the asymmetric gapped cantilever which exhibits significantly improved sensitivity is extensively studied. A meso-scale prototype is developed for capturing the high quality cardio and respiratory sounds on healthy people as well as on heart failure patients. A cascaded gapped cantilever based accelerometer is also explored for low frequency vibration sensing applications such as ballistocardiogram monitoring. Finally, to address the power issues of wireless sensors such as wireless wearable health monitors, a wide band vibration energy harvester based on a folded gapped cantilever is developed and demonstrated on a ceiling air condition unit

A wearable heart monitor at the ear using ballistocardiogram (BCG) and electrocardiogram (ECG) with a nanowatt ECG heartbeat detection circuit

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (p. 132-137).This work presents a wearable heart monitor at the ear that uses the ballistocardiogram (BCG) and the electrocardiogram (ECG) to extract heart rate, stroke volume, and pre-ejection period (PEP) for the application of continuous heart monitoring. Being a natural anchoring point, the ear is demonstrated as a viable location for the integrated sensing of physiological signals. The source of periodic head movements is identified as a type of BCG, which is measured using an accelerometer. The head BCG's principal peaks (J-waves) are synchronized to heartbeats. Ensemble averaging is used to obtain consistent J-wave amplitudes, which are related to stroke volume. The ECG is sensed locally near the ear using a single-lead configuration. When the BCG and the ECG are used together, an electromechanical duration called the RJ interval can be obtained. Because both head BCG and ECG have low signal-to-noise ratios, cross-correlation is used to statistically extract the RJ interval. The ear-worn device is wirelessly connected to a computer for real time data recording. A clinical test involving hemodynamic maneuvers is performed on 13 subjects. The results demonstrate a linear relationship between the J-wave amplitude and stroke volume, and a linear relationship between the RJ interval and PEP. While the clinical device uses commercial components, a custom integrated circuit for ECG heartbeat detection is designed with the goal of reducing power consumption and device size. With 58nW of power consumption, the ECG circuit replaces the traditional instrumentation amplifier, analog-to-digital converter, and signal processor with a single chip solution. The circuit demonstrates a topology that takes advantage of the ECG's characteristics to extract R-wave timings at the chest and the ear in the presence of baseline drift, muscle artifact, and signal clipping.by David Da He.Ph.D

24h seismocardiogram monitoring in ambulant subjects

Sternal seismocardiogram (SCG) is the assessment of microvibrations produced by the beating heart as detected by an accelerometer positioned on the sternum. This signal reflects mechanical events of the heart contraction, including the opening and closure of mitral and aortic valves and maximal blood flow acceleration. Traditionally, SCG has been detected in a laboratory setting with the subject lying at rest in supine position. Aims of this study were 1) to investigate the feasibility of a SCG monitoring over the 24 hours in ambulant subjects, and 2) to calculate number and time distribution of the SCG estimates obtainable over the 24 hours. In 5 healthy subjects ECG, respiration, body accelerations and sternal SCG were recorded for 24 hours in a workday by a smart garment recently developed in our laboratory, the MagIC-SCG system. Each recording was split into a series of contiguous 5-s data segments and SCG was estimated in each segment where the magnitude of the acceleration vector was <;4 milli-g (this condition indicates that the subject was not moving). All the 24-h recordings were found of good quality and could be entirely analyzed. A large number of SCG estimates could be obtained over the 24 hours. In particular, more than 100 estimates per hour were available during the day; at night this rate was three times higher. Thus our study indicates that not only the 24h SCG monitoring in daily life is feasible but also that possible changes over time in SCG and its derived parameters may be tracked with an extreme temporal detail

Evaluación no invasiva de la función muscular respiratoria mediante el análisis de la señal mecanomiográfica en pacientes con enfermedad pulmonar obstructiva crónica

