Ambient Assisted Living Systems Research Articles

Technological progression associated with monitoring and management of indoor air pollution and associated health risks: A comprehensive review

AbstractIndoor air pollution (IAP) is a prevalent issue, and in the absence of any concrete and stringent guidelines, particularly in developing countries the concern becomes graver. Technological strategies like the use of the Internet of Things (IoT) and cloud computing have been explored for real‐time monitoring and these interventions may be investigated to improve indoor air quality (IAQ) and human health. This review article explores the prospects of IoT and smart environments for the improvement of indoor living conditions through automation. Some specific interference like sensors, air pollution simulations and modeling, the concept of smart ventilation, and fuzzy logic controllers (FLC) have been elaborated with pieces of evidence taken from previously published studies in leading indexing databases. Furthermore, some automated health risk assessment tools like the Human exposure model (HEM), Integrated Fuzzy‐stochastic modeling (IFSM), and proximity and interpolation models have also been described. The findings suggested that IoT‐based gadgets require ambient intelligence capabilities for ambient assisted living (AAL). The studies showed that innovations in technology like sensors and modeling techniques may yield crucial information on pollution exposure enabling long‐term and sustainable predictions. However, efficient AAL systems may also face challenges in designing interfaces, usability, and accessibility. Although IoT can aid in mimicking real‐world scenarios, nevertheless its‐micro spatial scale application requires thorough investigation for reliable information extraction.

Organized leader election consensus protocol and a supportive blockchain model for ambient assisted living

Blockchain enhances the development of other technologies and systems by providing more transparency, sharing, decentralization, security, and privacy. Blockchain has many applications that enable integration and application in many areas, such as the field of ambient assisted living (AAL). However, blockchain includes many processes for data proposing, validating, and approving, which result in high latency. Such latency negatively influences AAL systems, which require real-time responses in many cases. In addition, blockchain processes are processed based on consensus. This challenges specialized decisions that should be taken only by specialized members within the blockchain network. Therefore, this work introduces an organized leader election consensus protocol with a blockchain model that is applicable for AAL. The proposed protocol and model are able to solve the issue of latency as well as consider specialized decisions. Solving the issue of latency enables AAL to perform real-time processes in the needed cases. While considering the matter of specialization gives more credibility to the blockchain’s decisions, which is necessary for AAL. Indeed, it classifies the blocks into special-sensitive, time-sensitive, and non-sensitive classes, where each class has a suitable procedure. In addition, to manage the organized leader election consensus protocol, we manage the blockchain feature and rewarding strategy. This enhances the miners’ participation in a way that supports blockchain and AAL simultaneously. The experimental results show that our model reduces the execution time by about 51% in comparison to the original blockchain model.

Healthy and Happy? An Ethical Investigation of Emotion Recognition and Regulation Technologies (ERR) within Ambient Assisted Living (AAL)

Ambient Assisted Living (AAL) refers to technologies that track daily activities of persons in need of care to enhance their autonomy and minimise their need for assistance. New technological developments show an increasing effort to integrate automated emotion recognition and regulation (ERR) into AAL systems. These technologies aim to recognise emotions via different sensors and, eventually, to regulate emotions defined as “negative” via different forms of intervention. Although these technologies are already implemented in other areas, AAL stands out by its tendency to enable an inconspicuous 24-hour surveillance in the private living space of users who rely on the technology to maintain a certain degree of independence in their daily activities. The combination of both technologies represents a new dimension of emotion recognition in a potentially vulnerable group of users. Our paper aims to provide an ethical contextualisation of the novel combination of both technologies. We discuss different concepts of emotions, namely Basic Emotion Theory (BET) and the Circumplex Model of Affect (CMA), that form the basis of ERR and provide an overview over the current technological developments in AAL. We highlight four ethical issues that specifically arise in the context of ERR in AAL systems, namely concerns regarding (1) the reductionist view of emotions, (2) solutionism as an underlying assumption of these technologies, (3) the privacy and autonomy of users and their emotions, (4) the tendency of machine learning techniques to normalise and generalise human behaviour and emotional reactions.

Deep edge intelligence-based solution for heart failure prediction in ambient assisted living

Heart failure and heart disease prediction in real-time is a highly significant necessity for the patients living under the observation of Internet of Things-based Ambient Assisted Living systems because cardiovascular diseases are the most common fatal chronic diseases. Most of the solutions regarding heart disease prediction in the Internet of Things-based medical systems are relying on server-based predictive analysis which can appear to be complex for generating real-time prediction notifications and unreliable in case of any network interruption occurrences. The suggested edge-based solution for the prediction of heart disease from collected sensor data in real-time using a proposed lightweight deep learning technique called Oversampled Quinary Feed Forward Network (OQFFN) provides a less complex framework and more reliable notification system in case of network failure for the disease prediction which also reduces the need of forwarding all the data to the server resulting in reduced network bottleneck.

