Ambient Assisted Living Environments Research Articles

A Biologically Inspired Movement Recognition System with Spiking Neural Networks for Ambient Assisted Living Applications.

This study presents a novel solution for ambient assisted living (AAL) applications that utilizes spiking neural networks (SNNs) and reconfigurable neuromorphic processors. As demographic shifts result in an increased need for eldercare, due to a large elderly population that favors independence, there is a pressing need for efficient solutions. Traditional deep neural networks (DNNs) are typically energy-intensive and computationally demanding. In contrast, this study turns to SNNs, which are more energy-efficient and mimic biological neural processes, offering a viable alternative to DNNs. We propose asynchronous cellular automaton-based neurons (ACANs), which stand out for their hardware-efficient design and ability to reproduce complex neural behaviors. By utilizing the remote supervised method (ReSuMe), this study improves spike train learning efficiency in SNNs. We apply this to movement recognition in an elderly population, using motion capture data. Our results highlight a high classification accuracy of 83.4%, demonstrating the approach's efficacy in precise movement activity classification. This method's significant advantage lies in its potential for real-time, energy-efficient processing in AAL environments. Our findings not only demonstrate SNNs' superiority over conventional DNNs in computational efficiency but also pave the way for practical neuromorphic computing applications in eldercare.

Gated recurrent unit decision model for device argumentation in ambient assisted living

The increasing elderly population worldwide is facing a variety of social, physical, and cognitive issues, such as walking problems, falls, and difficulties in performing daily activities. To support elderly people, continuous monitoring and supervision are needed. Due to the busy modern lifestyle of caretakers, taking care of elderly people is difficult. As a result, many elderly people prefer to live independently at home without any assistance. To help such people, an ambient assisted living (AAL) environment is provided that monitors and evaluates the daily activities of elderly individuals. An AAL environment has heterogeneous devices that interact, and exchange information of the activities performed by the users. The devices can be involve in an argumentation about the occurrence of an activity thus leading to generate conflicts. To address this issue, the paper proposes a gated recurrent unit (GRU) learning techniques to facilitate decision-making for device argumentation during activity occurrences. The proposed model is used to initially classify user activities and each sensor value status. Then a novel method is used to identify argumentation among devices for activity occurrences in the classified user activities. Later, the GRU decision making model is used to resolve the argumentation and to identify the target activity that occurred. The result of the proposed model is compared with other existing techniques. The proposed model outperformed the other existing methods with an accuracy of 85.45%, precision of 72.32%, recall of 65.83%, and F1-Score of 60.22%.

Novel Stacking Classification and Prediction Algorithm Based Ambient Assisted Living for Elderly

Recognition of human activity is a significant area of research with numerous uses. In developed countries, the rising age of citizens requires the improvement of the medical service structure, which raises the price of resources, both financial and human. In that sense, ambient assisted living (AAL) is a relatively novel information and communication technology (ICT) that presents services and recognizes various products that enable older people and the disabled to live autonomously and improve their quality of life. It further assists in reducing the cost of hospital services. In the AAL environment, various sensors and devices are fixed to gather a broad range of data. Moreover, AAL will be the motivating technology for the latest care models by acting as an adjunct. This will become thought-provoking research in a fast-growing world, but exploring different ADL and self-classification will become a major challenge. This paper proposed a Novel Stacking Classification and Prediction (NSCP) algorithm based AAL for the elderly with Multi-strategy Combination based Feature Selection (MCFS) and Novel Clustering Aggregation (NCA) algorithms. This paper’s main aim is to recognize the activity of older people, such as standing, walking, sitting, falling, cramps, and running. The dataset is derived from the Kaggle repository, which refers to data collection from wearable IoT devices. The experimental outcomes demonstrate that the MCFS, NCA, and NSCP algorithms work more efficiently than existing feature selection, clustering, and classification algorithms, respectively, regarding the accuracy, sensitivity, specificity, precision, recall, F-measure, and execution time dataset size and the number of features. Furthermore, the NSCP algorithm provided high accuracy, precision, recall, and F-measure are 98%, 0.96, 0.95, and 0.98, respectively.

