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Over the past decade, WiFi CSI-based device-free sensing technology has shown great potential in smart homes, assisted living, and many other applications. While model-based device-free sensing approaches analyze and recognize human behaviors by constructing mathematical relationships among WiFi devices, environment, human position/posture, and received channel state information, they have attracted great attention because of the interpretable physical meaning and the ability to guide the WiFi-based sensing system design. In this paper, we retrospect two general-purpose sensing models, i.e., the Fresnel zone model and CSI-ratio model, and demonstrate how these two models are leveraged to extract insightful properties and support a variety of device-free sensing applications.

One of the most important lifestyle risk factors for many chronic conditions in the older age, low physical activity has shown to have significant impact on the sustainability of national welfare in many developed countries. Technology-based assisted living solutions can effectively be used to enable older adults to optimise their health-related quality of life, as well as to promote an active and healthy longevity. This paper describes vINCI—an interdisciplinary research project to actively support assisted living for older adults via state-of-the-art assistive technologies—which seamlessly deploys an ambient intelligence environment to integrate wearable devices, networking, software, and personalised services. It entails clinical validation and feedback at home and residential care facilities via a cloud microservices platform. Underpinned by blockchain technologies, multiple wearable devices, apps, and cameras securely capture the anonymised facets of different life events, whilst machine learning models create individualised user profiles to analyse any decrease in the perceived health-related quality of life typically associated with old age. Two controlled pilots are being conducted with 80 participants at older adult facilities in Romania and Cyprus. By incorporating clinical validation and feedback from specialised practitioners, the vINCI technologies enable older adults not only to self-evaluate their physical activity level, but also to change their behaviours and lifestyle in the long-term.

Background Using Ambient Assisted Living sensors to detect acute stress could help people mitigate the harmful effects of everyday stressful situations. This would help both the healthy and those affected more by sudden stressors, e.g., people with diabetes or heart conditions. The study aimed to develop a method for providing reliable stress detection based on heart rate variability features extracted from portable devices. Methods Features extracted from portable electrocardiogram sensor recordings were used for training various classification algorithms for stress detection purposes. Data were recorded in a clinical trial with 7 participants and two stressors, the Trier Social Stress Test and the Stroop colour word test, both validated by standardised questionnaires. Different heart rate variability feature sets (all, time-domain and non-linear only, frequency-domain only) were tested to investigate how classification performance is affected, in addition to various time window length setups and participant-wise training sessions. The accuracy and F1 score of the trained models were compared and analysed. Results The best results were achieved with models using time-domain and non-linear heart rate variability features with 5-min-long overlapping time windows, yielding 96.31% accuracy and 96.26% F1 score. Shorter overlapping windows had slightly lower performance, with 91.62–94.55% accuracy and 91.77–94.55% F1 score ranges. Non-overlapping window configurations were less effective, with both accuracy and F1 score below 88%. For participant-wise learning, average F1 scores of 99.47%, 98.93% and 96.1% were achieved for feature sets using all, time-domain and non-linear, and frequency-domain features, respectively. Conclusion The tested stress detector models based on heart rate variability data recorded by a single electrocardiogram sensor performed just as well as those published in the literature working with multiple sensors, or even better. This suggests that once portable devices such as smartwatches provide reliable hear rate variability recordings, efficient stress detection can be achieved without the need for additional physiological measurements.

Rehabilitation robots are becoming an important means of assisted living for the elderly, and the appearance of rehabilitation robots directly affects the willingness of the elderly to interact with the robots. Much of the current research on robot appearance preferences relies solely on subjective evaluations, which are relatively cheap, but do not reach deep into the brain to get an accurate grasp of how humans respond to robot appearance. Using electroencephalogram signal and questionnaire survey, we studied the preference of the elderly for abstract and figurative robots. The experimental materials are derived from the pictures of 10 robots in the market. The electroencephalogram signal are collected by BrainVision Recorder and processed by BrainVision Analyzer, as well as SPSS statistical analysis. Experiment shows that the peak of figurative robot pictures is higher and the fluctuation is more intense from 350 ms to 600 ms in the central region and the right half of parietal region. While the peak of abstract robot pictures is higher and the fluctuation is more intense in the prefrontal region, and the difference between abstract robot and figurative robot is not obvious in the occipital region. Based on the electroencephalogram signal and experimental results, it provides the possibility for objective preference evaluation of the elderly to the robot designed features.

