Ambient Assisted Living System Research Articles

A Decision-Aware Ambient Assisted Living System with IoT Embedded Device for In-Home Monitoring of Older Adults

Older adults' independent life is compromised due to various problems, such as memory impairments and decision-making difficulties. This work initially proposes an integrated conceptual model for assisted living systems capable of providing helping means for older adults with mild memory impairments and their caregivers. The proposed model has four main components: (1) an indoor location and heading measurement unit in the local fog layer, (2) an augmented reality (AR) application to make interactions with the user, (3) an IoT-based fuzzy decision-making system to handle the direct and environmental interactions with the user, and (4) a user interface for caregivers to monitor the situation in real time and send reminders once required. Then, a preliminary proof-of-concept implementation is performed to evaluate the suggested mode's feasibility. Functional experiments are carried out based on various factual scenarios, which validate the effectiveness of the proposed approach. The accuracy and response time of the proposed proof-of-concept system are further examined. The results suggest that implementing such a system is feasible and has the potential to promote assisted living. The suggested system has the potential to promote scalable and customizable assisted living systems to reduce the challenges of independent living for older adults.

A Simplistic and Cost-Effective Design for Real-World Development of an Ambient Assisted Living System for Fall Detection and Indoor Localization: Proof-of-Concept

Falls, highly common in the constantly increasing global aging population, can have a variety of negative effects on their health, well-being, and quality of life, including restricting their capabilities to conduct activities of daily living (ADLs), which are crucial for one’s sustenance. Timely assistance during falls is highly necessary, which involves tracking the indoor location of the elderly during their diverse navigational patterns associated with different activities to detect the precise location of a fall. With the decreasing caregiver population on a global scale, it is important that the future of intelligent living environments can detect falls during ADLs while being able to track the indoor location of the elderly in the real world. Prior works in these fields have several limitations, such as the lack of functionalities to detect falls and indoor locations in a simultaneous manner, high cost of implementation, complicated design, the requirement of multiple hardware components for deployment, and the necessity to develop new hardware for implementation, which make the wide-scale deployment of such technologies challenging. To address these challenges, this work proposes a cost-effective and simplistic design paradigm for an ambient assisted living system that can capture multimodal components of user behaviors during ADLs that are necessary for performing fall detection and indoor localization in a simultaneous manner in the real-world. Proof-of-concept results from real-world experiments are presented to uphold the effective working of the system. The findings from two comparative studies with prior works in this field are also presented to uphold the novelty of this work. The first comparative study shows how the proposed system outperforms prior works in the areas of indoor localization and fall detection in terms of the effectiveness of its software design and hardware design. The second comparative study shows that the cost of the development of this system is the lowest as compared to prior works in these fields, which involved real-world development of the underlining systems, thereby upholding its cost-effective nature.

Ray‐tracing assisted fingerprinting for localization in IoT Health 4.0

AbstractThe Internet of Things (IoT) can help healthcare companies establish Ambient Assisted Living (AAL). It permits innovative solutions to a well‐known problem, notably the real‐time localization and monitoring of patients and clinicians. Yet, there is no method for indoor localization in the healthcare context that delivers centimeter‐level precision. Although radio‐based localization has intriguing potential, the primary limitations of such a method are non‐line‐of‐sight radio signal transmission and multipath reflection, both of which lead to the lowest accuracy. The advanced 5G communication technology allows for the simultaneous utilization of temporal and angular features of a broadcast signal, as well as its multipath property, to accomplish precise target localization. This article presents a method for multipath channel‐assisted localization in the AAL system by utilizing the temporal and angular measurement of the signal concurrently. We simulated and tested the localization approach in an AAL system and achieved centimeter‐level accuracy over 90% of the time in all four cases.