ZigBee Healthcare Monitoring System for Ambient Assisted Living Environments

Healthcare Monitoring Systems (HMSs) are promising to monitor patients in hospitals and elderly people living in Ambient Assisted Living environments using Wireless Sensor Networks. HMSs assist in monitoring chronic diseases such as Heart Attacks, High Blood Pressure and other cardiovascular diseases. Wearable and implanted devices are types of Body sensors that collect human health related data. Collected data is sent over Personal Area Networks (PANs). However, PANs are facing the challenge of increasing network traffic due to the increased number of IP-enabled devices connected in Healthcare Monitoring Systems to assist patients. ZigBee technology is an IEEE 802.15.4 standard designed to address network traffic issues in PANs. To route traffic, ZigBee network use ZigBee Tree Routing (ZTR) protocol. ZTR however suffers a challenge of network latency caused by end to end delay during packet forwarding. This paper is proposing a New Tree Routing Protocol (NTRP) for Healthcare Monitoring Systems to collect Heart Rate signals. NTRP uses Kruskal’s minimum spanning tree to find shortest routes on a ZigBee network which improves ZTR. Neighbor tables are implemented in NTRP instead of parent–child mechanism implemented in ZTR. To reduce end to end delay, NTRP groups’ nodes into clusters and the cluster heads use neighbor tables to forward heart rate data to the destination node. NS-2 simulation tool is used to evaluate NTRP performance.

Ambient Assisted Living: Identifying New Challenges and Needs for Digital Technologies and Service Innovation.

SummaryBackground : The ambient assisted living (AAL) market is rapidly becoming fundamental to the delivery of health and social care services for the elderly. Worldwide many different steps have been taken to increase the engagement of older adults with these technologies. Much of this work has focused on the development of novel digital services that increase wellbeing or tackle social challenges. Aim : The aim of the study was to identify and describe the demands for AAL-services from the perspective of older adults. We also examine the challenges and needs of the ambient assisted living market using a needs based approach. Method : An exploratory case study was conducted with an aim to capture information about older adults’ demands for AAL services. A survey was used to collect the data. The survey study respondents validated the results. Results : The results of the study indicate that the area of AAL needs be studied from a multiple-sided market perspective. Our research suggests there is a need to describe and understand the factors that facilitate or constrain the implementation of services with focus on health and social care. There is also a need to describe and analyze the relationship between policy and practice and its effects on the AAL market. It is necessary to capture expressed demand, to identify market challenges at the macro level and to be able to understand how services should operate and serve older adults in practice. Such research is critical to the development of guidance for policy makers, suppliers and service providers. Discussion : Older adults are asking for intelligent, assistive living solutions that help them to continue to live independent lives and remain socially included in their networks, associations, and communities. The elderly need services that stimulate and maintain their physical and intellectual capital. The development of innovative AAL environments is, however, a complex social process that involves the use and delivery of innovative ICT–based services. The implementation and use of AAL to support older adults involve service providers and elderly consumers. Conclusions : The results of the study may be of interest to policy makers, entrepreneurs, technology suppliers, service providers and health and social care organizations, who are willing to innovate and influence the development of the AAL market through their choices and decisions.

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.

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.

Multimodal Sensor Data Integration for Indoor Positioning in Ambient-Assisted Living Environments

A reliable Indoor Positioning System (IPS) is a crucial part of the Ambient-Assisted Living (AAL) concept. The use of Wi-Fi fingerprinting techniques to determine the location of the user, based on the Received Signal Strength Indication (RSSI) mapping, avoids the need to deploy a dedicated positioning infrastructure but comes with its own issues. Heterogeneity of devices and RSSI variability in space and time due to environment changing conditions pose a challenge to positioning systems based on this technique. The primary purpose of this research is to examine the viability of leveraging other sensors in aiding the positioning system to provide more accurate predictions. In particular, the experiments presented in this work show that Inertial Motion Units (IMU), which are present by default in smart devices such as smartphones or smartwatches, can increase the performance of Indoor Positioning Systems in AAL environments. Furthermore, this paper assesses a set of techniques to predict the future performance of the positioning system based on the training data, as well as complementary strategies such as data scaling and the use of consecutive Wi-Fi scanning to further improve the reliability of the IPS predictions. This research shows that a robust positioning estimation can be derived from such strategies.

