A deep learning approach for pressure ulcer prevention using wearable computing

Abstract In recent years, statistics have confirmed that the number of elderly people is increasing. Aging always has a strong impact on the health of a human being; from a biological of point view, this process usually leads to several types of diseases mainly due to the impairment of the organism. In such a context, healthcare plays an important role in the healing process, trying to address these problems. One of the consequences of aging is the formation of pressure ulcers (PUs), which have a negative impact on the life quality of patients in the hospital, not only from a healthiness perspective but also psychologically. In this sense, e-health proposes several approaches to deal with this problem, however, these are not always very accurate and capable to prevent issues of this kind efficiently. Moreover, the proposed solutions are usually expensive and invasive. In this paper we were able to collect data coming from inertial sensors with the aim, in line with the Human-centric Computing (HC) paradigm, to design and implement a non-invasive system of wearable sensors for the prevention of PUs through deep learning techniques. In particular, using inertial sensors we are able to estimate the positions of the patients, and send an alert signal when he/she remains in the same position for too long a period of time. To train our system we built a dataset by monitoring the positions of a set of patients during their period of hospitalization, and we show here the results, demonstrating the feasibility of this technique and the level of accuracy we were able to reach, comparing our model with other popular machine learning approaches

Development of a bed-based nighttime monitoring toolset

Doctor of PhilosophyDepartment of Electrical and Computer EngineeringSteven WarrenA movement is occurring within the healthcare field towards evidence-based or preventative care-based medicine, which requires personalized monitoring solutions. For medical technologies to fit within this framework, they need to adapt. Reduced cost of operation, ease-of-use, durability, and acceptance will be critical design considerations that will determine their success. Wearable technologies have shown the capability to monitor physiological signals at a reduced cost, but they require consistent effort from the user. Innovative unobtrusive and autonomous monitoring technologies will be needed to make personalized healthcare a reality. Ballistocardiography, a nearly forgotten field, has reemerged as a promising alternative for unobtrusive physiological monitoring. Heart rate, heart rate variability, respiration rate, movement, and additional hemodynamic features can be estimated from the ballistocardiogram (BCG). This dissertation presents a bed-based nighttime monitoring toolset designed to monitor BCG, respiration, and movement data motivated by the need to quantify the sleep of children with severe disabilities and autism – a capability currently unmet by commercial systems. A review of ballistocardiography instrumentation techniques (Chapter 2) is presented to 1) build an understanding of how the forces generated by the heart are coupled to the measurement apparatus and 2) provide a background of the field. The choice of sensing modalities and acquisition hardware and software for developing the unobtrusive bed-based nighttime monitoring platform is outlined in Chapters 3 and 4. Preliminary results illustrating the system’s ability to track physiological signals are presented in Chapter 5. Analyses were conducted on overnight data acquired from three lower-functioning children with autism (Chapters 6 and 9) who reside at Heartspring, Wichita, KS, where results justified the platform’s multi-sensor architecture and demonstrated the system’s ability to track physiological signals from this sensitive population over many months. Further, this dissertation presents novel BCG signal processing techniques – a signal quality index (Chapter 7) and a preprocessing inverse filter (Chapter 8) that are applicable to any ballistocardiograph. The bed-based nighttime monitoring toolset outlined in this dissertation presents an unobtrusive, autonomous, robust physiological monitoring system that could be used in hospital-based or personalized, home-based medical applications that consist of short or long-term monitoring scenarios