OntoDomus: A Semantic Model for Ambient Assisted Living System Based on Smart Homes

Ambient assisted living (AAL) makes it possible to build assistance for older adults according to the person’s context. Understanding the person’s context sometimes involves transforming one’s home into a smart home. Typically, this is carried out using nonintrusively distributed sensors and calm technologies. Older adults often have difficulty performing activities of daily living, such as taking medication, drinking coffee, watching television, using certain electronic devices, and dressing. This difficulty is even greater when these older adults suffer from cognitive impairments. Defining an assistance solution requires a multidisciplinary and iterative collaborative approach. It is necessary, therefore, to reason about the imperatives and solutions of this multidisciplinary collaboration (e.g., clinical), as well as the adaptation of technical constraints (e.g., technologies). A common approach to reasoning is to represent knowledge using logic-based formalisms, such as ontologies. However, there is not yet an established ontology that defines concepts such as multidisciplinary collaboration in successive stages of the assistance process. This article presents OntoDomus, an ontology that describes, at several levels, the semantic interactions between ambient assisted living, context awareness, smart home, and Internet of Things, based on multidisciplinarity. It revolves around two main notions: multidisciplinarity, based on specific sub-ontologies and the ambient feedback loop. OntoDomus combines SPARQL queries and OWL 2 models to improve the reusability of domain terminology, allowing stakeholders to represent their knowledge in different collaborative and adaptive situations. The ontological model is validated, first by its reuse in more specific works—specific to an aspect of ambient assistance. Second, it is validated by the structuring of ambient knowledge and inferences of the formalization in a case study that includes instances for a particular activity of daily living. It places the ambient feedback loop at the center of the ontology by focusing on highly expressive domain ontology formalisms with a low level of expressiveness between them.

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.

A Simplistic and Cost-Effective Design for Real-World Development of an Ambient Assisted Living System for Fall Detection and Indoor Localization: Proof of Concept

A Simplistic and Cost-Effective Design for Real-World Development of an Ambient Assisted Living System for Fall Detection and Indoor Localization: Proof of Concept

What Characterizes Safety of Ambient Assisted Living Technologies?

Ambient assisted living (AAL) technologies aim at increasing an individual's safety at home by early recognizing risks or events that might otherwise harm the individual. A clear definition of safety in the context of AAL is still missing and facets of safety still have to be shaped. The objective of this paper is to characterize the facets of AAL-related safety, to identify opportunities and challenges of AAL regarding safety and to identify open research issues in this context. Papers reporting aspects of AAL-related safety were selected in a literature search. Out of 395 citations retrieved, 28 studies were included in the current review. Two main facets of safety were identified: user safety and system safety. System safety concerns an AAL system's reliability, correctness and data quality. User safety reflects impact on physical and mental health of an individual. Privacy, data safety and security issues, sensor quality and integration of sensor data, as well as technical failures of sensors and systems are reported challenges. To conclude, there is a research gap regarding methods and metrics for measuring user and system safety in the context of AAL technologies.

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%.

Involving Stakeholders in the Implementation of Microservice-Based Systems: A Case Study in an Ambient-Assisted Living System

Microservice-based systems promote agility and rapid business development. Some features, such as fast time-to-market, scalability and optimal response times, have encouraged stakeholders to get more involved in the development and implementation of microservices architectures in order to translate their business vision into the implementation of the architecture. Although some techniques allow the inclusion of the stakeholders' perspective in the design of microservice architectures, few proposals consider such perspectives in the selection and evaluation of technologies that implement microservice architectures. Indeed, the qualities that characterize microservice-based systems strongly depend on the suitable selection of technologies, such as application frameworks and platforms. This article proposes a collaborative technique that includes stakeholders and software architects in the selection and evaluation of application frameworks and platforms to implement microservice-based systems. We evaluated the technique in an industrial case of design and implementation of an Ambient-Assisted Living (AAL) system, which combines microservice architecture and Internet-of-Medical-Things (IoMT) sensors. The case results indicate that the proposed technique supported stakeholders in the pragmatic evaluation of alternative technological solutions. Additionally, it allowed the implementation of an AAL system that satisfies the quality specifications of stakeholders and end-users. This initial study suggests that actively including stakeholders in the implementation of microservice-based systems allows architects to make design decisions that better consider stakeholders viewpoints as well as managing their expectations.

Sensor Failure Detection in Ambient Assisted Living Using Association Rule Mining.