Recognition of the human activities in videos has gathered numerous demands in various applications of computer vision like Ambient Assisted Living, intelligent surveillance, Human-Computer interaction. One of the most pioneering techniques for Human Detection in Video Surveillance based on deep learning and this project mainly focuses on various approaches based on that. This paper provides an idea of solution to use video surveillance more effectively, by detecting any humans present and notifying the concerned people. The deep learning model, preferred for fast computation, Convolution Neural Network is used by stacking 3 blocks of layers on fully connected layers. This provided an identification of humans and naïve approach to eliminate inanimate human like objects such as mannequins.

Demographically, the proportion of elderly people in the population is increasing worldwide. The proportion of elderly people (aged over 60) has been rising and in 2015 the proportion was reported to be 12.3% and is expected to reach 16.5% of the world population in the year 2030. Additionally, elderly people are more vulnerable to many diseases and heart failure persists as the most common cause of mortality. However, while acknowledging this threat, elderly people have a longer life expectancy and live more independently in the environment of their choice. To meet this challenge, smart devices are considered a solution to monitor heart rates of elderly people and detect any abnormal activity that may require action to be taken. In order to assess how this solution could be implemented, the aim of this paper was to compare the accuracy of heart rate readings by conducting experiments on five different models of smart devices: (A) Vivosmart, (B) Philips, (C) Pulsimeter, (D) Fitbit-Surge, and (E) Chest Strap. A comparison of the results demonstrated that the Smart watch devices detect heart rates more accurately when compared to Chest Straps and the Pulsoximeter, with a variation of $$\pm$$ 10%. The results of this paper suggest that replacing current devices (such as chest strap and finger device) with Smart watches as a primary solution for heart rate monitoring to support healthcare and for Ambient Assisted Living (AAL) would significantly reduce cost incurred by physically visiting elderly individuals on a daily, weekly or even monthly basis.

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.

The detection of Generalized Tonic Clonic Seizures (GTCS) and Falls is of utmost importance due to the increase in prevalence of epilepsy and Sudden Death in Epileptic Patients during CoVID-19 pandemic, and prevention of serious injuries in Fall risk groups such as elderly requiring continuous monitoring for disease management and assisted living etc. Monitoring of Activities of Daily Living (ADLs) can assist in the detection of symptoms and onset of neurological disorders such as Alzheimer’s, stroke, and epileptic seizures. With a host of embedded sensors, improved memory, enhanced processing capabilities and availability to masses, smartphones can be used for Human Activity Recognition (HAR) through continuous monitoring of ADLs. This paper presents a tri-axial accelerometer-based approach to detect and classify activities performed by individuals by applying machine learning algorithms including RF, J48, NB, LMT and SVM to movement data. Movement data is collected in real-time from the embedded accelerometer of a smartphone worn by individual on upper-left arm in unconstraint environment. It is pre-processed using time and frequency domain analysis and spatial domain features are computed. Supervised machine learning techniques are applied to classify ADLs into five classes based on the intensity of movements: Stationary, Light Ambulatory, Intense Ambulatory, GTCS and Falls. We also used training data from MyNeuroHealth dataset collected from 23 individuals including epilepsy patients. Based on gathered results, Random Forest outperforms other classifiers with classification accuracy of 99.6% for stationary, 81.5% for light ambulatory, 99.8% for intense ambulatory and GTCS, and 97.2% for Falls corresponding to training data of 14000 samples. To date, activity classification in our system has been implemented on cloud instead of mobile phone application as subjects are using smartphones with dissimilar software and hardware specifications for assisted living applications.

