Excitation wavelength dependence of the fluorescence lifetime of anisole.

Photo-physical models that describe the pressure- and temperature-dependent fluorescence quantum yield of organic fluorescence tracers rely on an accurate prediction of the initial excited-state population, collision-dependent relaxation processes, and state-dependent relaxation processes. In case the initial excited-state population distribution reached after the laser excitation equals on average the thermal distribution, the fluorescence quantum yield becomes pressure independent. This initial distribution critically depends on the temperature-dependent ground-state population before excitation as well as the excitation wavelength. The ability to predict this behavior is a critical check for the validity of the existing photophysical models. The dependence of the effective fluorescence lifetime of anisole on the excitation wavelength (256-270 nm) was investigated at temperatures between 325 and 525 K for pressures between 1 and 4 bar. For each temperature, a unique excitation wavelength was found where the fluorescence lifetime is pressure-independent. The comparison of the experimental results with the predictions based on the established photophysical step-ladder models revealed a systematic underestimation of the required excitation photon energies for direct excitation into the thermalized level. An improved modeling approach based on quantum chemistry calculations for implementing simulated excitation spectra and state-dependent transition probabilities overcomes these limitations. Our results show for the example of anisole that the fluorescence step-ladder models that exist for aromatic fluorescence tracers must be modified to correctly predict the effect of the excitation wavelength.

Automatic food detection in egocentric images using artificial intelligence technology.

OBJECTIVE: To develop an artificial intelligence (AI)-based algorithm which can automatically detect food items from images acquired by an egocentric wearable camera for dietary assessment. DESIGN: To study human diet and lifestyle, large sets of egocentric images were acquired using a wearable device, called eButton, from free-living individuals. Three thousand nine hundred images containing real-world activities, which formed eButton data set 1, were manually selected from thirty subjects. eButton data set 2 contained 29 515 images acquired from a research participant in a week-long unrestricted recording. They included both food- and non-food-related real-life activities, such as dining at both home and restaurants, cooking, shopping, gardening, housekeeping chores, taking classes, gym exercise, etc. All images in these data sets were classified as food/non-food images based on their tags generated by a convolutional neural network. RESULTS: A cross data-set test was conducted on eButton data set 1. The overall accuracy of food detection was 91·5 and 86·4 %, respectively, when one-half of data set 1 was used for training and the other half for testing. For eButton data set 2, 74·0 % sensitivity and 87·0 % specificity were obtained if both 'food' and 'drink' were considered as food images. Alternatively, if only 'food' items were considered, the sensitivity and specificity reached 85·0 and 85·8 %, respectively. CONCLUSIONS: The AI technology can automatically detect foods from low-quality, wearable camera-acquired real-world egocentric images with reasonable accuracy, reducing both the burden of data processing and privacy concerns.

An adaptive Hidden Markov model for activity recognition based on a wearable multi-sensor device.

Human activity recognition is important in the study of personal health, wellness and lifestyle. In order to acquire human activity information from the personal space, many wearable multi-sensor devices have been developed. In this paper, a novel technique for automatic activity recognition based on multi-sensor data is presented. In order to utilize these data efficiently and overcome the big data problem, an offline adaptive-Hidden Markov Model (HMM) is proposed. A sensor selection scheme is implemented based on an improved Viterbi algorithm. A new method is proposed that incorporates personal experience into the HMM model as a priori information. Experiments are conducted using a personal wearable computer eButton consisting of multiple sensors. Our comparative study with the standard HMM and other alternative methods in processing the eButton data have shown that our method is more robust and efficient, providing a useful tool to evaluate human activity and lifestyle.

Food/Non-Food Classification of Real-Life Egocentric Images in Low- and Middle-Income Countries Based on Image Tagging Features.