Optimal Sensor Deployment According to a New Approach for Target Tracking in Smart Homes

Ambient Assisted Living (AAL) aims to ease the activities of daily lives for elderly or disabled people living in smart homes. In these systems, the assisting services are provided by use of the sensors that detect the location of inhabitants. Even though the cost and performance efficiencies of sensor networks depend directly on the number of the deployed sensors as well as the approaches that are employed to track the location of inhabitants, these factors are separately considered in research studies. In this paper, we propose an approach to track a target in smart homes. We present a new Integer Linear Programming (ILP) model that i. optimizes the number of the deployed sensors according to our proposed target tracking approach, ii. determines the placement of sensors in the network while considering the layout of the Region of Interest (RoI), iii. proposes appropriate settings for the sensors' field of view and their orientation, and, iv. deploys sensors according to the satisfaction of a reliability level in the network. In order to validate the proposed model and show its applicability, we use two sets of data, including real data gathered from a living lab and randomly generated data, as well as a test scenario. We also present a user interface that employs the concepts, which are presented in this research. The interface helps to easily monitor the location, energy consumption and well-being factors of a target in an AAL environment.

Serious Motion-Based Exercise Games for Older Adults: Evaluation of Usability, Performance, and Pain Mitigation.

BackgroundMany societies are facing demographic changes that challenge the viability of health and welfare systems. Serious games for health care and ambient assisted living (AAL) offer health benefits and support for older adults and may mitigate some of the negative effects of the demographic shift.ObjectiveThis study aimed to examine the acceptance of serious games to promote physical health in AAL environments. Since AAL environments are designed specifically to support independent living in older adults, we studied the relationship among age and user diversity, performance in the game, and overall usability and acceptance evaluation.MethodsWe developed a motion-based serious exercise game for prototypical AAL environments. In two evaluations, outside (n=71) and within (n=64) the AAL environment, we investigated the influence of age, gender, self-efficacy in interacting with technology, need for achievement on performance, effect of the game, usability evaluation of the game, and overall acceptance.ResultsBoth games were evaluated as easy to use and fun to play. Both game interventions had a strong pain-mitigating effect in older adults (game 1: −55%, P=.002; game 2: −66%, P=.01).ConclusionsSerious exercise games outside and inside AAL environments can contribute to individuals’ health and well-being and to the stability of health care systems.

Ambient Assisted Living: A Research on Human Activity Recognition and Vital Health Sign Monitoring using Deep Learning Approaches

The rise in life expectancy rate and dwindled birth rate in new age society has led to the phenomenon of population ageing which is being witnessed across the world from past few decades. India is also a part of this demographic transition which will have the direct impact on the societal and economic conditions of the country. In order to effectively deal with the prevailing phenomenon, stakeholders involved are coming up with the Information and Communication Technology (ICT) based ecosystem to address the needs of elderly people such as independent living, activity recognition, vital health sign monitoring, prevention from social isolation etc. Ambient Assisted Living (AAL) is one such ecosystem which is capable of providing safe and secured living environment for the elderly and disabled people. In this paper we will focus on reviewing the sensor based Human Activity Recognition (HAR) and Vital Health Sign Monitoring (VHSM) which is applicable for AAL environments. At first we generally describe the AAL environment. Next we present brief insights into sensor modalities and different deep learning architectures. Later, we survey the existing literature for HAR and VHSM based on sensor modality and deep learning approach used.

