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BackgroundPoorly managed pain can lead to substance use disorders, depression, suicide, worsening health, and increased use of health services. Most pain assessments occur in clinical settings away from patients’ natural environments. Advances in smart home technology may allow observation of pain in the home setting. Smart homes recognizing human behaviors may be useful for quantifying functional pain interference, thereby creating new ways of assessing pain and supporting people living with pain. ObjectiveThis study aimed to determine if a smart home can detect pain-related behaviors to perform automated assessment and support intervention for persons with chronic pain. MethodsA multiple methods, secondary data analysis was conducted using historic ambient sensor data and weekly nursing assessment data from 11 independent older adults reporting pain across 1-2 years of smart home monitoring. A qualitative approach was used to interpret sensor-based data of 27 unique pain events to support clinician-guided training of a machine learning model. A periodogram was used to calculate circadian rhythm strength, and a random forest containing 100 trees was employed to train a machine learning model to recognize pain-related behaviors. The model extracted 550 behavioral markers for each sensor-based data segment. These were treated as both a binary classification problem (event, control) and a regression problem. ResultsWe found 13 clinically relevant behaviors, revealing 6 pain-related behavioral qualitative themes. Quantitative results were classified using a clinician-guided random forest technique that yielded a classification accuracy of 0.70, sensitivity of 0.72, specificity of 0.69, area under the receiver operating characteristic curve of 0.756, and area under the precision-recall curve of 0.777 in comparison to using standard anomaly detection techniques without clinician guidance (0.16 accuracy achieved; P

BackgroundAs commercially available activity trackers are being utilized in clinical trials, the research community remains uncertain about reliability of the trackers, particularly in studies that involve walking aids and low-intensity activities. While these trackers have been tested for reliability during walking and running activities, there has been limited research on validating them during low-intensity activities and walking with assistive tools. ObjectiveThe aim of this study was to (1) determine the accuracy of 3 Fitbit devices (ie, Zip, One, and Flex) at different wearing positions (ie, pants pocket, chest, and wrist) during walking at 3 different speeds, 2.5, 5, and 8 km/h, performed by healthy adults on a treadmill; (2) determine the accuracy of the mentioned trackers worn at different sites during activities of daily living; and (3) examine whether intensity of physical activity (PA) impacts the choice of optimal wearing site of the tracker. MethodsWe recruited 15 healthy young adults to perform 6 PAs while wearing 3 Fitbit devices (ie, Zip, One, and Flex) on their chest, pants pocket, and wrist. The activities include walking at 2.5, 5, and 8 km/h, pushing a shopping cart, walking with aid of a walker, and eating while sitting. We compared the number of steps counted by each tracker with gold standard numbers. We performed multiple statistical analyses to compute descriptive statistics (ie, ANOVA test), intraclass correlation coefficient (ICC), mean absolute error rate, and correlation by comparing the tracker-recorded data with that of the gold standard. ResultsAll the 3 trackers demonstrated good-to-excellent (ICC>0.75) correlation with the gold standard step counts during treadmill experiments. The correlation was poor (ICC

BackgroundMedication nonadherence is a critical problem with severe implications in individuals at risk for atherosclerotic cardiovascular disease. Many studies have attempted to predict medication adherence in this population, but few, if any, have been effective in prediction, sug-gesting that essential risk factors remain unidentified.ObjectiveThis study's objective was to (1) establish an accurate prediction model of medi-cation adherence in individuals at risk for atherosclerotic cardiovascular disease and (2) identify significant contributing factors to the predictive accuracy of medication adherence. In particular, we aimed to use only the baseline questionnaire data to assess medication adherence prediction feasibility.MethodsA sample of 40 individuals at risk for atherosclerotic cardiovascular disease was recruited for an eight-week feasibility study. After collecting baseline data, we recorded data from a pillbox that sent events to a cloud-based server. Health measures and medication use events were analyzed using machine learning algorithms to identify variables that best predict medication adherence.ResultsOur adherence prediction model, based on only the ten most relevant variables, achieved an average error rate of 12.9%. Medication adherence was closely correlated with being encouraged to play an active role in their treatment, having confidence about what to do in an emergency, knowledge about their medications, and having a special person in their life.ConclusionsOur results showed the significance of clinical and psychosocial factors for predicting medication adherence in people at risk for atherosclerotic cardiovascular diseases. Clini-cians and researchers can use these factors to stratify individuals to make evidence-based decisions to reduce the risks.

In the context of an aging population, tools to help elderly to live independently must be developed. The goal of this paper is to evaluate the possibility of using unobtrusively collected activity-aware smart home behavioral data to automatically detect one of the most common consequences of aging: functional health decline. After gathering the longitudinal smart home data of 29 older adults for an average of >2 years, we automatically labeled the data with corresponding activity classes and extracted time-series statistics containing 10 behavioral features. Using this data, we created regression models to predict absolute and standardized functional health scores, as well as classification models to detect reliable absolute change and positive and negative fluctuations in everyday functioning. Functional health was assessed every six months by means of the Instrumental Activities of Daily Living-Compensation (IADL-C) scale. Results show that total IADL-C score and subscores can be predicted by means of activity-aware smart home data, as well as a reliable change in these scores. Positive and negative fluctuations in everyday functioning are harder to detect using in-home behavioral data, yet changes in social skills have shown to be predictable. Future work must focus on improving the sensitivity of the presented models and performing an in-depth feature selection to improve overall accuracy.