The significant increase in the number of individuals with chronic ailments (including the elderly and disabled) has dictated an urgent need for an innovative model for healthcare systems. The evolved model will be more personalized and less reliant on traditional brick-and-mortar healthcare institutions such as hospitals, nursing homes, and long-term healthcare centers. The smart healthcare system is a topic of recently growing interest and has become increasingly required due to major developments in modern technologies, especially in artificial intelligence (AI) and machine learning (ML). This paper is aimed to discuss the current state-of-the-art smart healthcare systems highlighting major areas like wearable and smartphone devices for health monitoring, machine learning for disease diagnosis, and the assistive frameworks, including social robots developed for the ambient assisted living environment. Additionally, the paper demonstrates software integration architectures that are very significant to create smart healthcare systems, integrating seamlessly the benefit of data analytics and other tools of AI. The explained developed systems focus on several facets: the contribution of each developed framework, the detailed working procedure, the performance as outcomes, and the comparative merits and limitations. The current research challenges with potential future directions are addressed to highlight the drawbacks of existing systems and the possible methods to introduce novel frameworks, respectively. This review aims at providing comprehensive insights into the recent developments of smart healthcare systems to equip experts to contribute to the field.

PurposeThe authors analyse the impact of European funding research programmes on the topic of Ambient Assisted Living by considering its status, future context, and the implications for prospective knowledge management.Design/methodology/approachThe authors apply our variation of classical Systematic Literature Review – Systematic Literature Network Analysis, which also includes bibliographic networks – to identify the readership cliques of the associated technological publication outputs.FindingsThe authors’ main conclusion suggests that there was an increase in scientific production on AAL fields just after the start of the two EU funding programmes (2008 and 2014). Three main research directions were identified: activity and vital sign recognition, human-computer interaction and technology acceptance.Originality/valueTo date, previous reviews on Ambient Assisted Livig focus on specific aspects, such as the study of technology. The present review provides a complete overview of Ambient Assisted living technology and it grasps how the European funds have impacted on the development of this technology.

Contactless radar and radio frequency (RF) sensing has recently gained much interest in the domain of health care and assisted living, due to its capability to monitor relevant parameters for the health and well‐being of people. Applications range from the monitoring of respiration and heartbeat to mobility levels, gait and locomotion parameters, and behavioral patterns. What makes radar sensing attractive compared to alternative and complementary technologies such as video‐cameras, wearables, or ambient sensors, is their contactless capabilities, whereby no sensors need to be worn by the people monitored, and no plain images or videos need to be collected in private spaces and homes.

Recognizing activities of daily living is useful for ambient assisted living. In this regard, the use of wearable cameras is a promising technology. In this paper, we propose a novel approach for recognizing activities of daily living using egocentric viewpoint video clips. First, in every frame, the appearing objects are detected and labelled depending if they are being used or not by the subject. Later, the video clip is divided into spatiotemporal bins created with an object-centric cut. Finally, a support vector machine classifier is computed using a spatiotemporal flexible kernel between video clips. The validity of the proposed method has been proved by conducting experiments in the ADL dataset. Results confirm the suitability of using the space-time location of objects as information for the classification of activities using an egocentric viewpoint.

Purpose Technology plays an important role in assisting elderly people to live independently, longer and improve their quality of life and health, in supporting their daily activities, etc. The ageing population becomes a global phenomenon. The population of Saudi Arabia continues to age (>60 years of age) currently (5%) compared to other group ages. In 2050, it will increase rapidly to 20.9% of the Saudi population. The current research aims at examining the barriers that health-care providers in the Kingdom of Saudi Arabia are experiencing in the adoption of ambient assisted living (AAL) technologies among the elderly. The study aims to identify a challenging issue with the increasing the number of elderly among the population in the country, which has highlighted the need to use AAL technology to improve the quality of life among the elderly. Design/methodology/approach This study involved a community of practice (CoP) study as a method of data collection where data collected were presented and discussed in line with the existing literature review findings. Findings In total, 14 factors were identified in this study and discussed in the context of Saudi Arabia, which resulted in developing a decision-making framework for using AAL by health-care providers. Those factors are essential in boosting the usage of technology in improving elderly health in Saudi Arabia. Research limitations/implications This study includes implications for developing a decision-making framework for using AAL. Social implications This study clarifies that technology can connect elderly people with society. Originality/value In total, 14 factors were identified in this study and discussed in the context of Saudi Arabia.

