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Older adults with mild cognitive impairment (MCI) often have difficulty performing complex instrumental activities of daily living (IADLs), which are critical to independent living. In this study, amnestic multi-domain MCI (N = 29), amnestic single-domain MCI (N = 18), and healthy older participants (N = 47) completed eight scripted IADLs (e.g., cook oatmeal on the stove) in a smart apartment testbed. We developed and experimented with a graded hierarchy of technology-based prompts to investigate both the amount of prompting and type of prompts required to assist individuals with MCI in completing the activities. When task errors occurred, progressive levels of assistance were provided, starting with the lowest level needed to adjust performance. Results showed that the multi-domain MCI group made more errors and required more prompts than the single-domain MCI and healthy older adult groups. Similar to the other two groups, the multi-domain MCI group responded well to the indirect prompts and did not need a higher level of prompting to get back on track successfully with the tasks. Need for prompting assistance was best predicted by verbal memory abilities in multi-domain amnestic MCI. Participants across groups indicated that they perceived the prompting technology to be very helpful.

Background: The goal of this work is to develop intelligent systems to monitor the wellbeing of individuals in their home environments., Objective: This paper introduces a machine learning-based method to automatically predict activity quality in smart homes and automatically assess cognitive health based on activity quality., Methods: This paper describes an automated framework to extract set of features from smart home sensors data that reflects the activity performance or ability of an individual to complete an activity which can be input to machine learning algorithms. Output from learning algorithms including principal component analysis, support vector machine, and logistic regression algorithms are used to quantify activity quality for a complex set of smart home activities and predict cognitive health of participants., Results: Smart home activity data was gathered from volunteer participants (n=263) who performed a complex set of activities in our smart home testbed. We compare our automated activity quality prediction and cognitive health prediction with direct observation scores and health assessment obtained from neuropsychologists. With all samples included, we obtained statistically significant correlation (r=0.54) between direct observation scores and predicted activity quality. Similarly, using a support vector machine classifier, we obtained reasonable classification accuracy (area under the ROC curve=0.80, g-mean=0.73) in classifying participants into two different cognitive classes, dementia and cognitive healthy., Conclusions: The results suggest that it is possible to automatically quantify the task quality of smart home activities and perform limited assessment of the cognitive health of individual if smart home activities are properly chosen and learning algorithms are appropriately trained.

Performing daily activities without assistance is important to maintaining an independent functional lifestyle. As a result, automated activity prompting systems can potentially extend the period of time that adults can age in place. In this paper we introduce AP, an algorithm to automate activity prompting based on smart home technology. AP learns prompt rules based on the time when activities are typically performed as well as the relationship between activities that normally occur in a sequence. We evaluate the AP algorithm based on smart home datasets and demonstrate its ability to operate within a physical smart environment.

Many intelligent systems that focus on the needs of a human require information about the activities that are being performed by the human. At the core of this capability is activity recognition. Activity recognition techniques have become robust but rarely scale to handle more than a few activities. They also rarely learn from more than one smart home data set because of inherent differences between labeling techniques. In this paper we investigate a data-driven approach to creating an activity taxonomy from sensor data found in disparate smart home datasets. We investigate how the resulting taxonomy can help analyze the relationship between classes of activities. We also analyze how the taxonomy can be used to scale activity recognition to a large number of activity classes and training datasets. We describe our approach and evaluate it on 34 smart home datasets. The results of the evaluation indicate that the hierarchical modeling can reduce training time while maintaining accuracy of the learned model.

The growth in popularity of smart environments has been quite steep in the last decade and so has the demand for smart health assistance systems. A smart home-based prompting system can enhance these technologies to deliver in-home interventions to users for timely reminders or brief instructions describing the way a task should be done for successful completion. This technology is in high demand given the desire of people who have physical or cognitive limitations to live independently in their homes. In this paper, with the introduction of the "PUCK" prompting system, we take an approach to automate prompting-based interventions without any predefined rule sets or user feedback. Unlike other approaches, we use simple off-the-shelf sensors and learn the timing for prompts based on real data that is collected with volunteer participants in our smart home test bed. The data mining approaches taken to solve this problem come with the challenge of an imbalanced class distribution that occurs naturally in the data. We propose a variant of an existing sampling technique, SMOTE, to deal with the class imbalance problem. To validate the approach, a comparative analysis with Cost Sensitive Learning is performed.

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.

Older adults with cognitive impairments often have difficulty performing instrumental activities of daily living (IADLs). Prompting technologies have gained popularity over the last decade and have the potential to assist these individuals with IADLs in order to live independently. Although prompting techniques are routinely used by caregivers and health care providers to aid individuals with cognitive impairment in maintaining their independence with everyday activities, there is no clear consensus or gold standard regarding prompt content, method of instruction, timing of delivery, or interface of prompt delivery in the gerontology or technology literatures. In this paper, we demonstrate how cognitive rehabilitation principles can inform and advance the development of more effective assistive prompting technologies that could be employed in smart environments. We first describe cognitive rehabilitation theory (CRT) and show how it provides a useful theoretical foundation for guiding the development of assistive technologies for IADL completion. We then use the CRT framework to critically review existing smart prompting technologies to answer questions that will be integral to advancing development of effective smart prompting technologies. Finally, we raise questions for future exploration as well as challenges and suggestions for future directions in this area of research.