An Efficient and Secure Authentication for Ambient Assisted Living System

Although the birthrate is declining, the average life expectancy continues to increase. Therefore, it is more important for elderly people to maintain their independence while staying at home. Ambient Assisted Living (AAL) includes the use of devices and methods of ensuring that elderly people can stay safe and age at home rather than at a facility. Assisted living services help people live as independently and safely as possible when they can no longer perform everyday activities on their own. Because the information transmitted in AAL systems is personal, the security and privacy of such data are becoming important issues that must be addressed. Herein, we propose an efficient and secure authentication scheme for an AAL system. Our proposed authentication scheme not only satisfies several important security requirements of such a system but also withstands various types of attacks. Moreover, the proposed authentication scheme achieves lightweight performance by manipulating basic cryptographic operations including bitwise-eXclusive-OR (XOR) and hash functions. We simulated our proposed authentication scheme using Automated Validation of Internet Security Protocols and Applications (AVISPA), which is a prominent security verification tool. Security and performance analysis show that our proposed scheme is not only robust against several attacks and has a lower computational cost in terms of execution time than those of existing authentication schemes.

Design of a Kitchen-Monitoring and Decision-Making System to Support AAL Applications.

Numerous researchers are working on Ambient Assisted Living systems to enable more comfortable and safer living for senior people in their homes. Due to modern lifestyles and an aging population, this has become a very important issue. According to the available literature, it is obvious that the kitchen is one of the most important and most dangerous rooms in the home. However, there is still evident lack of monitoring systems suitable for specific kitchen activities. In this paper, we propose a monitoring system capable of identifying activities related to the cooking process, and a decision-making system capable of identifying some unwanted and possibly critical conditions. The proposed systems are designed to satisfy the requirements of the modern Ambient Assisted Living systems dedicated to older adults. The proposed monitoring system consists of ultrasound, temperature, and humidity sensors. The acquired results from these sensors are the inputs for the decision-making system, which generate warnings or alarms intended for the senior users and/or formal or informal caregivers. This system is designed to improve home safety related to kitchen activities, as well as to provide information about the lifestyle and daily activities of senior users. Experimental validation of the proposed system confirms its functionality and accurate design approach.

FriendCare-AAL: a robust social IoT based alert generation system for ambient assisted living

The use of advanced communication technologies such as Internet of Things (IoT) in the domain of Ambient Assisted Living (AAL) tends to promote the quality of living for elderly staying independently. However, the state of the art IoT based solutions for AAL systems have not fully expressed the importance of building social connections between smart devices. This paper attempts to study the significance of deploying socially enabled IoT systems in AAL environment by proposing a robust Social IoT based AAL system for elderly named FriendCare-AAL. In addition, it presents a schematic approach to establish a partnership among smart devices and introduces the concept of responsibility offloading between devices. The proposed system is capable of providing assistance to the elderly staying in smart home environment. In case of emergency, the system automatically generates alerts intimating about the situation to the concerned entities. To experimentally evaluate the system’s performance, a smart home AAL environment for an elderly person is simulated using human activity simulator namely ‘Home Sensor Simulator’ and person’s routine dataset is generated. Further, two machine learning models; Naive Bayes (NB) and Random Forest (RF) are employed to analyze the data in order to predict the well being of the elderly person. The performance of the two classifiers is assessed using metrics such as sensitivity, specificity, detection rate and accuracy. Experimental results revealed that RF classifier outperforms NB classifier in terms of overall accuracy, detection rate and balanced accuracy. The overall accuracy is observed to be 89.2% for RF and 83.9% for NB classifier. Furthermore, a performance comparison of the proposed model is performed with two baseline approaches. A system prototype is also developed using Node-Red simulation tool to determine the performance of the proposed system in real-world and failure-prone environments. It turns out that the system performs well in critical situations with a tolerable response time of less than 1.2 s for a high failure rate of upto 50%.

