Smart Home Residents Research Articles

Unobtrusive Monitoring of Clinical Deterioration in Smart Homes.

Clinical deterioration (CD) is the physiological decompensation that incurs care escalation, protracted hospital stays, or even death. The early warning score (EWS) calculates the occurrence of CD based on five vital signs. However, there are limited reports regarding EWS monitoring in smart home settings. This study aims to design a CD detection system for health monitoring at home (HM@H) that automatically identifies unstable vital signs and alarms the medical emergency team. We conduct a requirement analysis by interviewing experts. We use unified modeling language (UML) diagrams to define the behavioral and structural aspects of HM@H. We developed a prototype using a SQL-based database and Python to calculate the EWS in the front end. A team of five experts assessed the accuracy and validity of the designed system. The requirement analysis for four main users yielded 30 data elements and 10 functions. Three main components of HM@H are the graphical user interface (GUI), the application programming interface (API), and the server. Results show the possibility of using unobtrusive sensors to collect smart home residents' vital signs and calculate their EWS scores in real-time. However, further implementation with real data, for frail elderly and hospital-discharged patients is required.

Automated Cognitive Health Assessment Based on Daily Life Functional Activities.

Dementia is increasing day-by-day in older adults. Many of them are spending their life joyfully due to smart home technologies. Smart homes contain several smart devices which can support living at home. Automated assessment of smart home residents is a significant aspect of smart home technology. Detecting dementia in older adults in the early stage is the basic need of this time. Existing technologies can detect dementia timely but lacks performance. In this paper, we proposed an automated cognitive health assessment approach using machines and deep learning based on daily life activities. To validate our approach, we use CASAS publicly available daily life activities dataset for experiments where residents perform their routine activities in a smart home. We use four machine learning algorithms: decision tree (DT), Naive Bayes (NB), support vector machine (SVM), and multilayer perceptron (MLP). Furthermore, we use deep neural network (DNN) for healthy and dementia classification. Experiments reveal the 96% accuracy using the MLP classifier. This study suggests using machine learning classifiers for better dementia detection, specifically for the dataset which contains real-world data.

Behavioral Differences Between Subject Groups Identified Using Smart Homes and Change Point Detection.

With the arrival of the internet of things, smart environments are becoming increasingly ubiquitous in our everyday lives. Sensor data collected from smart home environments can provide unobtrusive, longitudinal time series data that are representative of the smart home resident's routine behavior and how this behavior changes over time. When longitudinal behavioral data are available from multiple smart home residents, differences between groups of subjects can be investigated. Group-level discrepancies may help isolate behaviors that manifest in daily routines due to a health concern or major lifestyle change. To acquire such insights, we propose an algorithmic framework based on change point detection called Behavior Change Detection for Groups (BCD-G). We hypothesize that, using BCD-G, we can quantify and characterize differences in behavior between groups of individual smart home residents. We evaluate our BCD-G framework using one month of continuous sensor data for each of fourteen smart home residents, divided into two groups. All subjects in the first group are diagnosed with cognitive impairment. The second group consists of cognitively healthy, age-matched controls. Using BCD-G, we identify differences between these two groups, such as how impairment affects patterns of performing activities of daily living and how clinically-relevant behavioral features, such as in-home walking speed, differ for cognitively-impaired individuals. With the unobtrusive monitoring of smart home environments, clinicians can use BCD-G for remote identification of behavior changes that are early indicators of health concerns.

Automated cognitive health assessment in smart homes using machine learning

The Internet of Things (IoT) provides smart solutions for future urban communities to address key benefits with the least human intercession. A smart home offers the necessary capabilities to promote efficiency and sustainability to a resident with their healthcare-related, social, and emotional needs. In particular, it provides an opportunity to assess the functional health ability of the elderly or individuals with cognitive impairment in performing daily life activities. This work proposes an approach named Cognitive Assessment of Smart Home Resident (CA-SHR) to measure the ability of smart home residents in executing simple to complex activities of daily living using pre-defined scores assigned by a neuropsychologist. CA-SHR also measures the quality of tasks performed by the participants using supervised classification. Furthermore, CA-SHR provides a temporal feature analysis to estimate if the temporal features help to detect impaired individuals effectively. The goal of this study is to detect cognitively impaired individuals in their early stages. CA-SHR assess the health condition of individuals through significant features and improving the representation of dementia patients. For the classification of individuals into healthy, Mild Cognitive Impaired (MCI), and dementia categories, we use ensemble AdaBoost. This results in improving the reliability of the CA-SHR through the correct assignment of labels to the smart home resident compared with existing techniques.