ZigBee Healthcare Monitoring System for Ambient Assisted Living Environments

Healthcare Monitoring Systems (HMSs) are promising to monitor patients in hospitals and elderly people living in Ambient Assisted Living environments using Wireless Sensor Networks. HMSs assist in monitoring chronic diseases such as Heart Attacks, High Blood Pressure and other cardiovascular diseases. Wearable and implanted devices are types of Body sensors that collect human health related data. Collected data is sent over Personal Area Networks (PANs). However, PANs are facing the challenge of increasing network traffic due to the increased number of IP-enabled devices connected in Healthcare Monitoring Systems to assist patients. ZigBee technology is an IEEE 802.15.4 standard designed to address network traffic issues in PANs. To route traffic, ZigBee network use ZigBee Tree Routing (ZTR) protocol. ZTR however suffers a challenge of network latency caused by end to end delay during packet forwarding. This paper is proposing a New Tree Routing Protocol (NTRP) for Healthcare Monitoring Systems to collect Heart Rate signals. NTRP uses Kruskal’s minimum spanning tree to find shortest routes on a ZigBee network which improves ZTR. Neighbor tables are implemented in NTRP instead of parent–child mechanism implemented in ZTR. To reduce end to end delay, NTRP groups’ nodes into clusters and the cluster heads use neighbor tables to forward heart rate data to the destination node. NS-2 simulation tool is used to evaluate NTRP performance.

Activities suggestion based on emotions in AAL environments.

The elderly population is increasing and the response of the society was to provide them with services directed to them to cope with their needs. One of the oldest solutions is the retirement home, providing housing and permanent assistance for the elderly. Furthermore, most of the retirement homes are inhabited by multiple elderly people, thus creating a community of people who are somewhat related in age and medical issues. The ambient assisted living (AAL) area tries to solve some of the elderly issues by producing technological products, some of them dedicated to elderly homes. One of the identified problem is that elderly people are sometimes discontent about the activities that consume most of their day promoted by the retirement home social workers. The work presented in this paper attempts to improve how these activities are scheduled taking into account the elderlies' emotional response to these activities. The aim is to maximize the group happiness by promoting the activities the group likes, minding if they are bored due to activities repetition. In this sense, this paper presents an extension of the Cognitive Life Assistant platform incorporating a social emotional model. The proposed system has been modelled as a free time activity manager which is in charge of suggesting activities to the social workers.

An evaluation study of vital parameter determination with RGB cameras in the field of ambient assisted living

Abstract The remote determination of vital parameters can contribute to the field of ambient assisted living (AAL) to face the challenges of a steadily ageing society. In this study, we examined the possibility to remotely measure the four vital parameters heart rate, respiration rate, oxygen saturation and blood pressure in AAL environments. For this, we evaluated the state of the art, implementations and concepts. While, the determination of heart and respiration rate is ready for usage, further attention has to be paid to the determination of oxygen saturation and blood pressure.

Tackling The New Health-Care Paradigm Through Service Robotics: Unobtrusive, efficient, reliable, and modular solutions for assisted-living environments

Medical care, supported by modern technology and methods, will play an important role in our global society. An increasing average age and the need to keep standards in care lead to the introduction of several solutions often described as ambient assisted living (AAL). However, most people feel uncomfortable with these solutions, describing a sense of secret observation. To alleviate this sense of secret observation, the sensor platform is placed in clear view of the end user. This is accomplished through the involvement of robotics in this domain, and both caregivers and the patient are offered support for several tasks. The Robots in Assisted Living Environments: Unobtrusive, Efficient, Reliable, and Modular Solutions for Independent Ageing (RADIO) project targets this topic but differs from other solutions by its specific concepts, which are especially the unobtrusiveness and inclusion into home automation. This article presents the RADIO approach with its solutions for combining robotics and home automation for use in AAL environments.