Malnutrition, including both undernutrition and obesity, is a significant problem in low- and middle-income countries (LMICs). In order to study malnutrition and develop effective intervention strategies, it is crucial to evaluate nutritional status in LMICs at the individual, household, and community levels. In a multinational research project supported by the Bill & Melinda Gates Foundation, we have been using a wearable technology to conduct objective dietary assessment in sub-Saharan Africa. Our assessment includes multiple diet-related activities in urban and rural families, including food sources (e.g., shopping, harvesting, and gathering), preservation/storage, preparation, cooking, and consumption (e.g., portion size and nutrition analysis). Our wearable device ("eButton" worn on the chest) acquires real-life images automatically during wake hours at preset time intervals. The recorded images, in amounts of tens of thousands per day, are post-processed to obtain the information of interest. Although we expect future Artificial Intelligence (AI) technology to extract the information automatically, at present we utilize AI to separate the acquired images into two binary classes: images with (Class 1) and without (Class 0) edible items. As a result, researchers need only to study Class-1 images, reducing their workload significantly. In this paper, we present a composite machine learning method to perform this classification, meeting the specific challenges of high complexity and diversity in the real-world LMIC data. Our method consists of a deep neural network (DNN) and a shallow learning network (SLN) connected by a novel probabilistic network interface layer. After presenting the details of our method, an image dataset acquired from Ghana is utilized to train and evaluate the machine learning system. Our comparative experiment indicates that the new composite method performs better than the conventional deep learning method assessed by integrated measures of sensitivity, specificity, and burden index, as indicated by the Receiver Operating Characteristic (ROC) curve.

Usability Testing of a Sensor-Controlled Digital Game to Engage Older Adults with Heart Failure in Physical Activity and Weight Monitoring.

BACKGROUND: Poor self-management of heart failure (HF) has contributed to poor health outcomes. Sensor-controlled digital games (SCDGs) integrates data from behavior-tracking sensors to trigger progress, rewards, content, and positive feedback in a digital game to motivate real-time behaviors. OBJECTIVES: To assess the usability of an SCDG prototype over a week of game-playing among 10 older adults with HF in their homes. METHODS: During initial play, participants' SCDG experiences were observed in their homes using a checklist based on the seven-item Serious Game User Evaluator (SeGUE) instrument. After a week of game-playing, participants completed a survey guided by the Intrinsic Motivation Inventory, to provide their perceptions of the SCDG's usability. Qualitative analysis via semistructured interview-derived themes on experiences playing the SCDG, perceptions regarding engaging with the SCDG, and any usability issues encountered. RESULTS: Ten HF participants (50% women and 50% White) played the SCDG for an average of 6 out of 7 days. Nine found the SCDG to be interesting, satisfying, and easy to play. The average step count over a week was 4,117 steps (range: 967-9,892). Average adherence with weight monitoring was 5.9 days in a week. Qualitative analysis yielded outcomes regarding attitudes toward SCDG, and barriers and facilitators that influenced participants' engagement with the SCDG. CONCLUSION: To the best of the authors' knowledge, this usability and feasibility study is the first to report an SCDG designed to improve HF self-management behaviors of older adults in their homes. Future research should consider several issues, such as user profiles, prior game-playing experiences, and network conditions most suitable for connected health interventions for older adults living in the community.

Personalizing Sensor-Controlled Digital Gaming to Self-Management Needs of Older Adults with Heart Failure: A Qualitative Study.

Objectives: In older persons with heart failure (HF), an inability to self-manage their disease condition can result in poor health outcomes and quality of life. With the rise in smartphone use and digital game playing among older adults, digital tools such as sensor-controlled digital games (SCDGs) can offer accessible health-promoting tools that are enjoyable and easy to use. However, designing SCDGs that are compelling and aligned with their life values and self-management needs can be challenging. This article describes a qualitative study with older adults with HF who were recruited from a cardiac rehabilitation laboratory in central Texas to identify their perceptions and expectations regarding a SCDG for HF self-management. Materials and Methods: A low-fidelity prototype that demonstrated the features of a SCDG was used to obtain the participants' perceptions about the value of SCDGs for HF self-management with respect to content, customization, flexibility, and usability through qualitative interviews. Results: We interviewed 15 patients with HF (53% women; age range, 53-90 years; 60% white). The concept of SCDGs for HF self-management was highly acceptable (80%). Participants provided suggestions for game characters, progress in the game, and game notifications and incentives. Perceived benefits included helping users track their behaviors and establish routines, become informed on strategies to manage HF, and empower themselves to take charge of their health. Conclusions: The study's findings will guide personalization of SCDG development to motivate patient engagement in HF self-management behaviors.