Analyzing Sensor-Based Individual and Population Behavior Patterns via Inverse Reinforcement Learning.

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.

Wavelet-Based Filtration Procedure for Denoising the Predicted CO2 Waveforms in Smart Home within the Internet of Things.

The operating cost minimization of smart homes can be achieved with the optimization of the management of the building’s technical functions by determination of the current occupancy status of the individual monitored spaces of a smart home. To respect the privacy of the smart home residents, indirect methods (without using cameras and microphones) are possible for occupancy recognition of space in smart homes. This article describes a newly proposed indirect method to increase the accuracy of the occupancy recognition of monitored spaces of smart homes. The proposed procedure uses the prediction of the course of CO2 concentration from operationally measured quantities (temperature indoor and relative humidity indoor) using artificial neural networks with a multilayer perceptron algorithm. The mathematical wavelet transformation method is used for additive noise canceling from the predicted course of the CO2 concentration signal with an objective increase accuracy of the prediction. The calculated accuracy of CO2 concentration waveform prediction in the additive noise-canceling application was higher than 98% in selected experiments.

Cyber-physical Support of Daily Activities

This article introduces RAS, a cyber-physical system that supports individuals with memory limitations to perform daily activities in their own homes. RAS represents a partnership between a smart home, a robot, and software agents. When smart home residents perform activities, RAS senses their movement in the space and identifies the current activity. RAS tracks activity steps to detect omission errors. When an error is detected, the RAS robot finds and approaches the human with an offer of assistance. Assistance consists of playing a video recording of the entire activity, showing the omitted activity step, or guiding the resident to the object that is required for the current step. We evaluated RAS performance for 54 participants performing three scripted activities in a smart home testbed and for 2 participants using the system over multiple days in their own homes. In the testbed experiment, activity errors were detected with a sensitivity of 0.955 and specificity of 0.992. RAS assistance was performed successfully with a rate of 0.600. In the in-home experiments, activity errors were detected with a combined sensitivity of 0.905 and a combined specificity of 0.988. RAS assistance was performed successfully for the in-home experiments with a rate of 0.830.

Business Platform Model for Smart Home for Technology Planning Task Force

In this study we used an architectural research method based on the Open Services Gateway Initiative and Mobile Agent Technology, which is a model for analyzing smart home business platforms for technology governance planning tasks that include the recommended decision flow process and as recommendations from established business models and expanded to include a series of additional parameters needed to produce a business platform so that it can analyze the business model offered by the developer and detailed analysis of proposed governance in the service sector. And finally this research produces a Framework that presents a technology governance model for updating strategic plans and governance models for technology and frameworks designed to meet the needs of the elderly population and as recommendations for smart home management that aim to measurably improve the welfare of people in Indonesia. The proposed model and strategy recognizes the diversity of the community with existing providers of household tools and developers to optimize resources to achieve the goals and objectives of elderly smart home residents

Smart home resident identification based on behavioral patterns using ambient sensors

In this paper, a novel approach is presented to identify the smart home residents. The different behavioral patterns of smart home’s inhabitants are exploited to distinguish the residents. The variation of a specific individual behavior in smart homes is a significant challenge. We introduce different features that are useful to handle this problem. Moreover, we introduce an innovative strategy which considers the Bag of Sensor Events and Bayesian networks. In the Bag of Sensor Events approach, the frequency of each sensor event occurrence is considered, regardless of the order of sensor events. The efficiency of the Bag of sensor Events approach is compared to the Sequence of Sensor Events. Our experiments confirm that the Bag of Sensor Events approach outperformed the previous approaches. When the smart homes residents are people who repeat their daily activities frequently, applying the Bag of Sensor Events on Activity Based Window Frame features, which considers the performed daily activities, would identify them more accurately. In contrast, in cases where residents perform their activities in different ways, considering the Time Based Window Frame leads to higher accuracy in distinguishing residents. In this approach, the features are created by considering the constant time intervals. The F-measure of our proposed approach on the Twor2009, Tulum2009, and Tulum2010 datasets is 96%, 100%, and 99%, respectively, which improves the results of the previous researches which consider behavioral patterns to identify smart home residents.