Bio-Radar: sistema de aquisição de sinais vitais sem contacto

The Bio-Radar system is capable to measure vital signs accurately, namely the respiratory and cardiac signal, using electromagnetic waves. In this way, it is possible to monitor subjects remotely and comfortably for long periods of time. This system is based on the micro-Doppler effect, which relates the received signal phase variation with the distance change between the subject chest-wall and the radar antennas, which occurs due to the cardiopulmonary function. Considering the variety of applications where this system can be used, it is required to evaluate its performance when applied to real context scenarios and thus demonstrate the advantages that bioradar systems can bring to the general population. In this work, a bio-radar prototype was developed in order to verify the viability to be integrated in specific applications, using robust and low profile solutions that equally guarantee the general system performance while addressing the market needs. Considering these two perspectives to be improved, different level solutions were developed. On the hardware side, textile antennas were developed to be embedded in a car seat upholstery, thus reaching a low profile solution and easy to include in the industrialization process. Real context scenarios imply long-term monitoring periods, where involuntary body motion can occur producing high amplitude signals that overshadow the vital signs. Non-controlled monitoring environments might also produce time varying parasitic reflections that have a direct impact in the signal. Additionally, the subject's physical stature and posture during the monitoring period can have a different impact in the signals quality. Therefore, signal processing algorithms were developed to be robust to low quality signals and non-static scenarios. On the other hand, the bio-radar potential can also be maximized if the acquired signals are used pertinently to help identify the subject's psychophysiological state enabling one to act accordingly. The random body motion until now has been seen as a noisy source, however it can also provide useful information regarding subject's state. In this sense, the acquired vital signs as well as other body motions were used in machine learning algorithms with the goal to identify the subject's emotions and thus verify if the remotely acquired vital signs can also provide useful information.O sistema Bio-Radar permite medir sinais vitais com precisão, nomeadamente o sinal respiratório e cardíaco, utilizando ondas eletromagnéticas para esse fim. Desta forma, é possível monitorizar sujeitos de forma remota e confortável durante longos períodos de tempo. Este sistema é baseado no efeito de micro-Doppler, que relaciona a variação de fase do sinal recebido com a alteração da distância entre as antenas do radar e a caixa torácica do sujeito, que ocorre durante a função cardiopulmonar. Considerando a variedade de aplicações onde este sistema pode ser utilizado, é necessário avaliar o seu desempenho quando aplicado em contextos reais e assim demonstrar as vantagens que os sistemas bio-radar podem trazer à população geral. Neste trabalho, foi desenvolvido um protótipo do bio radar com o objetivo de verificar a viabilidade de integrar estes sistemas em aplicações específicas, utilizando soluções robustas e discretas que garantam igualmente o seu bom desempenho, indo simultaneamente de encontro às necessidades do mercado. Considerando estas duas perspetivas em que o sistema pode ser melhorado, foram desenvolvidas soluções de diferentes níveis. Do ponto de vista de hardware, foram desenvolvidas antenas têxteis para serem integradas no estofo de um banco automóvel, alcançando uma solução discreta e fácil de incluir num processo de industrialização. Contextos reais de aplicação implicam períodos de monitorização longos, onde podem ocorrer movimentos corporais involuntários que produzem sinais de elevada amplitude que se sobrepõem aos sinais vitais. Ambientes de monitorização não controlados podem produzir reflexões parasitas variantes no tempo que têm impacto direto no sinal. Adicionalmente, a estrutura física do sujeito e a sua postura durante o período de monitorização podem ter impactos diferentes na qualidade dos sinais. Desta forma, foram desenvolvidos algoritmos de processamento de sinal robustos a sinais de baixa qualidade e a cenários não estáticos. Por outro lado, o potencial do bio radar pode também ser maximizado se os sinais adquiridos forem pertinentemente utilizados de forma a ajudar a identificar o estado psicofisiológico do sujeito, permitindo mais tarde agir em conformidade. O movimento corporal aleatório que foi até agora visto como uma fonte de ruído, pode no entanto também fornecer informação útil sobre o estado do sujeito. Neste sentido, os sinais vitais e outros movimentos corporais adquiridos foram utilizados em algoritmos de aprendizagem automática com o objetivo de identificar as emoções do sujeito e assim verificar que sinais vitais adquiridos remotamente podem também conter informação útil.Programa Doutoral em Engenharia Eletrotécnic

NASA Tech Briefs Index, 1978

Approximately 601 announcements of new technology derived from the research and development activities of the National Aeronautics and Space Administration are presented. Emphasis is placed on information considered likely to be transferrable across industrial, regional, or disciplinary lines. Subject matter covered includes: electronic components and circuits; electron systems; physical sciences; materials; life sciences; mechanics; machinery; fabrication technology; and mathematics and information sciences

Contribución al diseño de sensores vestibles y ambientales para medir la respiración y el salto vertical en adultos mayores y frágiles.