Role of Digital Games in Self-Management of Cardiovascular Diseases: A Scoping Review.

OBJECTIVE: Examine research on the use of digital games to improve self-management (SM) behaviors in patients diagnosed with cardiovascular diagnoses of hypertension, coronary artery disease, heart failure, or myocardial infarction. MATERIALS AND METHODS: For this scoping review, the CINAHL, PubMed, and Web of Science databases were searched for studies published from January 1, 2008 to December 20, 2017 using terms relevant to digital games and cardiovascular diseases (CVDs). RESULTS: Eight articles met the inclusion/exclusion criteria, seven of which presented studies with participants 50 years or older. Five of the eight studies assessed physical activity. Only two studies included a control group. Digital games significantly improved exercise capacity and energy expenditure but did not affect quality of life, self-efficacy, anxiety, or depression. Digital games were found enjoyable by 79%-93% of participants, including those with lower education or age; however, barriers to game use included being tired or bored, lack of interest in digital games, poor perception of fitness through games, sensor limitations, conflicts with daily life routine, and preferences for group exercise. Average adherence ranged from 70% to 100% over 2 weeks to 6 months of study duration, with higher adherence rates in studies that included human contact through supervision or social support. CONCLUSION: Paucity of studies about digital games for CVD SM behaviors precludes the need to undertake a full systematic review. Future studies examining digital games should include larger sample sizes, longer durations, game-design guided by behavioral change theoretical frameworks, and CVD SM behaviors in addition to physical activity behaviors.

Intermittent antegrade cardioplegia: isolated heart preservation with the Asporto heart preservation device.

BACKGROUND: A major problem in procurement of donor hearts is the limited time a donor heart remains viable. After cardiectomy, ischemic hypoxia is the main cause of donor heart degradation. The global myocardial ischemia causes a cascade of oxygen radical formation that cumulates in an elevation in hydrogen ions (decrease in pH), irreversible cellular injury, and potential microvascular changes in perfusion. OBJECTIVE: To determine the changes of prolonged storage times on donor heart microvasculature and the effects of intermittent antegrade perfusion. MATERIALS AND METHODS: Using porcine hearts flushed with a Ribosol-based cardioplegic solution, we examined how storage time affects microvascular myocardial perfusion by using contrast-enhanced magnetic resonance imaging at a mean (SD) of 6.1 (0.6) hours (n = 13) or 15.6 (0.6) hours (n = 11) after cardiectomy. Finally, to determine if administration of cardioplegic solution affects pH and microvascular perfusion, isolated hearts (group 1, n = 9) given a single antegrade dose, were compared with hearts (group 2, n = 8) given intermittent antegrade cardioplegia (150 mL, every 30 min, 150 mL/min) by a heart preservation device. Khuri pH probes in left and right ventricular tissue continuously measured hydrogen ion levels, and perfusion intensity on magnetic resonance images was plotted against time. RESULTS: Myocardial perfusion measured via magnetic resonance imaging at 6.1 hours was significantly greater than at 15.6 hours (67% vs 30%, P = .00008). In group 1 hearts, the mean (SD) for pH at the end of 6 hours decreased to 6.2 (0.2). In group 2, hearts that received intermittent antegrade cardioplegia, pH at the end of 6 hours was higher at 6.7 (0.3) (P = .0005). Magnetic resonance imaging showed no significant differences between the 2 groups in contrast enhancement (group 1, 62%; group 2, 40%) or in the wet/dry weight ratio. CONCLUSION: Intermittent perfusion maintains a significantly higher myocardial pH than does a conventional single antegrade dose. This difference may translate into an improved quality of donor hearts procured for transplantation, allowing longer distance procurement, tissue matching, improved outcomes for transplant recipients, and ideally a decrease in transplant-related costs.

Children's Cognitive and Affective Responses About a Narrative Versus a Non-Narrative Cartoon Designed for an Active Videogame.