Detecting and exploring deviating behaviour of smart home residents

A system for detecting deviating human behaviour in a smart home environment is the long-term goal of this work. Clearly, such systems will be very important in ambient assisted living services. A new approach to modelling human behaviour patterns is suggested in this paper. The approach reveals promising results in unsupervised modelling of human behaviour and detection of deviations by using such a model. Human behaviour/activity in a short time interval is represented in a novel fashion by responses of simple non-intrusive sensors. Deviating behaviour is revealed through data clustering and analysis of associations between clusters and data vectors representing adjacent time intervals (analysing transitions between clusters). To obtain clusters of human behaviour patterns, first, a random forest is trained without using beforehand defined teacher signals. Then information collected in the random forest data proximity matrix is mapped onto the 2D space and data clusters are revealed there by agglomerative clustering. Transitions between clusters are modelled by the third order Markov chain.Three types of deviations are considered: deviation in time, deviation in space and deviation in the transition between clusters of similar behaviour patterns.The proposed modelling approach does not make any assumptions about the position, type, and relationship of sensors but is nevertheless able to successfully create and use a model for deviation detection–this is claimed as a significant result in the area of expert and intelligent systems. Results show that spatial and temporal deviations can be revealed through analysis of a 2D map of high dimensional data. It is demonstrated that such a map is stable in terms of the number of clusters formed. We show that the data clusters can be understood/explored by finding the most important variables and by analysing the structure of the most representative tree.

Unsupervised daily routine and activity discovery in smart homes.

The ability to accurately recognize daily activities of residents is a core premise of smart homes to assist with remote health monitoring. Most of the existing methods rely on a supervised model trained from a preselected and manually labeled set of activities, which are often time-consuming and costly to obtain in practice. In contrast, this paper presents an unsupervised method for discovering daily routines and activities for smart home residents. Our proposed method first uses a Markov chain to model a resident's locomotion patterns at different times of day and discover clusters of daily routines at the macro level. For each routine cluster, it then drills down to further discover room-level activities at the micro level. The automatic identification of daily routines and activities is useful for understanding indicators of functional decline of elderly people and suggesting timely interventions.

A Study on the Characteristics of Active Aging for the Development of Healthcare Services -Mainly based on the deduction of the appropriate services for AAL-based smart-home residents-

A Study on the Characteristics of Active Aging for the Development of Healthcare Services -Mainly based on the deduction of the appropriate services for AAL-based smart-home residents-

Ink Spraying Based Liquid Metal Printed Electronics for Directly Making Smart Home Appliances

The quickly emerging smart home is heavily involved with various information technologies where electronic devices play perhaps the most important roles. However, there is currently a strong lack of an efficient tool for the direct manufacture of electronics as desired. Meanwhile, although printed electronics has aroused much attention in making printed circuit board, radio frequency identification card, sensor and so on, few existing approaches can fulfill the above needs. Among various technologies, liquid metal spraying method owns rather unique merits in printing electronically conductive patterns on nearly all kinds of substrates, either hard or soft, rough or smooth. For this reason, we are dedicated here to extend such method into a generalized strategy of printing electronics for the direct construction of smart home appliances which would generate profound influence on future daily lives. Through adopting the room temperature liquid metal GaIn24.5 as the electronic ink and with various designed masks, a group of typical conductive patterns were directly printed on the surfaces of organic glass, marble windowsill, paper, wood plank of cabinet, building wall and so on, which consist of the most popular materials that could serve as circuit substrates in a home. It was shown that this energy-saving printing method brings about big convenience for equipping future smart home residents. Several new conceptual homemade printed patterns or circuits in providing services such as decoration and entertainment were illustrated. Further, the basic electronic structures of the smart home and the generalized concept of adopting any substrate surface as printed circuit board were explained. The liquid metal spray printing suggests a powerful tool for making consumer electronics and constructing future smart home. It also raised important scientific and technical issues worth of pursuing in the coming time.

