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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]

Smart homes equipped with anonymous binary sensors offer a low-cost, unobtrusive solution that powers activity-aware applications, such as building automation, health monitoring, behavioral intervention, and home security. However, when multiple residents are living in a smart home, associating sensor events with the corresponding residents can pose a major challenge. Previous approaches to multi-resident tracking in smart homes rely on extra information, such as sensor layouts, floor plans, and annotated data, which may not be available or inconvenient to obtain in practice. To address those challenges in real-life deployment, we introduce the sMRT algorithm that simultaneously tracks the location of each resident and estimates the number of residents in the smart home, without relying on ground-truth annotated sensor data or other additional information. We evaluate the performance of our approach using two smart home datasets recorded in real-life settings and compare sMRT with two other methods that rely on sensor layout and ground truth-labeled sensor data. [ABSTRACT FROM AUTHOR]

Anomaly detection techniques can extract a wealth of information about unusual events. Unfortunately, these methods yield an abundance of findings that are not of interest, obscuring relevant anomalies. In this work, we improve upon traditional anomaly detection methods by introducing Isudra, an Indirectly Supervised Detector of Relevant Anomalies from time series data. Isudra employs Bayesian optimization to select time scales, features, base detector algorithms, and algorithm hyperparameters that increase true positive and decrease false positive detection. This optimization is driven by a small amount of example anomalies, driving an indirectly supervised approach to anomaly detection. Additionally, we enhance the approach by introducing a warm-start method that reduces optimization time between similar problems. We validate the feasibility of Isudra to detect clinically relevant behavior anomalies from over 2M sensor readings collected in five smart homes, reflecting 26 health events. Results indicate that indirectly supervised anomaly detection outperforms both supervised and unsupervised algorithms at detecting instances of health-related anomalies such as falls, nocturia, depression, and weakness. [ABSTRACT FROM AUTHOR]

Deep learning has produced state-of-the-art results for a variety of tasks. While such approaches for supervised learning have performed well, they assume that training and testing data are drawn from the same distribution, which may not always be the case. As a complement to this challenge, single-source unsupervised domain adaptation can handle situations where a network is trained on labeled data from a source domain and unlabeled data from a related but different target domain with the goal of performing well at test-time on the target domain. Many single-source and typically homogeneous unsupervised deep domain adaptation approaches have thus been developed, combining the powerful, hierarchical representations from deep learning with domain adaptation to reduce reliance on potentially costly target data labels. This survey will compare these approaches by examining alternative methods, the unique and common elements, results, and theoretical insights. We follow this with a look at application areas and open research directions. [ABSTRACT FROM AUTHOR]

The relationship between religious orientation and personality has been investigated using several different measures and diverse samples. As part of a larger study, 114 undergraduate students were given the Duke University Religion Index (DUREL) to include only those participants who were considered religious. The final sample of 80 participants were given the NEO Personality Inventory-Revised (NEO PI-R) and the New Indices of Religious Orientation (NIRO). Based on the results of the bivariate correlations, higher levels of intrinsic religious orientation were associated with greater Agreeableness and Conscientiousness. Multivariate analyses further revealed that Agreeableness was the most important personality variable when predicting intrinsic and quest religious orientations, with higher levels of Agreeableness predicting greater intrinsic and lower quest orientations. While lower levels of Neuroticism predicted greater intrinsic orientation, higher levels of Neuroticism predicted greater quest orientations. Higher Conscientiousness predicted greater intrinsic and lower quest orientations. Implications of these findings are discussed. [ABSTRACT FROM AUTHOR]

