Ganong's review of medical physiology , Ganong's review of medical physiology , کتابخانه دیجیتال جندی شاپور اهواز

Ganong's review of medical physiology

金水暎 著, 「政策學原論」 (법지사, 1992)

Recent advances in wearable sensors and portable electronics for sleep monitoring.

Sleep quality prediction in caregivers using physiological signals.

Aging population is a worldwide trend, which has created a societal crisis as many countries face the challenges of supporting an aging population with increasing costs of healthcare and decreasing numbers of caregivers. Sleep related disorders are common diseases, especially among the elderly. In this paper, we propose a simple and affordable unobtrusive sensing environment including a high-sensitive accelerometer on a bed and passive infrared (PIR) motion sensors in every room, following the generic framework of Internet of Health Things (IoHT) for monitoring the elderly’s sleep-wake conditions, to assess their sleep quality. The environment is nonintrusive, comfortable and can be used for long-term sleep monitoring, detecting early symptoms of sleep related disorders, and responding to caregivers. We implement and pilot test the environment under different daily living situations related to sleep quality. We develop a feature extraction algorithm and applied five popular data analytics models to assess their relative performance. Our study shows that all classifiers except Naive Bayes can effectively detect sleep quality with the Area under ROC curve (AUC) performance higher than 90%. Among which multilayer feed-forward neural network achieved the best results, in which the detecting sensitivity is up to 96.61%, specificity is 91.81% and AUC performance is 94.21%.

IoT-Based Unobtrusive Sensing for Sleep Quality Monitoring and Assessment

We use self-report and electrodermal activity (EDA) wearable sensor data from 77 nights of sleep of six participants to test the efficacy of EDA data for sleep monitoring. We used factor analysis to find latent factors in the EDA data, and used causal model search to find the most probable graphical model accounting for self-reported sleep efficiency (SE), sleep quality (SQ), and the latent factors in the EDA data. Structural equation modeling was used to confirm fit of the extracted graph to the data. Based on the generated graph, logistic regression and naive Bayes models were used to test the efficacy of the EDA data in predicting SE and SQ. Six EDA features extracted from the total signal over a night’s sleep could be explained by two latent factors, EDA Magnitude and EDA Storms. EDA Magnitude performed as a strong predictor for SE to aid detection of substantial changes in time asleep. The performance of EDA Magnitude and SE in classifying SQ demonstrates promise for using a wearable sensor for sleep monitoring. However, our data suggest that obtaining a more accurate sensor-based measure of SE will be necessary before smaller changes in SQ can be detected from EDA sensor data alone.

Toward sensor-based sleep monitoring with electrodermal activity measures

This exploratory qualitative study examined the feasibility of utilizing gaming technology that will ultimately assess task performance and stress among caregivers of dementia patients. The long-term goal is to use this unobtrusive application (app) to detect caregiver burnout for the purposes of early intervention. This preliminary study examined participant interface with a specific gaming technology called CAST (Caregiver Assessment Using Serious Gaming Technology). Ten dementia caregivers participated. Participants attended a demonstration and then interacted with a preliminary version of the CAST tablet application. Social work researchers interviewed participants using open-ended questions to gauge interest, technology skill level, and comfort with the app. Participants expressed interest and identified potential ways to further develop the system in order to increase ease of use, decrease time commitment, and improve suitability for daily use. The provided feedback will be used to refine th...

A Preliminary Qualitative Analysis on the Feasibility of Using Gaming Technology in Caregiver Assessment

As pre-diagnostic technologies are becoming increasingly accessible, using them to improve the quality of care available to dementia patients and their caregivers is of increasing interest. Specifically, we aim to develop a tool for non-invasively assessing task performance in a simple gaming application. To address this, we have developed Caregiver Assessment using Smart Technology (CAST), a mobile application that personalizes a traditional word scramble game. Its core functionality uses a Fuzzy Inference System (FIS) optimized via a Genetic Algorithm (GA) to provide customized performance measures for each user of the system. With CAST, we match the relative level of difficulty of play using the individual’s ability to solve the word scramble tasks. We provide an analysis of the preliminary results for determining task difficulty, with respect to our current participant cohort.

Caregiver Assessment Using Smart Gaming Technology: A Feasibility Study

We use self-report and electrodermal activity (EDA) wearable sensor data from 77 nights of sleep on six participants to test the efficacy of EDA data for sleep monitoring. We used factor analysis to find latent factors in the EDA data, and causal model search to find the most probable graphical model accounting for self-reported sleep efficiency (SE), sleep quality (SQ), and the latent EDA factors. Structural equation modeling was used to confirm fit of the extracted graph. Based on the generated graph, logistic regression and naive Bayes models were used to test the efficacy of the EDA data in predicting SE and SQ. Six EDA features extracted from the total signal over a night's sleep could be explained by two latent factors, EDA Magnitude and EDA Storms. EDA Magnitude performed as a strong predictor for SE to aid detection of substantial changes in time asleep. The performance of EDA Magnitured and SE in classifying SQ showed promise for wearable sleep monitoring applications. However, our data suggest that obtaining a more accurate sensor-based measure of SE will be necessary before smaller changes in SQ can be detected from EDA sensor data alone.

Toward Sensor-based Sleep Monitoring with Electrodermal Activity Measures

Cardiovascular health is among one of the most important areas of the medical field today. Approximately 2,303 Americans die from cardiovascular disease (CVD) daily. By 2035, approximately 130 million Americans will be affected by CVD leading to an estimated $1.1 trillion in medical cost. The medical industry is steadily moving toward individual medicine. To provide assistance in the cardiovascular field, this paper presents a methodology for creating a real time near 3D model of the heart while accounting for the thoracic cage obstruction. That is, creating a partial 3D heart consisting of the apical and parasternal views that are viewable in real time on a monitor. We discuss a wearable vest consisting of eight ultrasound transducers in a 4x2 arrangement covering the parasternal and apical views of the human heart. The calculations for the overlap of each view and between both views are provided. Also, generalized formulas for the sequential activation of multiple ultrasound transducers with no overlap are given. The presented methodology is capable of real time near 3D modeling without constant attention from a medical professional where the final goal will be abnormality detection.

Garrett Goodman

Papers

Toward sensor-based sleep monitoring with electrodermal activity measures

A Preliminary Qualitative Analysis on the Feasibility of Using Gaming Technology in Caregiver Assessment

Caregiver Assessment Using Smart Gaming Technology: A Feasibility Study

Toward Sensor-based Sleep Monitoring with Electrodermal Activity Measures

A Wearable Ultrasound Methodology for Creating a Real Time Near 3D Model of the Heart