Emotion Recognition in Human Computer Interaction Systems

In order to ensure comfortable life human computer interaction (HCI) systems are widely used in the smart home. It is important to detect and respond to the smart home residents’ emotional state and reduce stress levels. The HCI system for emotional state recognition is developed. This paper deals with stressful state recognition by means of the heart rate variability (HRV) analysis, because it is a non-invasive method. The emotional state should be identified in the situations which correspond to real life at home: a person sits, walks, and changes his/her posture over time. The impact of the emotional state and the posture impact on heart rate variability are examined. Time domain, frequency domain and nonlinear parameters are calculated. The parameters that are most sensitive to the emotional state are chosen. Variability of the HRV parameters are verified over time. It was found that posture has a great impact on the HRV parameters, so posture detection subsystem is integrated in our HCI system for emotion recognition. The subject- dependent thresholds should be used for emotional state recognition. DOI: http://dx.doi.org/10.5755/j01.eee.20.10.8878

CRAFFT: An Activity Prediction Model based on Bayesian Networks.

Recent advances in the areas of pervasive computing, data mining, and machine learning offer unique opportunities to provide health monitoring and assistance for individuals facing difficulties to live independently in their homes. Several components have to work together to provide health monitoring for smart home residents including, but not limited to, activity recognition, activity discovery, activity prediction, and prompting system. Compared to the significant research done to discover and recognize activities, less attention has been given to predict the future activities that the resident is likely to perform. Activity prediction components can play a major role in design of a smart home. For instance, by taking advantage of an activity prediction module, a smart home can learn context-aware rules to prompt individuals to initiate important activities. In this paper, we propose an activity prediction model using Bayesian networks together with a novel two-step inference process to predict both the next activity features and the next activity label. We also propose an approach to predict the start time of the next activity which is based on modeling the relative start time of the predicted activity using the continuous normal distribution and outlier detection. To validate our proposed models, we used real data collected from physical smart environments.

Activity modeling under uncertainty by trace of objects in smart homes

A typical resident of a smart home can be an Alzheimer patient that forgets sometimes to complete the activities that he begins. The key point to assist the smart home resident is to model the activities and discover correct realization patterns of activities. To accomplish this task, we apply sensors to provide primary data about realization patterns of actions, operations, plans, goals and generally any objective that the smart home resident may desire to do. In the consequence, by applying fuzzy clustering techniques, we are able to mine sensor data to retrieve the realization patterns of activities, and so the prediction patterns of intentions are recognizable. Comparing the realization patterns with prediction patterns of activities, we would be able to predict the intention of the resident about the activity that the resident considers to realize. In this way, we would be able to provide hypotheses about the resident goals and his possible goal achievement’s defects. Spatiotemporal aspects of daily activities such as movement of objects are surveyed to discover the patterns of activities realized by the smart homes residents. In this research, uncertainty is considered as a property of activity recognition.

Sensor selection to support practical use of health‐monitoring smart environments

AbstractThe data mining and pervasive sensing technologies found in smart homes offer unprecedented opportunities for providing health monitoring and assistance to individuals experiencing difficulties in 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 datasets collected in five distinct smart home environments. © 2011 John Wiley & Sons, Inc. WIREs Data Mining Knowl Discov 2011 1 339–351 DOI: 10.1002/widm.20This article is categorized under: Application Areas > Science and Technology

Discovering Activities to Recognize and Track in a Smart Environment

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.

Annotating smart environment sensor data for activity learning

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. In order to monitor the functional health of smart home residents, we need to design technologies that recognize and track the activities that people perform at home. Machine learning techniques can perform this task, but the software algorithms rely upon large amounts of sample data that is correctly labeled with the corresponding activity. Labeling, or annotating, sensor data with the corresponding activity can be time consuming, may require input from the smart home resident, and is often inaccurate. Therefore, in this paper we investigate four alternative mechanisms for annotating sensor data with a corresponding activity label. We evaluate the alternative methods along the dimensions of annotation time, resident burden, and accuracy using sensor data collected in a real smart apartment.