Time series data collected from sensors can be analyzed to monitor changes in physical activity as an individual makes a substantial lifestyle change, such as recovering from an injury or illness. In an inpatient rehabilitation setting, approaches to detect and explain changes in longitudinal physical activity data collected from wearable sensors can provide value as a monitoring, research, and motivating tool. We adapt and expand our Physical Activity Change Detection (PACD) approach to analyze changes in patient activity in such a setting. We use Fitbit Charge Heart Rate devices with two separate populations to continuously record data to evaluate PACD, nine participants in a hospitalized inpatient rehabilitation group and eight in a healthy control group. We apply PACD to minute-by-minute Fitbit data to quantify changes within and between the groups. The inpatient rehabilitation group exhibited greater variability in change throughout inpatient rehabilitation for both step count and heart rate, with the greatest change occurring at the end of the inpatient hospital stay, which exceeded day-to-day changes of the control group. Our additions to PACD support effective change analysis of wearable sensor data collected in an inpatient rehabilitation setting and provide insight to patients, clinicians, and researchers. [ABSTRACT FROM AUTHOR]

Background: The goal of this work is to develop a digital version of a standard cognitive assessment, the Trail Making Test (TMT), and assess its utility.Objective: This paper introduces a novel digital version of the TMT and introduces a machine learning based approach to assess its capabilities.Methods: Using digital Trail Making Test (dTMT) data collected from (N = 54) older adult participants as feature sets, we use machine learning techniques to analyze the utility of the dTMT and evaluate the insights provided by the digital features.Results: Predicted TMT scores correlate well with clinical digital test scores (r = 0.98) and paper time to completion scores (r = 0.65). Predicted TICS exhibited a small correlation with clinically derived TICS scores (r = 0.12 Part A, r = 0.10 Part B). Predicted FAB scores exhibited a small correlation with clinically derived FAB scores (r = 0.13 Part A, r = 0.29 for Part B). Digitally derived features were also used to predict diagnosis (AUC of 0.65).Conclusion: Our findings indicate that the dTMT is capable of measuring the same aspects of cognition as the paper-based TMT. Furthermore, the dTMT's additional data may be able to help monitor other cognitive processes not captured by the paper-based TMT alone. [ABSTRACT FROM AUTHOR]

Background: The goal of this research is to use smart home technology to assist people who are recovering from injuries or coping with disabilities to live independently.Objective: We introduce an algorithm to model and forecast wake and sleep behaviors that are exhibited by the participant. Furthermore, we propose that sleep behavior is impacted by and can be modeled from wake behavior, and vice versa.Methods: This paper describes the Behavior Forecasting (BF) algorithm. BF consists of 1) defining numeric values that reflect sleep and wake behavior, 2) forecasting wake and sleep values from past behavior, 3) analyzing the effect of wake behavior on sleep and vice versa, and 4) improving prediction performance by using both wake and sleep scores.Results: The BF method was evaluated with data collected from 20 smart homes. We found that regardless of the forecasting method utilized, wake behavior and sleep behavior can be modeled with a minimum accuracy of 84%. Additionally, normalizing the wake and sleep scores drastically improves the accuracy to 99%.Conclusions: The results show that we can effectively model wake and sleep behaviors in a smart environment. Furthermore, wake behaviors can be predicted from sleep behaviors and vice versa. [ABSTRACT FROM AUTHOR]

Smart homes offer an unprecedented opportunity to unobtrusively monitor human behavior in everyday environments and to determine whether relationships exist between behavior and health changes. Behavior change detection (BCD) can be used to identify changes that accompany health events, which can potentially save lives. [ABSTRACT FROM AUTHOR]

Transfer learning aims to improve performance on a target task by utilizing previous knowledge learned from source tasks. In this paper we introduce a novel heterogeneous transfer learning technique, Feature-Space Remapping (FSR), which transfers knowledge between domains with different feature spaces. This is accomplished without requiring typical feature-feature, feature instance, or instance-instance co-occurrence data. Instead we relate features in different feature-spaces through the construction of metafeatures. We show how these techniques can utilize multiple source datasets to construct an ensemble learner which further improves performance. We apply FSR to an activity recognition problem and a document classification problem. The ensemble technique is able to outperform all other baselines and even performs better than a classifier trained using a large amount of labeled data in the target domain. These problems are especially difficult because, in addition to having different feature-spaces, the marginal probability distributions and the class labels are also different. This work extends the state of the art in transfer learning by considering large transfer across dramatically different spaces. [ABSTRACT FROM AUTHOR]