Generative adversarial networks have become a de facto approach to generate synthetic data points that resemble their real counterparts. We tackle the situation where the realism of individual samples is not the sole criterion for synthetic data generation. Additional constraints such as privacy preservation, distribution realism, and diversity promotion may also be essential to optimize. To address this challenge, we introduce HydraGAN, a multi-agent network that performs multi-objective synthetic data generation. We theoretically verify that training the HydraGAN system, containing a single generator and an arbitrary number of discriminators, leads to a Nash equilibrium. Experimental results for six datasets indicate that HydraGAN consistently outperforms prior methods in maximizing the Area under the Radar Chart, balancing a combination of cooperative or competitive data generation goals. [ABSTRACT FROM AUTHOR]

Background: This study examined associations of device‐measured physical activity and sedentary time with quality of life (QOL) and fatigue in newly diagnosed breast cancer patients in the Alberta Moving Beyond Breast Cancer (AMBER) cohort study. Methods: After diagnosis, 1409 participants completed the SF‐36 version 2 and the Fatigue Scale, wore an ActiGraph device on their right hip to measure physical activity, and an activPAL device on their thigh to measure sedentary time (sitting/lying) and steps. ActiGraph data was analyzed using a hybrid machine learning method (R Sojourn package, Soj3x) and activPAL data were analyzed using activPAL algorithms (PAL Software version 8). Quantile regression was used to examine cross‐sectional associations of QOL and fatigue with steps, physical activity, and sedentary hours at the 25th, 50th, and 75th percentiles of the QOL and fatigue distributions. Results: Total daily moderate and vigorous physical activity (MVPA) hours was positively associated with better physical QOL at the 25th (β = 2.14, p = <.001), 50th (β = 1.98, p = <.001), and 75th percentiles (β = 1.25, p =.003); better mental QOL at the 25th (β = 1.73, p =.05) and 50th percentiles (β = 1.07, p =.03); and less fatigue at the 25th (β = 4.44, p <.001), 50th (β = 3.08, p = <.001), and 75th percentiles (β = 1.51, p = <.001). Similar patterns of associations were observed for daily steps. Total sedentary hours was associated with worse fatigue at the 25th (β = −0.58, p =.05), 50th (β = −0.39, p =.06), and 75th percentiles (β = −0.24, p =.02). Sedentary hours were not associated with physical or mental QOL. Conclusions: MVPA and steps were associated with better physical and mental QOL and less fatigue in newly diagnosed breast cancer patients. Higher sedentary time was associated with greater fatigue symptoms. Consistent and significant associations emerged between device‐measured physical activity, quality of life, and fatigue outcomes in this large cohort of newly diagnosed breast cancer patients. These results may be used to inform clinical practice and policies about incorporating physical activity and reducing sedentary time as adjuvant therapy for newly diagnosed breast cancer patients starting treatment. [ABSTRACT FROM AUTHOR]

The goal of this project is to develop a unique simulation environment that can be used to increase students' interest and expertise in Computer Science curriculum. Hands-on experience with physical or simulated equipment is an essential ingredient for learning, but many approaches to training develop a separate piece of equipment or software for each topic area. We describe the development of a simulation environment that provides a foundation for cross-disciplinary exercises. The Wireless Intelligent agent Simulation Environment (WISE), which combines activities from virtual and physical versions of the Wumpus World game, provides a dynamic learning environment that can enhance a number of Computer Science courses. In this paper, we describe the WISE environment design. We also describe steps for integrating WISE into Computer Science curriculum. As a demonstration of the effectiveness of the tool, we describe its use in the artificial intelligence, multimedia, and wireless networks courses at the University of Texas at Arlington.

Describes the development of a Web-based multimedia delivery method of increasing students' interest in artificial intelligence (AI). The course material features an integrated simulation environment that allows students to develop and test AI algorithms in a dynamic and uncertain visual environment. Evaluated the effect of the simulation on the interest level and confidence in class material of students over 3 years. (SLD)

Discusses structural components of databases that require data-mining algorithms and describes the SUBDUE system that is designed to discover knowledge in structural databases. Analyses the ability of SUBDUE to scale to large databases and describes parallel and distributed implementations to investigate methods for improving the scalability of scientific-discovery systems. (LRW)

Abstract Background Analyzing global experimental data can be tedious and time-consuming. Thus, helping biologists see results as quickly and easily as possible can facilitate biological research, and is the purpose of the software we describe. Results We present BirdsEyeView, a software system for visualizing experimental transcriptomic data using different views that users can switch among and compare. BirdsEyeView graphically maps data to three views: Cellular Map (currently a plant cell), Pathway Tree with dynamic mapping, and Gene Ontology http://www.geneontology.org Biological Processes and Molecular Functions. By displaying color-coded values for transcript levels across different views, BirdsEyeView can assist users in developing hypotheses about their experiment results. Conclusions BirdsEyeView is a software system available as a Java Webstart package for visualizing transcriptomic data in the context of different biological views to assist biologists in investigating experimental results. BirdsEyeView can be obtained from http://metnetdb.org/MetNet_BirdsEyeView.htm.

The ability to transfer knowledge learned in one environment in order to improve performance in a different environment is one of the hallmarks of human intelligence. Insights into human transfer learning help us to design computer-based agents that can better adapt to new environments without the need for substantial reprogramming. In this paper, we study the transfer of knowledge by humans playing various scenarios in a graphically realistic urban setting that are specifically designed to test various levels of transfer. We determine the amount and type of transfer that is being performed based on the performance of trained and untrained human players. In addition, we use a graph-based relational learning algorithm to extract patterns from player graphs. These analyses reveal that indeed humans are transferring knowledge from one set of games to another and the amount and type of transfer varies according to player experience and scenario complexity. The results of this analysis help us understand the nature of human transfer in such environments and shed light on how we might endow computer-based agents with similar capabilities. The game simulator and human data collection also represent a significant testbed in which other AI capabilities can be tested and compared to human performance. Index Terms--Data mining, graph algorithms, transfer learning, games.