An argumentation enabled decision making approach for Fall Activity Recognition in Social IoT based Ambient Assisted Living systems

With the advancement in Information and Communication Technologies (ICTs), smart devices are becoming even more smart and intelligent with every passing day. Further, the evolution of speaking and hearing enabled devices in an IoT network is transforming the face of research in the Social IoT domain. However, the integration of argumentation enabled devices in Social IoT network has not been fully explored by researchers in the past. Therefore, this research work focuses on development of argument enabled Social IoT networks. In this paper, a fuzzy argument based classification scheme termed as Classification Enhanced with Fuzzy Argumentation (CleFAR) is proposed. The proposed scheme is deployed for classification of fall activities in fall prevention applications. A novel framework for fall prevention system using Fall Activity Recognition (FAR) is presented. The proposed system is designed for the purpose of fall activity recognition in smart home Ambient Assisted Living (AAL) systems. To experimentally evaluate the system’s performance, a smart home AAL environment is simulated and the inhabitant’s routine activity dataset is generated. The fall activities are simulated using wearable fall detection systems. The proposed scheme is trained and tested on generated datasets and its performance is compared with traditional classification algorithms such as Random Forest (RF), Support Vector Machines (SVM), Naive Bayes (NB), Decision Tree (DT) and Artificial Neural Networks (ANN) as well as existing argumentation based game theoretic Weighted Voting Scheme (WVS). Experimental results indicate that the proposed scheme outperforms the traditional classification schemes and WVS approach with prediction accuracy up to 91%. It turns out that the proposed approach achieves significant improvement over the existing schemes.

Evaluating the Impact of Different Symmetrical Models of Ambient Assisted Living Systems

In recent years, numerous attempts have been made to enhance the living standard for old-aged people. Ambient Assisted Living (AAL) is an evolving interdisciplinary field aimed at the exploitation of knowledge and communication technology in health and tele-monitoring systems to combat the impact of the growing aging population. AAL systems are designed for customized, responsive, and predictive requirements, requiring high performance of functionality to ensure interoperability, accessibility, security, and consistency. Standardization, continuity, and assistance of system development have become an urgent necessity to meet the increasing needs for sustainable systems. In this article, we examine and address the methods of the different AAL systems. In addition, we analyzed the acceptance criteria of the AAL framework intending to define and evaluate different AAL-based symmetrical models, leveraging performance characteristics under the integrated fuzzy environment. The main goal is to provide an understanding of the current situation of the AAL-oriented setups. Our vision is to investigate and evaluate the potential symmetrical models of AAL systems and frameworks for the implementation of effective new installations.

A human-centered artificial intelligence approach for privacy protection of elderly App users in smart cities

Artificial Intelligence and Machine Learning based Ambient Assisted Living systems play an important role in smart cities by improving the quality of life of the elderly population. Many Ambient Assisted Living systems are coupled with Android Apps for command-and-control purposes. Consequently, the privacy and security of Ambient Assisted Living systems depend on the privacy and security of the corresponding Android Apps, which follow a privacy self-management model. Unfortunately, the privacy self-management model ignores the decision-making abilities of the elderly and increases their cognitive loads, which put their privacy protection and wellbeing at stake. In this paper, we follow a Human-Centered Artificial Intelligence inspired approach for addressing these issues. This approach uses privacy as a shared responsibility model instead of the privacy self-management model. We have proposed two algorithms, the participatory privacy protection algorithm-I, and participatory privacy protection algorithm-II, for determining optimal privacy settings of an Ambient Assisted Living App and handling its runtime Permission requests, respectively. We demonstrated the working of these algorithms using a case study. We have also compared the proposed approach with state-of-the-art privacy management schemes for Android Apps. The proposed algorithms can improve the privacy protection of Ambient Assisted Living App users in smart cities and relieve them through cognitive offloading.

Automatic Classification Error Detection and Correction for Robust Human Activity Recognition

One of the main objectives of Ambient Assisted Living (AAL) systems is to proactively provide intelligent services to improve the quality of people's lives in terms of autonomy, safety, and well-being. Designing AAL systems that can autonomously monitor human's activities and provide assistance services poses several challenges of which Human Activity Recognition (HAR) which is critically important to adapt the assistance services to the user. In this letter, a robust multi-label HAR framework is proposed. The proposed framework is composed of two main modules: (i) activity classification module and (ii) classification error detection and correction module. In the first module, machine-learning models are used to predict human activities. Since these models may produce predictions with errors, there is a requirement to detect and correct these errors. The classification error detection and correction module is based on two acyclic directed graphical models and operates in two phases: (i) classification error detection and (ii) classification error correction. The proposed framework is evaluated on the Opportunity dataset , a benchmark and a unique dataset for multi-label human daily living activity recognition. The obtained results demonstrate the ability of the proposed framework to improve the performances of HAR.