The increasing aging population in the coming decades will result in many complications for society and in particular for the healthcare system due to the shortage of healthcare professionals and healthcare facilities. To remedy this problem, researchers have pursued developing remote monitoring systems and assisted living technologies by utilizing recent advances in sensor and networking technology, as well as in the data mining and machine learning fields. In this article, we report on our fully automated approach for discovering and monitoring patterns of daily activities. Discovering and tracking patterns of daily activities can provide unprecedented opportunities for health monitoring and assisted living applications, especially for older adults and individuals with mental disabilities. Previous approaches usually rely on preselected activities or labeled data to track and monitor daily activities. In this article, we present a fully automated approach by discovering natural activity patterns and their variations in real-life data. We will show how our activity discovery component can be integrated with an activity recognition component to track and monitor various daily activity patterns. We also provide an activity visualization component to allow caregivers to visually observe and examine the activity patterns using a user-friendly interface. We validate our algorithms using real-life data obtained from two apartments during a three-month period. Categories and Subject Descriptors: H.2.8 [Database Management]: Database Applications -- Data mining; 1.2.6 [Artificial Intelligence]: Learning; H.4.m [Information Systems Applications]: Miscellaneous General Terms: Design, Experimentation, Algorithms [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The CASAS architecture facilitates the development and implementation of future smart home technologies by offering an easy-to-install lightweight design that provides smart home capabilities out of the box with no customization or training. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM AUTHOR]

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 carried out 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 are 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. [ABSTRACT FROM AUTHOR]

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 collecting 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 test beds. [ABSTRACT FROM AUTHOR]

Much of the data that is collected and analyzed today is structural, consisting not only of entities but also of relationships between the entities. As a result, analysis applications rely on automated structural data mining approaches to find patterns and concepts of interest. This ability to analyze structural data has become a particular challenge in many security-related domains. In these domains, focusing on the relationships between entities in the data is critical to detect important underlying patterns. In this study we apply structural data mining techniques to automate analysis of nuclear smuggling data. In particular, we choose to model the data as a graph and use graph-based relational learning to identify patterns and concepts of interest in the data. In this article, we identify the analysis questions that are of importance to security analysts and describe the knowledge representation and data mining approach that we adopt for this challenge. We analyze the results using the Russian nuclear smuggling event database. [ABSTRACT FROM AUTHOR]

Recent advancements in supporting fields have increased the likelihood that smart-home technologies will become part of our everyday environments. However, many of these technologies are brittle and do not adapt to the user's explicit or implicit wishes. Here, we introduce CASAS, an adaptive smart-home system that utilizes machine learning techniques to discover patterns in resident's daily activities and to generate automation polices that mimic these patterns. Our approach does not make any assumptions about the activity structure or other underlying model parameters but leaves it completely to our algorithms to discover the smart-home resident's patterns. Another important aspect of CASAS is that it can adapt to changes in the discovered patterns based on the resident implicit and explicit feedback and can automatically update its model to reflect the changes. In this paper, we provide a description of the CASAS technologies and the results of experiments performed on bothsynthetic and real-world data. [ABSTRACT FROM AUTHOR]

The article highlights a literature survey of the automatic video classification which aimed at helping viewers find video of interest. In the survey, the authors find that features are drawn from three modalities: text, audio and visual, and that a large variety of combinations of features and classification have been explored and looked into. They describe the general features chosen and summarize the research in this area. Moreover, conclusions and some ideas for further research are provided.

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 Al capabilities can be tested and compared to human performance. [ABSTRACT FROM AUTHOR]

The article focuses on the design of the ProPHeT decision-learning algorithm that learns a strategy for controlling a smart environment based on sensor observation, power line control, and the generated hierarchical model. The performance of the algorithm is evaluated using real data collected from the MavHome smart home and smart office environments. Information about the performance application of the algorithm is discussed.