Ambient Assisted Living (AAL) is becoming crucial to help governments face the consequences of the emerging ageing population. It aims to motivate independent living of older adults at their place of residence by monitoring their activities in an unobtrusive way. However, challenges are still faced to develop a practical AAL system. One of those challenges is detecting failures in non-intrusive sensors in the presence of the non-deterministic human behaviour. This paper proposes sensor failure detection and isolation system in the AAL environments equipped with event-driven, ambient binary sensors. Association Rule mining is used to extract fault-free correlations between sensors during the nominal behaviour of the resident. Pruning is then applied to obtain a non-redundant set of rules that captures the strongest correlations between sensors. The pruned rules are then monitored in real-time to update the health status of each sensor according to the satisfaction and/or unsatisfaction of rules. A sensor is flagged as faulty when its health status falls below a certain threshold. The results show that detection and isolation of sensors using the proposed method could be achieved using unlabelled datasets and without prior knowledge of the sensors’ topology.

Regularized K-SVD-Based Dictionary Learning Approaches for PIR Sensor-Based Detection of Human Movement Direction

The present work shows how a novel solution using regularization concept based K-SVD dictionary learning (DL) can be proposed for passive infrared (PIR) sensor based ambient assisted living (AAL) technologies. In this work, the AAL system focuses on detecting any human movement in specific directions in an unmanned environment. The regularization concept punishes solutions with large values of sparse representation coefficients and this work has successfully implemented regularized K-SVD (RK-SVD) and regularized approximated K-SVD (RAK-SVD) algorithms for a low cost hardware-software based intelligent AAL system, indigenously developed in our laboratory. This work also proposes modified versions of both algorithms (named MRK-SVD and MRAK-SVD algorithms) where novel methods of adapting the regularization parameter have been introduced. Extensive experimentations established that these approaches could significantly improve upon performances of DL based state-of-the-art approaches known, e.g. using multiple cluster pursuit (MCP) approaches, with MRAK-SVD algorithm emerging as the best alternative.

Using Ambient Assisted Living to Monitor Older Adults With Alzheimer Disease: Single-Case Study to Validate the Monitoring Report.

BackgroundMany older adults choose to live independently in their homes for as long as possible, despite psychosocial and medical conditions that compromise their independence in daily living and safety. Faced with unprecedented challenges in allocating resources, home care administrators are increasingly open to using monitoring technologies known as ambient assisted living (AAL) to better support care recipients. To be effective, these technologies should be able to report clinically relevant changes to support decision making at an individual level.ObjectiveThe aim of this study is to examine the concurrent validity of AAL monitoring reports and information gathered by care professionals using triangulation.MethodsThis longitudinal single-case study spans over 490 days of monitoring a 90-year-old woman with Alzheimer disease receiving support from local health care services. A clinical nurse in charge of her health and social care was interviewed 3 times during the project. Linear mixed models for repeated measures were used to analyze each daily activity (ie, sleep, outing activities, periods of low mobility, cooking-related activities, hygiene-related activities). Significant changes observed in data from monitoring reports were compared with information gathered by the care professional to explore concurrent validity.ResultsOver time, the monitoring reports showed evolving trends in the care recipient’s daily activities. Significant activity changes occurred over time regarding sleep, outings, cooking, mobility, and hygiene-related activities. Although the nurse observed some trends, the monitoring reports highlighted information that the nurse had not yet identified. Most trends detected in the monitoring reports were consistent with the clinical information gathered by the nurse. In addition, the AAL system detected changes in daily trends following an intervention specific to meal preparation.ConclusionsOverall, trends identified by AAL monitoring are consistent with clinical reports. They help answer the nurse’s questions and help the nurse develop interventions to maintain the care recipient at home. These findings suggest the vast potential of AAL technologies to support health care services and aging in place by providing valid and clinically relevant information over time regarding activities of daily living. Such data are essential when other sources yield incomplete information for decision making.