Neuropsychologists are often asked to answer questions about the effects of cognitive deficits on everyday functioning. This study examined the relationship between and the cognitive correlates of self-report, performance-based, and direct observation measures commonly used as proxy measures for everyday functioning. Participants were 88 community-dwelling, cognitively healthy older adults (age 50-86 years). Participants completed standardized neuropsychological tests and questionnaires, and performed eight activities of daily living (e.g., water plants, fill a medication dispenser) while under direct observation in a campus apartment. All proxy measures of everyday function were sensitive to the effects of healthy cognitive aging. After controlling for age, cognitive predictors explained a unique amount of the variance for only the performance-based behavioral simulation measure (i.e., Revised Observed Tasks of Daily Living). The self-report instrumental activities of daily living (IADL) and the performance-based everyday problem-solving test (i.e., EPT) did not correlate with each other; however, both were unique predictors of the direct observation measure. These findings suggest that neuropsychologists must be cautious in making predictions about the quality of everyday activity completion in cognitively healthy older adults from specific cognitive functions. The findings further suggest that a self-report of IADLs and the performance-based EPT may be useful measures for assessing everyday functional status in cognitively healthy older adults.

The data mining and pervasive sensing technologies found in smart homes offer unprecedented opportunities for providing health monitoring and assistance to individuals experiencing difficulties living independently at home. In order to monitor the functional health of smart home residents, we need to design technologies that recognize and track activities that people normally perform as part of their daily routines. One question that frequently arises, however, is how many smart home sensors are needed and where should they be placed in order to accurately recognize activities? We employ data mining techniques to look at the problem of sensor selection for activity recognition in smart homes. We analyze the results based on six data sets collected in five distinct smart home environments.

The machine learning and pervasive sensing technologies found in smart homes offer unprecedented opportunities for providing health monitoring and assistance to individuals experiencing difficulties living independently at home. In order to monitor the functional health of smart home residents, we need to design technologies that recognize and track activities that people normally perform as part of their daily routines. Although approaches do exist for recognizing activities, the approaches are applied to activities that have been pre-selected and for which labeled training data is available. In contrast, we introduce an automated approach to activity tracking that identifies frequent activities that naturally occur in an individual's routine. With this capability we can then track the occurrence of regular activities to monitor functional health and to detect changes in an individual's patterns and lifestyle. In this paper we describe our activity mining and tracking approach and validate our algorithms on data collected in physical smart environments.

The data mining and pervasive computing technologies found in smart homes offer unprecedented opportunities for providing context-aware services, including health monitoring and assistance to individuals experiencing difficulties living independently at home. In order to provide these services, smart environment algorithms need to recognize and track activities that people normally perform as part of their daily routines. However, activity recognition has typically involved gathering and labeling large amounts of data in each setting to learn a model for activities in that setting. We hypothesize that generalized models can be learned for common activities that span multiple environment settings and resident types. We describe our approach to learning these models and demonstrate the approach using eleven CASAS datasets collected in seven environments.

The pervasive sensing technologies found in smart homes offer unprecedented opportunities for providing health monitoring and assistance to individuals experiencing difficulties living independently at home. A primary challenge that needs to be tackled to meet this need is the ability to recognize and track functional activities that people perform in their own homes and everyday settings. In this paper, we look at approaches to perform real-time recognition of Activities of Daily Living. We enhance other related research efforts to develop approaches that are effective when activities are interrupted and interleaved. To evaluate the accuracy of our recognition algorithms we assess them using real data collected from participants performing activities in our on-campus smart apartment testbed.

The pervasive sensing technologies found in smart environments offer unprecedented opportunities for monitoring and assisting the individuals who live and work in these spaces. An aspect of daily life that is important for one's emotional and physical health is social interaction. In this paper we investigate the use of smart environment technologies to detect and analyze interactions in smart spaces. We introduce techniques for collect and analyzing sensor information in smart environments to help in interpreting resident behavior patterns and determining when multiple residents are interacting. The effectiveness of our techniques is evaluated using two physical smart environment testbeds.

Text and structural data mining of web and social media (WSM) provides a novel disease surveillance resource and can identify online communities for targeted public health communications (PHC) to assure wide dissemination of pertinent information. WSM that mention influenza are harvested over a 24-week period, 5 October 2008 to 21 March 2009. Link analysis reveals communities for targeted PHC. Text mining is shown to identify trends in flu posts that correlate to real-world influenza-like illness patient report data. We also bring to bear a graph-based data mining technique to detect anomalies among flu blogs connected by publisher type, links, and user-tags.