PurposeAgeing populations induce needs to adapt existing housing. With ageing, the number of frail old people, who require assistance in daily life, is also increased. Converting existing housing into assisted living enables them to remain in their community while receiving necessary support and care. The purpose is to investigate whether post-war mass housing is spatially appropriate for adaptation into group homes for older people.Design/methodology/approachThe research material is attained from Finland. Spatial requirements for group homes are drawn from 130 units built or renovated during 2000–2015. Spatial characteristics of mass housing are mapped from 105 apartment buildings built in the 1970s. The latter are matched with the former by comparing the connectivity of layouts, sizes of units and the numbers and sizes of individual spaces.FindingsGroup homes typically utilize a linear layout, which can easily be created in apartment buildings. Individual spaces of a group home fit apartment buildings effortlessly. Whole group home units mostly prove to be spatially feasible but result in looser dimensioning than is typical in existing units. The mass housing stock can be considered a spatial reserve for adaptation into group homes.Originality/valueThis is the first study to employ a large-scale, multi-case spatial mapping approach to analyse the adaptability properties of mass housing into assisted living. The findings pertain primarily to the Finnish context, but a methodology is presented which can be applied to other countries and also to other spatial functions.

Identification of human activity considering social interactions and group dynamics non-intrusively has been one of the fundamental problems and a challenging area of research. In real life, it is required for designing human-centric applications like assisted living, health care, and creating a smart home environment. As human beings spend 90% of time indoors, such a system will be helpful to monitor the behavioral anomalies of the inhabitants. Existing approaches have used intrusive or invasive methods like camera or wearable devices. In this work, we present a device-free, non-invasive, and non-intrusive sensing framework called HumanSense using an array of heterogeneous sensor grid for human activity monitoring. The sensor grids, comprising the ultrasonic and sound sensors, have been deployed for collective sensing combining a person’s physical activity and verbal interaction information. The proposed system senses a stream of events when the occupant(s) perform different physical activities categorized as atomic and group activities like sitting, standing, and walking. Simultaneously, it also tracks person-person verbal interactions such as monologue and discussion. Both information are then integrated into a single framework to understand the overall behavioral scenario of the indoor environment. The experimental results have shown that HumanSense can detect different activities with accuracy more than 90% and also improves overall identification accuracy compared to existing works. Our developed system can be further evolved into ready-to-deploy smart sensing panels which can be effective for human activity monitoring in an indoor environment.

The primary objective of the ambient assisted living (AAL) is to enhance the living standards of the aged person by presenting various assistive solutions. The services presented to the occupants in AAL are localization, context awareness, activity and behaviour monitoring, personalization, overcoming loneliness, privacy, security, and many more. To endeavour the user with these services, human-activity recognition and human behaviour modeling is a fundamental necessity. Many researchers have assumed only the periodic and consistent behaviour of the users while designing their frameworks; in a real scenario, human behaviour is unpredictable. There arises a need for converting the proposal into the working prototype due to the enormous difference between the available solutions and actual needs. This paper introduces a novel hybrid framework for human behaviour modeling in AAL, which accommodates the varying nature of activity and behaviour of the inhabitant. The proposal uses feedback-based mechanism to recursively append new events and behaviour, such that they are classified into normal or abnormal human behaviour. The proposed solution uses the pattern mining and clustering mechanism to determine the anomalies in addition to the camera sensor, which detects human fall to deal with any critical situation. Once the action is recognized, it is more convenient to extend essential services like medical assistance or emergency response.

The progressive miniaturization of electronic devices and their exponential increase in processing, storage and transmission capabilities, represent key factors of the current digital transformation, also sustaining the great development of Ambient Assisted Living (AAL) and the Internet of Things. Although most of the investigations in the recent years focused on remote monitoring and diagnostics, rehabilitation too could be positively affected by the widespread integrated use of these devices. Smart Objects in particular may be among the enablers to new quantitative approaches. In this paper, we present a proof-of-concept and some preliminary results of an innovative pediatric rehabilitation protocol based on Smart Objects and biofeedback, which we administered to a sample of children with unilateral cerebral palsy. The novelty of the approach mainly consists in placing the sensing device into a common toy (a ball in our protocol) and using the information measured by the device to administer multimedia-enriched type of exercises, more engaging if compared to the usual rehabilitation activities used in clinical settings. We also introduce a couple of performance indexes, which could be helpful for a quantitative continuous evaluation of movements during the exercises. Even if the number of children involved and sessions performed are not suitable to assess any change in the subjects’ abilities, nor to derive solid statistical inferences, the novel approach resulted very engaging and enjoyable by all the children participating in the study. Moreover, given the almost non-existent literature on the use of Smart Objects in pediatric rehabilitation, the few qualitative/quantitative results here reported may promote the scientific and clinical discussion regarding AAL solutions in a “Computer Assisted Rehabilitation” perspective, towards what can be defined “Pediatric Rehabilitation 2.0”.