Historically, data mining research has been focused on discovering sets of attributes that discriminate data entities into classes or association rules between attributes. In contrast, we are working to develop data mining techniques to discover patterns consisting of complex relationships between entities. Our research is particularly applicable to domains in which the data is event driven, such as counter-terrorism intelligence analysis. In this paper we describe an algorithm designed to operate over relational data received from a continuous stream. Our approach includes a mechanism for summarizing discoveries from previous data increments so that the globally best patterns can be computed by examining only the new data increment. We then describe a method by which relational dependencies that span across temporal increment boundaries can be efficiently resolved so that additional pattern instances, which do not reside entirely in a single data increment, can be discovered. We also describe a method for change detection using a measure of central tendency designed for graph data. We contrast two formulations of the change detection process and demonstrate the ability to identify salient changes along meaningful dimensions and recognize trends in a relational data stream. [ABSTRACT FROM AUTHOR]

We describe an approach to learning patterns in relational data represented as a graph. The approach, implemented in the Subdue system, searches for patterns that maximally compress the input graph. Subdue can be used for supervised learning, as well as unsupervised pattern discovery and clustering. We apply Subdue in domains related to homeland security and social network analysis. [ABSTRACT FROM AUTHOR]

This study proposes that goal continuity contributes incrementally to older adult perceived marital adjustment when controlling for the five-factor model of personality (FFM). Analyses using correlational techniques, including hierarchical linear modeling, were used to test our hypothesis for a group of 117 currently married older adults. The final model, which included the FFM variables and goal continuity, provided the best fit to the data, with goal continuity being the largest and most significant predictor. Consistent with previous research using the construct, goal continuity served as an important predictor of older adult behavior and perceptions. Implications for conducting research in the areas of marital adjustment and continuity theory are discussed. [Copyright &y& Elsevier]

Prediction is an important component in a variety of domains in Artificial Intelligence and Machine Learning, in order that Intelligent Systems may make more informed and reliable decisions. Certain domains require that prediction be performed on sequences of events that can typically be modeled as stochastic processes. This work presents Active LeZi (ALZ), a sequential prediction algorithm that is founded on an Information Theoretic approach, and is based on the acclaimed LZ78 family of data compression algorithms. The efficacy of this algorithm in a typical Smart Environment – the Smart Home, is demonstrated by employing this algorithm to predict device usage in the home. The performance of this algorithm is tested on synthetic data sets that are representative of typical interactions between a Smart Home and the inhabitant. In addition, for the Smart Home environment, we introduce a method of learning a measure of the relative time between actions using ALZ, and demonstrate the efficacy of this approach on synthetic Smart Home data. [ABSTRACT FROM AUTHOR]

An intelligent home is likely in the near future. An important ingredient in an intelligent environment such as a home is prediction – of the next low-level action, the next location, and the next high-level task that an inhabitant is likely to perform. In this paper we model inhabitant actions as states in a simple Markov model. We introduce an enhancement to this basic approach, the Task-based Markov model (TMM) method. TMM discovers high-level inhabitant tasks using the supplied unlabeled data. We investigate clustering of actions to identify tasks, and integrate clusters into a hidden Markov model that predicts the next inhabitant action. We validate our approach and observe that for simulated data we achieve good accuracy using both the simple Markov model and the TMM, whereas on real data we see that simple Markov models outperform the TMM. We also perform an analysis of the performance of the HMM in the framework of the TMM when diverse patterns are introduced into the data. [ABSTRACT FROM AUTHOR]

Human and software agents exhibit regularities in their activities. We describe our ONASI algorithm, which derives a Markov model from such observed regularities, and dynamically scales the model through merging and splitting of states. ONASI uses this model to predict the agent's next action. We evaluate ONASI's predictive accuracy on a dataset of Wumpus World games, and demonstrate from this approach that the model can correctly predict the agent's next action with adjustable computation and memory resources. These predictions can be used to imitate, assist or obstruct an agent. [ABSTRACT FROM AUTHOR]