An innovative AAL system based on neural networks and IoT-aware technologies to improve the quality of life in elderly people

Nowadays more and more elderly people need support in daily activities. This is due to the increase of cognitive diseases and other conditions which lead the elderly to not being self-sufficient. Considering this, providing an Ambient Assisted Living system could improve significantly people life quality and could support caregivers' tasks. The combination of Ambient Assisted Living systems and information and communication technologies achieve this purpose perfectly. They exploit internet of things and artificial intelligence paradigms to make daily challenges easier for people with neurodegenerative diseases. This work melds technologies mentioned above providing a smart system for elderly to manage goods and fill in shopping lists. It was possible using software, hardware, and cloud systems combined with a neural network aimed to recognise products. The proposed system has been validated both from a functional point of view through a proof-of-concept and quantitatively by a performance analysis of its components.

Assessment of Reference Architectures and Reference Models for Ambient Assisted Living Systems

The innovation and development of software systems in the Ambient Assisted Living (AAL) domain have brought huge challenges for academia and software industry as well. Despite the existence of architectural models that can be used as references to build AAL systems, their selection for new AAL projects is a difficult task. In this work, the authors present the state of the art on Reference Architectures (RA) and Reference Models (RM) found through the conduction of a systematic literature review. The authors identified, analyzed, and assessed 24 existing RA&RM for AAL domain, and, as result, the authors spotted interesting research directions that should be further explored to improve existing and future RA&RM and software systems for that domain.

Framework-Oriented Approach to Ease the Development of Ambient Assisted-Living Systems

Ambient assisted-living (AAL) systems, including wearables and home automation, could improve the quality of life of fragile people. As analyzed in this paper, the typical architecture of AAL systems is based on wireless sensors networks and proprietary gateways and it can be complex, expensive, and hardly includes interoperable multivendor devices. Emerging Android-based gateways can help in reducing costs, but flexibility and interoperability issues will still remain. On the basis of this fact, the development of an AAL system by a framework-oriented approach is presented. Thanks to the use of the SAndroidE framework, the proposed approach leads to the reduction of the programming effort as well as improves interoperability among different vendors ensuring high customization levels. Results obtained in a typical use case indicate that the programming effort in integrating multivendor devices is reduced by a factor of ten. The presented approach paves the way to the implementation of new open multivendor AAL systems, also encouraging redundant architectures and improving robustness of these emerging solutions.

A Fuzzy Logic-Based Method for Evaluating AAL Systems

The Ambient Assisted Living (AAL) domain aims to support the daily life activities of elders, patients with chronic conditions, and disabled people. Several AAL platforms have been developed over the last two decades. Hence, there is a need to identify Quality Criteria (QC) and make it well defined in order to achieve the AAL system purposes. To be able to convince all stakeholders including both technologies and end users of AAL systems, high quality must be guaranteed. The goal of this article is to obtain a set of data quality characteristics that would be applicable to AAL system, and have its performance evaluated using sensors' data. To this end, this work uses the ISO/IEC 25012 and ISO/IEC 25010 standards to extract the most relevant criteria that are apt for AAL systems. As a result, an evaluation approach on an indoor localization platform was made, and an evaluation procedure has been established. This is done by first generating a hierarchical data quality model, and have it evaluated using the metrics, based on the sensors data and the concept of fuzzy logic.

Enhancing the Ambient Assisted Living Capabilities with a Mobile Robot.

Ambient assisted living (AAL) environments are currently a key focus of interest as an option to assist and monitor disabled and elderly people. These systems can improve their quality of life and personal autonomy by detecting events such as entering potentially dangerous areas, potential fall events, or extended stays in the same place. Nonetheless, there are areas that remain outside the scope of AAL systems due to the placement of cameras. There also exist sources of danger in the scope of the camera that the AAL system cannot detect. These sources of danger are relatively small in size, occluded, or nonstatic. To solve this problem, we propose the inclusion of a robot which maps such uncovered areas looking for new potentially dangerous areas that go unnoticed by the AAL. The robot then sends this information to the AAL system in order to improve its performance. Experimentation in real-life scenarios successfully validates our approach.

