Indoor air quality (IAQ) parameters are not only directly related to occupational health but also have a significant impact on quality of life as people typically spend more than 90% of their time in ...indoor environments. Although IAQ is not usually monitored, it must be perceived as a relevant issue to follow up for the inhabitants' well-being and comfort for enhanced living environments and occupational health. Carbon dioxide (CO
) has a substantial influence on public health and can be used as an essential index of IAQ. CO
levels over 1000 ppm, indicates an indoor air potential problem. Monitoring CO
concentration in real-time is essential to detect IAQ issues to quickly intervene in the building. The continuous technological advances in several areas such as Ambient Assisted Living and the Internet of Things (IoT) make it possible to build smart objects with significant capabilities for sensing and connecting. This paper presents the iAirCO
system, a solution for CO
real-time monitoring based on IoT architecture. The iAirCO
is composed of a hardware prototype for ambient data collection and a Web and smartphone software for data consulting. In future, it is planned that these data can be accessed by doctors in order to support medical diagnostics. Compared to other solutions, the iAirCO
is based on open-source technologies, providing a total Wi-Fi system, with several advantages such as its modularity, scalability, low-cost, and easy installation. The results reveal that the system can generate a viable IAQ appraisal, allowing to anticipate technical interventions that contribute to a healthier living environment.
We present a model-based approach to Activity Recognition (AR) in Ambient Assisted Living (AAL). The approach leverages an a priori stochastic model termed Continuous-Time Hidden Semi-Markov ...Model (CT-HSMM), capturing the continuous-time durations of activities and inter-event times. The model is enhanced according to the observed statistics, associating the events with an occurrence probability, and the sojourn time and the inter-event time in each activity with a continuous-time probability density function, allowing effective fitting of observed durations through non-Markovian distributions. The model is updated at run time according to a sequence of time-stamped observations, exploiting the method of stochastic state classes to perform transient analysis and derive a measure of likelihood that an activity is currently performed. The approach supports both online AR, predicting the activity performed at time <inline-formula><tex-math notation="LaTeX">t</tex-math></inline-formula> using only the events observed until that time, and offline AR, applying a forward-backward procedure that exploits all the events observed before and after time <inline-formula><tex-math notation="LaTeX">t</tex-math></inline-formula>. The approach is experimented on a real dataset of the literature, providing performance measures that can be compared with those of offline Hidden Markov Models (HMMs) and offline Hidden Semi-Markov Models (HSMMs).
The research area of ambient assisted living has led to the development of activity recognition systems (ARS) based on human activity recognition (HAR). These systems improve the quality of life and ...the health care of the elderly and dependent people. However, before making them available to end users, it is necessary to evaluate their performance in recognizing activities of daily living, using data set benchmarks in experimental scenarios. For that reason, the scientific community has developed and provided a huge amount of data sets for HAR. Therefore, identifying which ones to use in the evaluation process and which techniques are the most appropriate for prediction of HAR in a specific context is not a trivial task and is key to further progress in this area of research. This work presents a systematic review of the literature of the sensor-based data sets used to evaluate ARS. On the one hand, an analysis of different variables taken from indexed publications related to this field was performed. The sources of information are journals, proceedings, and books located in specialized databases. The analyzed variables characterize publications by year, database, type, quartile, country of origin, and destination, using scientometrics, which allowed identification of the data set most used by researchers. On the other hand, the descriptive and functional variables were analyzed for each of the identified data sets: occupation, annotation, approach, segmentation, representation, feature selection, balancing and addition of instances, and classifier used for recognition. This paper provides an analysis of the sensor-based data sets used in HAR to date, identifying the most appropriate dataset to evaluate ARS and the classification techniques that generate better results.
Internet of Things has been one of the catalysts in revolutionizing conventional healthcare services. With the growing society, traditional healthcare systems reach their capacity in providing ...sufficient and high-quality services. The world is facing the aging population and the inherent need for assisted-living environments for senior citizens. There is also a commitment by national healthcare organizations to increase support for personalized, integrated care to prevent and manage chronic conditions. Many applications related to In-Home Health Monitoring have been introduced over the last few decades, thanks to the advances in mobile and Internet of Things technologies and services. Such advances include improvements in optimized network architecture, indoor networks coverage, increased device reliability and performance, ultra-low device cost, low device power consumption, and improved device and network security and privacy. Current studies of in-home health monitoring systems presented many benefits including improved safety, quality of life and reduction in hospitalization and cost. However, many challenges of such a paradigm shift still exist, that need to be addressed to support scale-up and wide uptake of such systems, including technology acceptance and adoption by patients, healthcare providers and policymakers. The aim of this paper is three folds: First, review of key factors that drove the adoption and growth of the IoT-based in-home remote monitoring; Second, present the latest advances of IoT based in-home remote monitoring system architecture and key building blocks; Third, discuss future outlook and our recommendations of the in-home remote monitoring applications going forward.