The World Wide Web provides an immense source of information. Accessing information of interest presents a challenge to scientists and analysts, particularly if the desired information is structural in nature. Our goal is to design a structural search engine that uses the hyperlink structure of the Web, in addition to textual information, to search for sites of interest. Our structural search engine, called WebSUBDUE, searches not only for particular words or topics but also for a desired hyperlink structure. Enhanced by WordNet text functions, our search engine retrieves sites corresponding to structures formed by graph-based user queries. We hypothesize that this system can form the heart of a structural query engine, and demonstrate the approach on a number of structural web queries. [ABSTRACT FROM AUTHOR]

Hierarchical conceptual clustering has proven to be a useful,although under-explored,data mining technique. A graph-based representation of structural information combined with a substructure discovery technique has been shown to be successful in knowledge discovery. The SUBDUE substructure discovery system provides one such combinat on of approaches. This work presents SUBDUE and the development of its clustering functionalities. Several examples are used to illustrate the validity of the approach both in structured and unstructured domains,as well as to compare SUBDUE to the Cobweb clustering algorithm. We also develop a new metric for comparing structurally-def ned clusterings. Results show that SUBDUE successfully discovers hierarchical clusterings in both structured and unstructured data. [ABSTRACT FROM AUTHOR]

To reduce the complexity of studying a parallel mechanism for natural language learning and understanding which supports both utterance and discourse processing, we propose a computational model demonstrating the performance of a young language user just starting to construct one and two word utterances. This model is constructed using discoveries from both connectionism and symbolic research which guided the construction of our own cognitive modeling system. This system consists of 1) a cognitive theory, 2) a cognitive and a computational architecture, and 3) the support environment necessary to construct concurrent symbolic-based cognitive models using a multi-agent environment. After providing the discourse performance of the resulting model, we also address the pragmatic consideration of building and supporting the resulting modeling system and language model on a cluster of SMP computers. [ABSTRACT FROM AUTHOR]

Features the Subdue system of data mining on databases represented as graphs. Discovery of substructures that compresses the original data; Basis of heuristic for evaluating substructures; Applications of the system. INSETS: Data Mining: A Long-Term..., by David Waltz and Se June Hong.;Related work..

Knowledge engineering for planning is expensive and the resulting knowledge can be imperfect. To autonomously learn a plan operator definition from environmental feedback, our learning system WISER explores an instantiated literal space using a breadth-first search technique. Each node of the search tree represents a state, a unique subset of the instantiated literal space. A state at the root node is called a seed state. WISER can generate seed states with or without utilizing imperfect expert knowledge. WISER experiments with an operator at each node. The positive state, in which an operator can be successfully executed, constitutes initial preconditions of an operator. We analyze the number of required experiments as a function of the number of missing preconditions in a seed state. We introduce a naive domain assumption to test only a subset of the exponential state space. Since breadth-first search is expensive, WISER introduces two search techniques to reorder literals at each level of the search tree. We demonstrate performance improvement using the naive domain assumption and literal-ordering heuristics. To learn the effects of an operator, WISER computes the delta state, composed of the add list and the delete list, and parameterizes it. Unlike previous systems, WISER can handle unbound objects in the delta state. We show that machine-generated effects definitions are often simpler in representation than expert-provided definitions. [ABSTRACT FROM AUTHOR]

Presents a method for guiding the discovery process with domain-specific knowledge as it pertains to computer science. Use of SUBDUE discovery systems to evaluate the benefits of using process with domain knowledge to guide the discovery process; Information on domain knowledge; Details on the results; Illustration of the benefits.

Focuses on how to realize the benefits of domain-dependent discovery approaches in artificial intelligence by adding domain-specific knowledge to domain-independent discovery system. Benefits of using domain-specific information; Features of the minimum description length principle; Features of the Subdue system; Scalability of structure discovery using the Subdue system.

Describes a decision-theoretic approach to cooperative sensor planning between multiple autonomous vehicles executing a military mission. Type of reasoning which must be used for this autonomous vehicle application; How decisions are made; Use of cooperation to balance the work load; Information on the theoretical foundations of cooperative sensor planning research.