In this paper, an interpretable fuzzy deep belief network (DBN)-based classifier called deep belief networks-based Takagi-Sugeno-Kang fuzzy classifier (DBN-TSK-FC) is created for indoor user movement prediction in ambient assisted living applications. With its promising classification performance, DBN-TSK-FC features sharing both the powerful neural representation ability of a DBN and the strong uncertainty-handling capability of an interpretable fuzzy representation. On the one hand, DBN-TSK-FC builds its interpretable fuzzy representation in a hierarchical way by applying the classical fuzzy clustering algorithm FCM to obtain fuzzy partitions on the training dataset. Then, it forms interpretable antecedent parts of fuzzy rules as the corresponding fuzzy representation. On the other hand, DBN-TSK-FC builds its DBN-based neural representation in the other hierarchical way. That is, it applies the existing unsupervised DBN pretraining on the training dataset and then takes the neural representation of all the hidden nodes in the top layer of the corresponding DBN as the set of consequent variables of fuzzy rules. In this approach, both the interpretable fuzzy representation and the DBN-based neural representation are further fused to form the corresponding fuzzy rules quickly by using the least learning machine (LLM) on both the fuzzy rules and the labeling information of the original dataset. Therefore, DBN-TSK-FC is essentially a deep TSK fuzzy classifier from the perspective of fuzzy rules, and it indeed avoids the very slow fine-tuning training required after the unsupervised pretraining of the existing DBN learning. The experimental results on the MovementAAL_RSS dataset indicate the effectiveness of the proposed classifier DBN-TSK-FC.

Wearable and Internet of Things (IoT) devices have the potential to improve lifestyle, personalize receiving treatments or introduce assisted living for elderly people. However, service delivery depends on maintaining and troubleshooting device connectivity to smartphones, where user engagement and technology proficiency represent a possible barrier that prevents a wider adoption, especially in the elderly and disabled population. Low-cost and low-power wearable and IoT devices face challenges when operating out of range of known home networks or pared devices. We propose an architecture and methods to provide seamless connectivity (Se-Co) between devices and wireless networks while maintaining low-power, low-cost and standards compatibility. Through Se-Co, the devices connect without user interaction both in home and in unknown roaming networks while maintaining anonymity, privacy and security. Roaming networks approve data limited connectivity to unknown devices that are able to provide a valid anonymized certificate of compliance and no harm through a home provider. Se-Co enables shifting data processing, such as pattern processing using artificial intelligence, from a wearable device or smartphone towards the cloud. The proposed Se-Co architecture could provide solutions to increase usability of wearable devices and improve their wider adoption, while keeping low the costs of devices, development and services.

This work is a study about a comparison of survey tools and it should help developers in selecting a suited tool for application in an AAL environment. The first step was to identify the basic required functionality of the survey tools used for AAL technologies and to compare these tools by their functionality and assignments. The comparative study was derived from the data obtained, previous literature studies and further technical data. A list of requirements was stated and ordered in terms of relevance to the target application domain. With the help of an integrated assessment method, the calculation of a generalized estimate value was performed and the result is explained. Finally, the planned application of this tool in a running project is explained.

With increasing digitalization, expectations are growing for systems that make a self-determined life possible for people in need. On the other hand, increasing technological development also brings challenges. Producers and distributors are facing increasing difficulties, especially when it comes to the market launch of Active Assisted Living (AAL) solutions. With the help of AAL systems, the aim is to enable older generations and persons in need of help to live longer and more indefinitely in their own homes. Despite the advantages and fundamental importance of this topic, there are some major challenges. According to literature, none of the existing studies describe, categorize and identify these challenges with the ulterior motive to be aware of them and to find a remedy. Within this quantitative study based on a qualitative pre-study, two fundamental hypotheses and influencing factors, Expenses and Integration Capability, could be identified and shown to be significant. The two main challenges in the market introduction of AAL solutions are therefore the immense expenditure involved and the limited integration capacity. Further challenges lie in the improvement of measures concerning Usability, Customization, Acceptance and Data Security. The elaboration of the collected data sets was carried out using partial least squares structural equation modelling (PLS-SEM).