Analysis of Google influenza-like-illness (ILI) search queries has shown a strongly correlated pattern with Centers for Disease Control (CDC) and Prevention seasonal ILI reporting data. Web and social media provide another resource to detect increases in ILI. This paper evaluates trends in blog posts that discuss influenza. Our key finding is that from 5th October 2008 to 31st January 2009, a high correlation exists between the frequency of posts, containing influenza keywords, per week and CDC influenza-like-illness surveillance data.

The pervasive sensing technologies found in smart environments offer unprecedented opportunities for monitoring and assisting the individuals who live and work in these spaces. As aspect of daily life that is often overlooked in maintaining a healthy lifestyle is the air quality of the environment. In this paper we investigate the use of machine learning technologies to predict CO(2) levels as an indicator of air quality in smart environments. We introduce techniques for collecting and analyzing sensor information in smart environments and analyze the correlation between resident activities and air quality levels. The effectiveness of our techniques is evaluated using three physical smart environment testbeds.

Background: Researchers and medical practitioners have long sought the ability to continuously and automatically monitor patients beyond the confines of a doctor's office. We describe a smart home monitoring and analysis platform that facilitates the automatic gathering of rich databases of behavioral information in a manner that is transparent to the patient. Collected information will be automatically or manually analyzed and reported to the caregivers and may be interpreted for behavioral modification in the patient., Method: Our health platform consists of five technology layers. The architecture is designed to be flexible, extensible, and transparent, to support plug-and-play operation of new devices and components, and to provide remote monitoring and programming opportunities., Results: The smart home-based health platform technologies have been tested in two physical smart environments. Data that are collected in these implemented physical layers are processed and analyzed by our activity recognition and chewing classification algorithms. All of these components have yielded accurate analyses for subjects in the smart environment test beds., Conclusions: This work represents an important first step in the field of smart environment-based health monitoring and assistance. The architecture can be used to monitor the activity, diet, and exercise compliance of diabetes patients and evaluate the effects of alternative medicine and behavior regimens. We believe these technologies are essential for providing accessible, low-cost health assistance in an individual's own home and for providing the best possible quality of life for individuals with diabetes., (© Diabetes Technology Society)

Ambient intelligence has a history of focusing on technologies that are integrated into a person's environment. However, ambient intelligence can be found on a person's body as well. In this thematic issue we examine the role of wearable computing in the field of ambient intelligence. In this article we provide an overview of the field of wearable computing and discuss its relationship to the fields of smart environments and ambient intelligence. In addition, we introduce the papers presented in the thematic issue highlighting a number of research projects which are defining the state of the art in wearable computing and ambient intelligence.

Objectives: Pervasive computing technology can provide valuable health monitoring and assistance technology to help individuals live independent lives in their own homes. As a critical part of this technology, our objective is to design software algorithms that recognize and assess the consistency of activities of daily living that individuals perform in their own homes., Methods: We have designed algorithms that automatically learn Markov models for each class of activity. These models are used to recognize activities that are performed in a smart home and to identify errors and inconsistencies in the performed activity., Results: We validate our approach using data collected from 60 volunteers who performed a series of activities in our smart apartment testbed. The results indicate that the algorithms correctly label the activities and successfully assess the completeness and consistency of the performed task., Conclusions: Our results indicate that activity recognition and assessment can be automated using machine learning algorithms and smart home technology. These algorithms will be useful for automating remote health monitoring and interventions.

The pervasive sensing technologies found in smart homes offer unprecedented opportunities for providing health monitoring and assistance to individuals experiencing difficulties living independently at home. A primary challenge that needs to be tackled to meet this need is the ability to recognize and track functional activities that people perform in their own homes and everyday settings. In this paper we look at approaches to perform real-time recognition of Activities of Daily Living. We enhance other related research efforts to develop approaches that are effective when activities are interrupted and interleaved. To evaluate the accuracy of our recognition algorithms we assess them using real data collected from participants performing activities in our on-campus smart apartment testbed.

As the use of coronary stents continues to expand worldwide, in-stent restenosis remains a major clinical problem for interventional cardiologists. However, recent clinical trials have shown that the drug-eluting stents are the most promising method for reducing the restenosis and allowing the injured vessel to heal. Further development work is focused on investigating the use of this technology with a range of drugs for the inhibition of restenosis.

Fifty-nine chronic pain patients satisfying one of four previously identified pain group classifications were evaluated using the NEO Personality Inventory (NEO-PI), a standardized measure of normal adult personality structure. Minnesota Multiphasic Personality Inventory (MMPI) pain subgroups differed with respect to level of NEO-PI Neuroticism. In particular, emotionally overwhelmed pain patients as defined by multiple MMPI scale elevations had higher NEO-PI Neuroticism scores. Post hoc analyses revealed higher levels of depression, anxiety, vulnerability, and hostility in emotionally overwhelmed subjects. None of the remaining groups differed from each other on NEO-PI Neuroticism. Additionally, none of the other NEO-PI domains discriminated pain subgroups. NEO-PI profiles for pain patients (except for Neuroticism in emotionally overwhelmed patients) yielded t scores in the average range, suggesting that chronic pain patients present with a relatively normal underlying personality structure.