Smart-Home in a Box (SHiB) is a ubiquitous system that intends to improve older adults' life quality. SHiB requires self-installation before use. Our previous study found that it is not easy for seniors to install SHiB correctly. SHiB CBLE is a computer-based learning environment that is designed to help individuals install a SHiB kit. This article presents an experiment examining how smart home sensor installation was affected by knowledge gained from two methods, SHiB CBLE, and a written document. Results show that participants who were trained by the CBLE took significantly ( p < 0.05 ) less time in the installation session than those in the control group. The accuracy rate of SHiB kit installation is 78% for the group trained by the CBLE and 77% for the control group. Participants trained by the CBLE showed significantly ( p < 0.01 ) higher confidence in the actual installation than those in the control group. These results suggest that having a training before the actual installation will help installers avoid unnecessary work, shorten the installation time, and increase installers' confidence. [ABSTRACT FROM AUTHOR]

The article discusses technical advances that are helping to create intelligent homes. The author notes that over the course of the 20th century, advances in technology have helped enhance the comfort and shelter provided by homes, suggesting that insights gained from capturing and modeling behavior in homes can be useful in creating an indoor environment more intelligent and responsive to individuals' needs. Topics include a brief overview of ambient intelligence, which uses sensors to collect information about the environment and the residents, the goal of ambient research, and examples of some of the technologies offered by ambient intelligent homes, such as automated health monitoring, assessment, and intervention.

Digital markers of behavior can be continuously created, in everyday settings, using time series data collected by ambient sensors. The goal of this work was to perform individual- and population-level behavior analysis from such time series sensor data. In this paper, we introduce a novel algorithm—Resident Relative Entropy-Inverse Reinforcement Learning (RRE-IRL)—to perform an analysis of a single smart home resident or a group of residents, using inverse reinforcement learning. By employing this method, we learnt an individual's behavioral routine preferences. We then analyzed daily routines for an individual and for eight smart home residents grouped by health diagnoses. We observed that the behavioral routine preferences changed over time. Specifically, the probability that the observed behavior was the same at the beginning of data collection as it was at the end (months later) was lower for residents experiencing cognitive decline than for cognitively healthy residents. When comparing aggregated behavior between groups of residents from the two diagnosis groups, the behavioral difference was even greater. Furthermore, the behavior preferences were used by a random forest classifier to predict a resident's cognitive health diagnosis, with an accuracy of 0.84. [ABSTRACT FROM AUTHOR]

Creation of realistic synthetic behavior-based sensor data is an important aspect of testing machine learning techniques for healthcare applications. Many of the existing approaches for generating synthetic data are often limited in terms of complexity and realism. We introduce SynSys, a machine learning-based synthetic data generation method, to improve upon these limitations. We use this method to generate synthetic time series data that is composed of nested sequences using hidden Markov models and regression models which are initially trained on real datasets. We test our synthetic data generation technique on a real annotated smart home dataset. We use time series distance measures as a baseline to determine how realistic the generated data is compared to real data and demonstrate that SynSys produces more realistic data in terms of distance compared to random data generation, data from another home, and data from another time period. Finally, we apply our synthetic data generation technique to the problem of generating data when only a small amount of ground truth data is available. Using semi-supervised learning we demonstrate that SynSys is able to improve activity recognition accuracy compared to using the small amount of real data alone. [ABSTRACT FROM AUTHOR]