SmartWall: Novel RFID-Enabled Ambient Human Activity Recognition Using Machine Learning for Unobtrusive Health Monitoring

Human activity recognition (HAR) from sensor readings has proved to be an effective approach in pervasive computing for smart healthcare. Recent approaches in ambient assisted living (AAL) within the home or community setting offers people the prospect of independent care and improved quality of living. However, most of the available AAL systems are limited by several factors including the system complexity and computational cost. In this paper, a simple, the novel ambient HAR framework using the multivariate Gaussian is proposed. The classification framework augments prior information from passive RFID tags to obtain more detailed activity profiling. The proposed algorithm based on the multivariate Gaussian via maximum likelihood estimation is used to learn the features of the human activity model. The twelve sequential and concurrent experimental evaluations are conducted in a mock apartment environment. The sampled activities are predicted using a new dataset of the same activity and high prediction accuracy established. The proposed framework suits well for the single and multi-dwelling environment and offers pervasive sensing environment for elderly, disabled, and carers.

Online activity recognition and daily habit modeling for solitary elderly through indoor position-based stigmergy

This paper concerns the issue of monitoring elderly behavior in the context of ambient assisted living (AAL). Under the framework of online daily habit modeling (ODHM), we employ the emergent representation for activities of daily living (ADLs) with position-based stigmergy, and then combine it with convolution neural networks (CNNs) to accomplish the tasks of recognizing ADLs. In addition, we propose a new paradigm of activity summarization with the robustness to break interruptions. Radio tomographic imaging (RTI) is promoted as a simple yet flexible way of facilitating the required position-based stigmergy. Such position-based AAL systems can benefit the advantages of having no need any sophisticated domain models in analyzing and understanding ADLs while no burden training is involved in ODHM. Moreover, the emergent based data aggregation and deep learning of CNN together allow the recognition of ADLs at a fine-grained level, which contributes to the performance improvement of ODHM. Experimental results demonstrate the effectiveness of the proposed approach.

They Don't Care About Us! Care Personnel's Perspectives on Ambient Assisted Living Technology Usage: Scenario-Based Survey Study.

BackgroundDemographic change represents enormous burdens for the care sectors, resulting in high proportions of (older) people in need of care and a lack of care staff. Ambient Assisted Living (AAL) technologies have the potential to support the bottlenecks in care supply but are not yet in widespread use in professional care contexts.ObjectiveThe objective of our study was to investigate professional caregivers’ AAL technology acceptance and their perception regarding specific technologies, data handling, perceived benefits, and barriers. In particular, this study focuses on the perspectives on AAL technologies differing between care professionals working in diverse care contexts to examine the extent to which the care context influences the acceptance of assistive technologies.MethodsA Web-based survey (N=170) was carried out focusing on professional caregivers including medical, geriatric, and disabled people’s caregivers. Based on a scenario, the participants were asked for their perceptions concerning specific technologies, specific types of gathered data, and potential benefits of and barriers to AAL technology usage.ResultsThe care context significantly impacted the evaluations of AAL technologies (F14,220=2.514; P=.002). Professional caregivers of disabled people had a significantly more critical attitude toward AAL technologies than medical and geriatric caregivers, indicated (1) by being the only caregiver group that rejected evaluations of AAL technology acceptance (F2,118=4.570; P=.01) and specific technologies (F2,118=11.727; P<.001) applied for gathering data and (2) by the comparatively lowest agreements referring to the evaluations of data types (F2,118=4.073, P=.02) that are allowed to be gathered.ConclusionsAAL technology acceptance is critical because of technology implementation reasons, especially in the care of people with disabilities. AAL technologies in care contexts have to be tailored to care professional’s needs and concerns (“care about us”). The results contribute to a broader understanding of professional caregivers’ needs referring to specific data and technology configurations and enclose major differences concerning diverse care contexts. Integrating these findings into user group-tailored technology concepts and communication strategies will support a sustainable adoption of AAL systems in professional care contexts.

A Review and Classification of Assisted Living Systems

Europe’s social agenda for the “active elderly” is based upon a series of programs that provide a flexible infrastructure for their lives so that they are motivated, engaged in lifelong learning, and contributing to society. Economically speaking, Europe must engage in active aging research in order to avoid unsustainable health costs, and ambient assisted living (AAL) systems provide a platform for the elderly to remain living independently. This paper reviews research conducted within the area of AAL, and offers a taxonomy within which such systems may be classified. This classification distinguishes itself from others in that it categorises AAL systems in a top-down fashion, with the most important categories placed immediately to the left. In this paper, each section is explored further, and AAL systems are the focus. Entire AAL systems still cannot be fully evaluated, but their constituent technical parts can be assessed. The activities of daily living (ADLs) component was given further priority due to its potential for system evaluation, based on its ability to recognise ADLs with reasonable accuracy.