El estudio y evaluación de la función muscular respiratoria en enfermedades respiratorias a través de técnicas no invasivas representa un tema de gran interés, dado que hasta la fecha no existen métodos satisfactorios aplicables en situaciones clínicas. En la enfermedad pulmonar obstructiva crónica (EPOC), el trabajo mecánico de los músculos respiratorios aumenta dando lugar a la fatiga, disminución de los movimientos de la caja torácica, y por tanto una disminución de la eficiencia muscular respiratoria. Es conocido que el músculo diafragma, principal responsable de la actividad mecánica respiratoria, al igual que otros músculos esqueléticos vibra lateralmente durante su contracción. De ahí, que estas vibraciones puedan ser registradas mediante micrófonos, sensores piezoeléctricos o acelerómetros posicionados encima de la pared inferior del pecho en la zona de aposición del diafragma con la caja torácica. El registro de estas vibraciones da lugar a la señal mecanomiográfica del diafragma (MMGdi). El principal objetivo de esta tesis ha sido el estudio y caracterización no invasiva de la función muscular respiratoria en pacientes con EPOC a través de la señal MMGdi registrada mediante acelerómetros posicionados entre el séptimo y octavo espacios intercostales, en la línea axilar izquierda y derecha del cuerpo durante la realización de los protocolos respiratorios de carga incremental progresiva y de flujo incremental progresivo. Para mejorar la estimación de la amplitud de la señal MMGdi se han propuesto tres nuevos índices, que tienen en cuenta la naturaleza aleatoria y el ruido asociado en las señales MMGdi, y están basados en: el algoritmo de Lempel-Ziv (LZM), la entropía aproximada (fApEn), y la entropía muestral (fSampEn). Todos ellos son calculados con intervalos de cuantificación fijos y empleando ventanas móviles. Los resultados obtenidos con éstos índices han permitido estimar con mayor fiabilidad y robustez la amplitud de las señales MMGdi, en relación a los métodos clásicos utilizados en el estudio de señales miográficas. El estudio del valor medio de los parámetros analizados ha mostrado, que existe una tendencia incremental de éste en los parámetros de amplitud, y una tendencia decreciente en los parámetros frecuenciales (frecuencias media y máxima), con el incremento de la carga y/o flujo. En este sentido, se ha observado que el valor medio es mayor cuanto mayor es la severidad del paciente con EPOC. Por otra parte, se ha observado que existe una fuerte correlación entre los parámetros de amplitud y la presión inspiratoria máxima en el protocolo de flujo incremental progresivo, con una tendencia decreciente con la severidad. Del mismo modo la eficiencia muscular respiratoria, evaluada como la relación entre la fuerza que producen los músculos respiratorios (la presión inspiratoria en boca) y lo que gastan o necesitan para producir esta presión (la vibración de los músculos respiratorios evaluada mediante las señales MMGdi), ha mostrado en general una tendencia decreciente con el aumento de la severidad. Finalmente, los resultados que se desprenden de esta tesis indican que el estudio de la señal MMGdi representa una herramienta útil con un gran potencial para evaluar el grado de la severidad presente en sujetos con EPOC y su relación con la debilidad de la musculatura respiratoria, y por tanto su aplicación en estudios clínicos podría ser de gran ayuda para evaluar el desarrollo de la EPOC.The study and evaluation of the respiratory muscles function in people who suffer from respiratory diseases can be evaluated through the use of noninvasive techniques. This is a topic of great interest considering there are currently no existing methods that can be successfully applied in clinical situations. In chronic obstructive pulmonary disease (COPD), the mechanical work of the respiratory muscles increases, which could lead to muscular fatigue, decreased movement of the ribcage, and, therefore, a decrease in the respiratory muscle efficiency. The diaphragm muscle is the principal muscle of inspiration and the main mechanical responsible for the ventilation. Similar to other skeletal muscles the diaphragm laterally vibrates during its contraction. These vibrations can be recorded by microphones, piezoelectric sensors or accelerometers, which are placed above the lower chest wall in the area of apposition of the diaphragm to the ribcage. The record of these vibrations is known as mechanomyographic signal of the diaphragm muscle (MMGdi). The main objective of this thesis has been the study and noninvasive characterization of the respiratory muscles function in patients with COPD. This characterization has been made possible through the use of MMGdi signals recorded by accelerometers placed between the seventh and eighth intercostals spaces on the left and right anterior axillary lines of the body during two respiratory protocols. The first protocol is called progressive incremental load protocol and the second one progressive incremental flow protocol. In this thesis three new indices have been proposed to improve the MMGdi amplitude estimation. These indices take into account the random nature and the associated noise in the MMGdi signals, and are based on the: Lempel-Ziv algorithm (MLZ), approximate entropy (fApEn), and sample entropy (fSampEn). All of them are calculated with fixed quantization intervals and using moving windows. The obtained results with these new indices have shown improved reliability and robustness in the MMGdi amplitude estimation in comparison with classic methods used to study myographic signals. The study of the mean value of the analyzed parameters has shown an increasing trend of the amplitude parameters and a decreasing trend of the frequency parameters (mean and maximum frequencies) with increasing load and/or flow. Furthermore, we found that there was a direct relationship between these mean values and the severity of COPD; hence, the greater the mean value, the greater the severity of COPD. Moreover, we have seen that there is a strong correlation between the amplitude parameters and the maximum inspiratory pressure in the progressive incremental flow protocol with a decreasing trend as the severity of the patients increases. Likewise, the respiratory muscle efficiency, evaluated as the ratio between the force produced by the respiratory muscles (mouth inspiratory pressure) and what they need to produce this pressure (the vibration of respiratory muscles assessed by MMGdi signals), has also shown a generally decreasing trend as the severity of patients increases. Finally, the results of this thesis suggest that the study of the MMGdi signal is a useful tool with great potential to assess the relationship between respiratory muscle weakness and the degree of severity in patients with COPD. Therefore, the application of this innovative tool in clinical studies may be helpful to assess the development of COPD