•Survey on Positive Technology (PT) for elderly well-being.•Computer Vision and Machine Learning for PT.•Intelligent systems for capturing/modeling emotional, social, and cognitive human ...behaviour.•Relation between PT and Ambient-Assisted Living, GeronTechnology, and Affective Computing.
In the last decades, given the necessity of assisting fragile citizens, of which elderly represent a significant portion, a considerable research effort has been devoted to the use of information and communication technologies (ICT) in daily living to promote activity, social connections, and independence. With similar purposes, in recent years psychologists proposed the novel paradigm of Positive Psychology (PP), the scientific study of positive human functioning and flourishing on multiple levels. The joint effort between ICT and PP has led to the definition of the emerging field of Positive Technology (PT), with the aim of developing technology consciously designed to foster well-being in individuals and groups. In this paper we review PT focusing on frameworks involving computer vision and machine learning for promoting cognitive, physical, emotional and social elderly well-being. Our discussion highlights a significant gap between theoretical needs and technological systems availability, suggesting future lines of research.
Currently, many applications have emerged from the implementation of software development and hardware use, known as the Internet of things. One of the most important application areas of this type ...of technology is in health care. Various applications arise daily in order to improve the quality of life and to promote an improvement in the treatments of patients at home that suffer from different pathologies. That is why there has emerged a line of work of great interest, focused on the study and analysis of daily life activities, on the use of different data analysis techniques to identify and to help manage this type of patient. This article shows the result of the systematic review of the literature on the use of the Clustering method, which is one of the most used techniques in the analysis of unsupervised data applied to activities of daily living, as well as the description of variables of high importance as a year of publication, type of article, most used algorithms, types of dataset used, and metrics implemented. These data will allow the reader to locate the recent results of the application of this technique to a particular area of knowledge.
Home absence detection is an emerging field on smart home installations. Identifying whether or not the residents of the house are present, is important in numerous scenarios. Possible scenarios ...include but are not limited to: elderly people living alone, people suffering from dementia, home quarantine. The majority of published papers focus on either pressure/door sensors or cameras in order to detect outing events. Although the aforementioned approaches provide solid results, they are intrusive and require modifications for sensor placement. In our work, appliance electrical use is investigated as a means for detecting the presence or absence of residents. The energy use is the result of power disaggregation, a non intrusive/non invasive sensing method. Since a dataset providing energy data and ground truth for home absence is not available, artificial outing events were introduced on the UK-DALE dataset, a well known dataset for Non Intrusive Load Monitoring (NILM). Several machine learning algorithms were evaluated using the generated dataset. Benchmark results have shown that home absence detection using appliance power consumption is feasible.
•Home absence detection using appliance electricity use is feasible.•Most common machine learning algorithms provided solid results.•C4.5 trees had an overall better performance.•A dataset with appliance electric consumption and home absence ground truth is needed.
Location-based services have increased in popularity in recent years and can be fruitfully exploited in the field of smart homes, opening the doors to a wide range of personalized services. In this ...context, radio technology can be widely employed since, other than connecting devices in the home system, it offers solutions for the user localization issue without the need of any extra device. Techniques based on received signal strength indicator (RSSI) are often used, relying on fingerprinting or proximity algorithms. In this paper, a novel RSSI-based fingerprinting approach for room-level localization is presented: it is a threshold algorithm based on receiver operating characteristic analysis. Moreover, the actual user location is estimated from his/her interaction with the home system devices deployed in the house: if the home environment is inhabited by more than one person, it becomes of utmost importance the identification of who is actually interacting with a given device. A proximity method is exploited for this purpose. Tests have been carried out to characterize the approach, particularly, the effects of RSSI samples, number and position, of the anchor nodes have been analyzed. Finally, some considerations about power consumption of the mobile node have been presented.
Physical and cognitive impairments decline the ability of elderly in execution of daily activities, such as eating, sleeping or taking medication. The proposed approach recognizes the activities ...performed in a smart home, and separates the normal from the anomalous activities. Moreover, we identify the anomalous days based on the number of activities performed in a day. We perform activity recognition by applying probabilistic neural network on the pre-segmented activity-data obtained from the sensors deployed at different locations in a smart home. We use H2O autoencoder to identify the anomalous from the normal instances of activities. We further categorize the anomalies based on the criteria such as missing or extra subevents, and unusual duration of activity. Since the ground truth of the anomalies is unavailable, we generate the ground truth using the boxplots of the duration, and the number of subevents in an activity. We provide the quantified results of activity recognition and anomaly detection that can be further used by the research community. A comprehensive evaluation of the proposed approach on two publicly available CASAS smart home datasets demonstrates its ability in the activity recognition and the correct identification of anomalies.
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