AbstractObjectiveMethodResultsConclusionExtraction of digital markers from passive sensors placed in homes is a promising method for understanding real-world behaviors. In this study, machine learning (ML) and multilevel modeling (MLM) are used to examine types of digital markers and whether smart home sensors can predict cognitive functioning, lifestyle behaviors, and contextual factors measured through ecological momentary assessment (EMA).Smart home sensors were installed in the homes of 44 community-dwelling midlife and older adults for 3–4 months. Sensor data were categorized into eight digital markers. Participants responded to iPad-delivered EMA prompts 4×/day for 2 wk. Prompts included an n-back task and survey on recent (past 2 h) lifestyle and contextual factors.ML marker rankings revealed that sensor counts (indicating increased activity) and time outside the home were among the most influential markers for all survey questions. Additionally, MLM revealed for every 1000 sensor counts, mental sharpness, social, physical, and cognitive EMA responses increased by 0.134–0.155 points on a 5-point scale. For every additional 30-minutes spent outside home, social, physical, and cognitive EMA responses increased by 0.596, 0.472, and 0.157 points. Advanced ML joint classification/regression significantly predicted EMA responses from smart home digital markers with error of 0.370 on a 5-point scale, and n-back performance with a normalized error of 0.040.Results from ML and MLM were complimentary and comparable, suggesting that machine learning may be used to develop generalized models to predict everyday cognition and track lifestyle behaviors and contextual factors that impact health outcomes using smart home sensor data. [ABSTRACT FROM AUTHOR]

Objective: In the real-world environment, multiple and interacting state-dependent factors (e.g., fatigue, distractions) can cause cognitive failures and negatively impact everyday activities. This study used ecological momentary assessment (EMA) and a n-back task to examine the relationship between fluctuating levels of cognition measured in the real-world environment and self-report and performance-based measures of functional status. Method: Thirty-five community-dwelling older adults (M age = 71.80) completed a brief battery of objective and self-report measures of cognitive and functional status. After completing 100, 45-second trials to reach stable performance on a n-back task, EMA data collection began. Four times daily for one week, participants received prompts on a tablet to complete a n-back task and a brief survey. From the EMA n-back trials, measures of EMA average performance and intra-individual variability (IIV) across performances were created. Results: For the EMA n-back, the correlation between IIV and EMA average was weak and non-significant. IIV associated with self-report measures, and EMA average with the objective, performance-based functional status composite. Hierarchical regressions further revealed that IIV was a significant predictor of self-reported functional status and cognitive failures over and above EMA average performance and global cognitive status. In contrast, for the objective, functional status composite, IIV did not explain additional variance. Conclusions: The findings suggest that IIV and self-report measures of functional status and cognitive failures may capture a real-world cognitive capacity that fluctuates over time and with context; one that may not easily be captured by objective, performance-based measures designed to assess optimal function. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses various reports published within the issue which include the approaches to user interfaces, the human-computer interaction, and the system designs for ambient intelligence.

Change Point Detection (CPD) is the problem of discovering time points at which the behavior of a time series changes abruptly. In this paper, we present a novel real-time nonparametric change point detection algorithm called SEP, which uses Separation distance as a divergence measure to detect change points in high-dimensional time series. Through experiments on artificial and real-world datasets, we demonstrate the usefulness of the proposed method in comparison with existing methods. [ABSTRACT FROM AUTHOR]

The article discusses the developments of the publication and offers information on topics focused within the issue. According to the author, the success of the publication was greatly attributed to the quality of the editorial board which maintained a turnaround time for submitted papers which are being published. Some of the topics in the issue include those in the areas of control, fuzzy systems, and neural networks.

Recent progress in Internet of Things (IoT) platforms has allowed us to collect large amounts of sensing data. However, there are significant challenges in converting this large-scale sensing data into decisions for real-world applications. Motivated by applications like health monitoring and intervention and home automation we consider a novel problem called Activity Prediction, where the goal is to predict future activity occurrence times from sensor data. In this paper, we make three main contributions. First, we formulate and solve the activity prediction problem in the framework of imitation learning and reduce it to a simple regression learning problem. This approach allows us to leverage powerful regression learners that can reason about the relational structure of the problem with negligible computational overhead. Second, we present several metrics to evaluate activity predictors in the context of real-world applications. Third, we evaluate our approach using real sensor data collected from 24 smart home testbeds. We also embed the learned predictor into a mobile-device-based activity prompter and evaluate the app for nine participants living in smart homes. Our results indicate that our activity predictor performs better than the baseline methods, and offers a simple approach for predicting activities from sensor data. [ABSTRACT FROM PUBLISHER]