OBJECTIVE: This article presents the results of interviews conducted with children regarding their cognitive and affective responses toward a narrative and a non-narrative cartoon. The findings will be used to further explore the role of a narrative in motivating continued active videogame play. MATERIALS AND METHODS: Twenty children (8-11 years old of mixed gender) watched two cartoons (narrative and non-narrative) and were subsequently interviewed. A thematic matrix was used to analyze the interviews. RESULTS: The narrative cartoon (n = 11) was only slightly preferred compared with the non-narrative one (n = 9), with little difference among the participants. The theme categories identified during the analyses were plot, characters, and suggestions. The fight scenes were mentioned by the children as a likeable aspect of the narrative cartoon. In the non-narrative cartoon, the vast majority (n = 17) liked the information about physical activity that was provided. The children enjoyed the appearance and personalities of the characters in both cartoons. A discrepancy in the data about the fight scenes (narrative cartoon) and characters (both cartoons) was found among the female participants (i.e., some girls did not like the fight and thought the characters were too aggressive). However, most of the children wanted to see more action in the story, an increase in the number of fight scenes (narrative cartoon), or more information about exercise and examples of exercises they could do (non-narrative cartoon). They also suggested adding a game to the non-narrative cartoon, including more characters, and improving the animation in both cartoons. CONCLUSIONS: The children preferred the narrative cartoon because of the story and the fight. Some gender differences were found, which further studies should investigate.

Qualitative Analysis of Cognitive Interviews With School Children: A Web-Based Food Intake Questionnaire.

BACKGROUND: The use of computers to administer dietary assessment questionnaires has shown potential, particularly due to the variety of interactive features that can attract and sustain children's attention. Cognitive interviews can help researchers to gain insights into how children understand and elaborate their response processes in this type of questionnaire. OBJECTIVE: To present the cognitive interview results of children who answered the WebCAAFE, a Web-based questionnaire, to obtain an in-depth understanding of children's response processes. METHODS: Cognitive interviews were conducted with children (using a pretested interview script). Analyses were carried out using thematic analysis within a grounded theory framework of inductive coding. RESULTS: A total of 40 children participated in the study, and 4 themes were identified: (1) the meaning of words, (2) understanding instructions, (3) ways to resolve possible problems, and (4) suggestions for improving the questionnaire. Most children understood questions that assessed nutritional intake over the past 24 hours, although the structure of the questionnaire designed to facilitate recall of dietary intake was not always fully understood. Younger children (7 and 8 years old) had more difficulty relating the food images to mixed dishes and foods eaten with bread (eg, jam, cheese). Children were able to provide suggestions for improving future versions of the questionnaire. CONCLUSIONS: More attention should be paid to children aged 8 years or below, as they had the greatest difficulty completing the WebCAAFE.

Saliency-aware food image segmentation for personal dietary assessment using a wearable computer.

Image-based dietary assessment has recently received much attention in the community of obesity research. In this assessment, foods in digital pictures are specified, and their portion sizes (volumes) are estimated. Although manual processing is currently the most utilized method, image processing holds much promise since it may eventually lead to automatic dietary assessment. In this paper we study the problem of segmenting food objects from images. This segmentation is difficult because of various food types, shapes and colors, different decorating patterns on food containers, and occlusions of food and non-food objects. We propose a novel method based on a saliency-aware active contour model (ACM) for automatic food segmentation from images acquired by a wearable camera. An integrated saliency estimation approach based on food location priors and visual attention features is designed to produce a salient map of possible food regions in the input image. Next, a geometric contour primitive is generated and fitted to the salient map by means of multi-resolution optimization with respect to a set of affine and elastic transformation parameters. The food regions are then extracted after contour fitting. Our experiments using 60 food images showed that the proposed method achieved significantly higher accuracy in food segmentation when compared to conventional segmentation methods.

An exploratory study on a chest-worn computer for evaluation of diet, physical activity and lifestyle.

Recently, wearable computers have become new members in the family of mobile electronic devices, adding new functions to those provided by smart-phones and tablets. As "always-on" miniature computers in the personal space, they will play increasing roles in the field of healthcare. In this work, we present our development of eButton, a wearable computer designed as a personalized, attractive, and convenient chest pin in a circular shape. It contains a powerful microprocessor, numerous electronic sensors, and wireless communication links. We describe its design concepts, electronic hardware, data processing algorithms, and its applications to the evaluation of diet, physical activity and lifestyle in the study of obesity and other chronic diseases.