Reliability and human factors in Ambient Assisted Living environments

Malnutrition, sedentariness, and cognitive decline in elderly people represent the target areas addressed by the DOREMI project. It aimed at developing a systemic solution for elderly, able to prolong their functional and cognitive capacity by empowering, stimulating, and unobtrusively monitoring the daily activities according to well-defined “Active Ageing” life-style protocols. Besides the key features of DOREMI in terms of technological and medical protocol solutions, this work is focused on the analysis of the impact of such a solution on the daily life of users and how the users’ behaviour modifies the expected results of the system in a long-term perspective. To this end, we analyse the reliability of the whole system in terms of human factors and their effects on the reliability requirements identified before starting the experimentation in the pilot sites. After giving an overview of the technological solutions we adopted in the project, this paper concentrates on the activities conducted during the two pilot site studies (32 test sites across UK and Italy), the users’ experience of the entire system, and how human factors influenced its overall reliability.

Situation Awareness in Ambient Assisted Living for Smart Healthcare

The success of providing smart healthcare services in ambient assisted living (AAL) largely depends on an effective prediction of situations in the environment. Situation awareness in AAL is to determine the environment smartness by perceiving information related to the surroundings and human behavioral changes. In AAL environment, there are plenty of ways to collect data about its inhabitants, such as through cameras, microphones, and other sensors. The collected data are complicated enough to go for an efficient processing in perceiving the situation. This paper gives an overview of the existing research results in multimodal data analysis in AAL environment to improve the living environment of the seniors, and it attempts to bring efficiency in complex event processing for real-time situational awareness. This paper thus considers multimodal sensing for detection of current situations as well as to predict future situations using decision-tree and association analysis algorithms. To illustrate the proposed approach, we consider elderly activity recognition in the AAL environment.

Semantic and Virtual Reality-Enhanced Configuration of Domestic Environments: The Smart Home Simulator

This paper introduces the Smart Home Simulator, one of the main outcomes of the D4All project. This application takes into account the variety of issues involved in the development of Ambient Assisted Living (AAL) solutions, such as the peculiarity of each end-users, appliances, and technologies with their deployment and data-sharing issues. The Smart Home Simulator—a mixed reality application able to support the configuration and customization of domestic environments in AAL systems—leverages on integration capabilities of Semantic Web technologies and the possibility to model relevant knowledge (about both the dwellers and the domestic environment) into formal models. It also exploits Virtual Reality technologies as an efficient means to simplify the configuration of customized AAL environments. The application and the underlying framework will be validated through two different use cases, each one foreseeing the customized configuration of a domestic environment for specific segments of users.

Effects of Ambient Lighting Displays on Peripheral Activity Awareness

The emergence of ubiquitous sensing and the Internet of Things (IoT) have inspired the development of “smart” everyday objects, which offer tremendous opportunities for maintaining the quality of life in ambient assisted living (AAL) environments. Inspired by the future possibilities of connected everyday devices, we envision a peripheral activity-based awareness system that captures human activity information and renders this information to enhance context awareness and support social connectedness between the elderly and their caregivers. Leveraging ambient intelligence and IoT technologies, ambient displays can convey activity information in the periphery of our attention. In particular, light has been used as a means to display ambient information and there is scientific evidence that it can enhance well-being, interconnectedness, and improve productivity. In this paper, we undertake two studies, first through an exploratory study we investigate the features of light suitable for conveying subtle activity information within the periphery of the users’ attention for promoting context awareness. Also, we examine the preferences, perceptions, and interpretations of ambient lighting configurations of prospective caregiver’s for conveying the activity information of older adults. In a second study, we assess the implications of activity awareness through lighting on cognitive performance, moods, and social connectedness. The set-up and design decisions of the second study were partly informed by previous research and by the outcomes of the first experiment. Together, these studies provide additional design guidelines for representing activity information with ambient lighting and highlight potential benefits and usage possibilities for lighting displays within the AAL domain. Furthermore, the results indicate a significant effect of activity awareness on cognitive performance. However, there were no significant effects of activity awareness through lighting on moods and social connectedness. Finally, we discuss the design guidelines and implications of these findings for designing activity-based ambient lighting displays for AAL environments.