Contactless Interactive Fall Detection and Sleep Quality Estimation for Supporting Elderly with Incipient Dementia

Abstract In recent years, the demographic change in conjunction with a lack of professional caregivers led to retirement homes reaching capacity. The Alzheimer Disease International stated that over 50 million people suffered from dementia in 2019 worldwide and twice the amount will presumably be effected in 2030. The field of Ambient Assisted Living (AAL) tackles this problem by facilitating technical system-aided everyday life. AUXILIA is such an AAL system and does not only support elderly people with dementia in an early phase, but also monitors their activities to provide behaviour analysis results for care attendants, relatives and physicians. Moreover, the system is capable of recognizing emergency situations like human falls. Furthermore, sleep quality estimation is employed to be able to draw conclusions about the current behaviour of an affected person. This article presents the current development state of AUXILIA.

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.

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.

An Innovative AAL System Based on IoT Technologies for Patients with Sarcopenia.

Sarcopenia is a highly prevalent, age-related muscle disorder associated with adverse outcomes. It is very important from a medical point of view to periodically monitor patients at risk of developing sarcopenia in order to early detect its onset or progression through objective and specific indicators. Today, the emerging Internet of Things (IoT)-enabling technologies allow us to create innovative, wearable, and non-invasive systems that can offer useful clinical support in this area. This work is focused on the use of combined hardware and software technologies, enabling the IoT, in order to monitor people suffering from sarcopenia by offering a high value-added service in the field of the Ambient Assisted Living (AAL). In addition to the description of the proposed system architecture, a validation of the entire system is also included, from both a performance and a functional point of view. Test beds have been carried out by using the independent replications method, and all measurements related to the identified sarcopenia parameters are characterized by a 95% confidence interval with a 5% maximum relative error. The implementation of these technologies as a supporting clinical tool used in a specific setting could significantly impact the life and independence of the sarcopenic frail elderly population.

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.

Nothing Else Matters! Trade-Offs Between Perceived Benefits and Barriers of AAL Technology Usage.

Ambient assisted living (AAL) technologies present one approach to counter the challenges of demographic change in terms of an aging population, rising care needs, and lacking care personnel by supporting (older) people in need of care and enabling a longer and more independent staying at own home. Although the number of studies focusing on AAL users' acceptance and perception has increased in the last years, trade-off decisions—the weighing of perceived benefits and barriers of technology usage—have not been studied so far. Nevertheless, this is of high relevance: A realistic evaluation of adoption behaviors in different stakeholders (patients, care personnel) requires an understanding of exactly the weighing process of benefits against the barriers in line with the decision of the final willingness to use AAL technology. The current study applied a conjoint analysis approach and investigates people's decision behavior to use an AAL system for a family member in need of care. Study participants (n = 140) had to decide between realistic care scenarios consisting of different options of two benefits (increase in safety, relief of caring burden of relatives) and two barriers (access to personal data and data handling) of technology usage. Results revealed data access and privacy to be most relevant for the decision to use AAL technology at home. However, care experience essentially influenced the decision patterns. For the care experienced group, data access should be limited to most trusted persons and close relatives, rather than to medical professionals. The most important reasons to use AAL are the emotional relief and the felt safety for the person in care. For care novices, in contrast, data access should be in the exclusive responsibility of medical professionals. The reasons that militate in favor of using AAL technology are the increase in process efficiency and medical safety. The results are useful to develop user-tailored technology concepts and derive user-specific communication guidelines within and across clinical and home care contexts.

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.

Democratization of runtime verification for internet of things

Internet of Things (IoT) devices have gained more prevalence in ambient assisted living (AAL) systems. Reliability of AAL systems is critical especially in assuring the safety and well-being of elderly people. Runtime verification (RV) is described as checking whether the observed behavior of a system conforms to its expected behavior. RV techniques generally involve heavy formal methods; thus, it is poorly utilized in the industry. Therefore, we propose a democratization of RV for IoT systems by presenting a model-based testing (MBT) approach. To enable modeling expected behaviors of an IoT system, we first describe an extension to a UML profile. Then, we capture the expected behavior of an interaction that is modeled on a Sequence Diagram (SD). Later, the expected behaviors are translated into runtime monitor statements expressed in Event-Processing Language (EPL), which are executed at the edge of the IoT network. We further demonstrate our contributions on a sample AAL system.