Con el avance de la tecnología, se ha popularizado entre la población el uso de dispositivos para medir su estado de salud. Para lograr esto, se suelen utilizar dispositivos vestibles como los smartwatch y smartbands, dispositivos ambientales embebidos en los alrededores, e incluso dispositivos conectados a aplicaciones móviles. El uso de estas tecnologías también se ha popularizado entre los profesionales de la salud.Esta tesis se centra en el desarrollo de dispositivos para monitorizar la salud de adultos mayores y adultos frágiles. Se desarrollaron dos líneas de trabajo: en la primera se diseñó e implementó un sistema vestible para monitorizar en tiempo real la respiración de los usuarios; en la segunda se desarrolló un sistema ambiental capaz de medir la altura del salto vertical efectuado por los usuarios sobre él.Sistema vestible para monitorizar la respiración:- Dentro de esta línea de trabajo se investigó un nuevo sensor de respiración que venía a cubrir algunas lagunas existentes en el estado de la técnica: la integración de todos los elementos electrónicos del sistema en un encapsulado compacto, la liberación del diseño para su reutilización y mejora por parte de otros investigadores y el bajo coste de los elementos que componen el sistema, entre otros. El sistema vestible consiste en un dispositivo que se coloca alrededor del pecho mediante una cinta ajustable. Este sistema funciona mediante un sensor piezoresistivo que detecta las variaciones en el diámetro del pecho ocasionadas al inhalar y exhalar; las variaciones detectadas son enviadas de forma inalámbrica mediante Bluetooth a una estación de visualización elegida por el usuario (PC, Tablet o Smartphone). El sistema se encuentra embebido en un armazón impreso en 3D. Para validar el funcionamiento de este sistema, se realizaron pruebas con 21 voluntarios que efectuaron diferentes ritmos de respiración. Para obtener los ritmos respiratorios de cada señal generada, se utilizaron dos algoritmos. Estos algoritmos calculan el ritmo respiratorio al segmentar la señal original en ventanas de tiempo desde 6 hasta 30 segundos. Los resultados obtenidos muestran que, con una ventana de tiempo de 27 segundos, se obtiene el menor error para cada algoritmo (4,02% y 3,40 %).Sistema ambiental para medir el salto vertical:- Dentro de esta segunda línea de trabajo se investigó en un novedoso sistema ambiental para medir la altura del salto, lo que supuso una innovación respecto a los sensores utilizados actualmente para este fin. El sistema ambiental consiste en una plataforma que detecta objetos sobre ella mediante la presión, y mide el tiempo transcurrido desde que un objeto se retira y se coloca de nuevo. El sistema detecta los objetos mediante una matriz de sensores piezoresitivos (Force Sensitive Resistors - FSR realizados con velostat). Las dimensiones de la plataforma son 30 cm x 30 cm, área sobre la cual se distribuyen un total de 256 sensores FSR. El salto vertical se calcula mediante la fórmula de tiempo de vuelo, y el resultado es enviado mediante Bluetooth a un PC o Smartphone. Se realizaron dos experimentos: en el primero participaron un total de 38 voluntarios, con el objetivo de validar el funcionamiento del sistema con una cámara de alta velocidad como referencia (120 fps); en el segundo experimento se capturaron los datos en crudo de 15 voluntarios, con estos datos se emularon 10 frecuencias de muestreo (desde 20 Hz hasta 200 Hz) y se analizaron los efectos de utilizar frecuencias más bajas. Del primer experimento se obtuvo un error relativo medio de 1.98% con un coeficiente de determinación r2= 0,996. Del segundo experimento se determinó que las frecuencias de muestreo de 200 Hz y 100 Hz muestran un desempeño similar al mantener un error relativo por debajo del 5% en el 95% de las mediciones.Finalmente, este trabajo de tesis concluye indicando las principales aportaciones realizadas para cada una de las dos líneas de trabajo, así como el trabajo futuro que podría desarrollarse en cada una de ellas.<br /

Impaired night-time mobility in patients with Parkinson’s disease: a systematic review

Impaired bed mobility (IBM) is a symptom characteristic of patients having difficulty intentionally moving their bodies during nighttime sleep. IBM is one of the most common nocturnal symptoms of Parkinson’s disease (PD) and may lead to extreme pain and even death; it also increases the burden on the patients’ caregivers. In this systematic review, we included 19 studies involving a total of 1,407 patients with PD to observe the causes, assessment methods, and treatment options for IBM. We conclude that the extent of IBM is positively correlated with the severity of symptoms such as disease duration, dyskinesia and decreased sleep quality in patients with PD, and the evidence implies that IBM may be able to serve as a prodromal feature in the development of PD. IBM probably results from low nocturnal dopamine concentrations, reduced function of the spinal tract, torque problems in the muscles, and aging. Therefore, treatment is mostly based on continuously increasing the patient’s nocturnal dopamine concentration, while deep brain stimulation (DBS) also has a mitigating effect on IBM. Both scales and sensors are commonly used to measure the severity of IBM, the wearable device monitoring and scales being updated makes measurements easier and more accurate. The future of the advancement in this field lies in the use of more family-oriented devices (such as smart phones or watches and bracelets, etc.) to monitor IBM’s symptoms and select the appropriate therapeutic treatment according to the severity of the symptoms to relieve patients’ suffering

NASA Tech Briefs Index 1978

Tech Briefs are short announcements of new technology derived from the research and development activities of the National Aeronautics and Space Administration. These briefs emphasize information considered likely to be transferrable across industrial, regional, or disciplinary lines and are issued to encourage commercial application. This Index to NASA Tech Briefs contains abstracts and four indexes -- subject, personal author, originating Center, and Tech Brief number -- for 1978 Tech Briefs

The Impact of Digital Technologies on Public Health in Developed and Developing Countries

This open access book constitutes the refereed proceedings of the 18th International Conference on String Processing and Information Retrieval, ICOST 2020, held in Hammamet, Tunisia, in June 2020.* The 17 full papers and 23 short papers presented in this volume were carefully reviewed and selected from 49 submissions. They cover topics such as: IoT and AI solutions for e-health; biomedical and health informatics; behavior and activity monitoring; behavior and activity monitoring; and wellbeing technology. *This conference was held virtually due to the COVID-19 pandemic