This paper explores antenna's polarization for Radio-Frequency Identification (RFID) based non-contact human activity detection. For the first time, a cross circular polarization configuration between reader antenna and tag antenna is proposed to increase the sensing range and spatial sensitivity. Compared to conventional RFID configurations - linearly polarized (LP) tag and circularly polarized (CP) reader, a cross CP configuration can improve the signal-to-noise ratio (SNR) which leads to 230% increase in the detection area over traditional approach in our experiment. As the result of the improvement of spatial sensitivity, the proposed approach can detect subtle and small body movements at almost 4.5m from the reader, such as head movements or even respiration, which enables a low-cost, easy-to-use, non-contact and non-intrusive monitoring of the elderly and disabled peoples in places like hospitals and assisted living homes.

The health domain is one of the most researched domains, simply because maintaining good health for humans is an essential aspect for life. But the health domain is not limited to humans, maintaining health for machines is essential if we want to achieve a consistent productive industry. There are different ways to monitor and maintain health for humans and other ways for machines. Internet of Things (IOT) is a new paradigm shift that can be used to monitor both subdomains at the same time. IOT is already being used in Ambient Assisted Living (AAL) and Prognostic Health Management (PHM) systems to achieve certain separate goals. The aim of this paper is to give a brief review of both subdomains showing their technology and how to achieve these systems, aiming to merge these two subdomains into a one big frame work to achieve the full potential of Industry 4.0.

Cameras and dedicated processing enable contactless monitoring of blood volume pulsation by detecting the pulse-induced subtle color variations from human skin surface. This technique, called Camera Photoplethysmography (camera-PPG), has been implemented in the visible and near infrared (NIR) ranges and prototyped for long-term, contactless, and continuous pulse-rate monitoring (24/7), for applications in various areas including clinical settings, assisted living, baby and elderly care, fitness, and automotive settings. In this chapter, we will review the development of the model-based camera-PPG technology. Furthermore, we focus on the latest model-based method that was tested in near-infrared (NIR). By applying the same technology to visible light in the challenging conditions of fitness, we demonstrate that this latest approach has the character of a versatile tool in camera-PPG.

Wireless body area network (WBAN) is growing enormously with the technical development of the Internet of things (IOT). WBAN is one of the types in which sensors are located on the body of the patient. The increasing use of wireless networks and the constant miniaturization of electrical invasive/noninvasive devices have empowered the development of wireless body area networks (WBANs). A WBAN provides a continuous health monitoring of a patient without any constraint on his/her normal daily life activities. Many technologies have proved their efficiency in supporting WBAN applications, such as remote monitoring, biofeedback, and assisted living by responding to their specific quality of service (QoS) requirements. Due to numerous available technologies, selecting the appropriate technology for a medical application is being a challenging task. In this paper, the different medical applications are presented. The most common technologies used in WBANs are highlighted. Finally, a matching between each application and the corresponding suitable technology is studied.

The growing global epidemic of coronavirus, as well as the shortage in health care professionals, impose protection measures to be taken by citizens themselves. One of these measures is social distancing. In an autonomous and assisted living environment a set of technologies can offer protection to the tenants by monitoring the individuals’ distance between them. This paper proposes to leverage the social distancing application in Ambient assisted living environments, following the precautions proposed by the experts against Covid-19. The evaluation results were quite impressive and a high accuracy was achieved as the difference between application’s and real world measurements was up to 5%.

Smart home ecosystems aim to adapt to users' needs and preferences and provide comfort and improved quality of life. Users need access to efficient and effective applications in order to interact with such environments. The target of this paper is to present a mobile application designed and created to support end user interaction with smart home ecosystems; this application is capable of adapting to any smart home, regardless of this architecture and the devices used. We followed a user first design thinking approach to design the user experience of this application and we deployed it on the infrastructure of the Ambient Assisted Living Laboratory, AAL House. Users via our application can monitor sensors and devices, define their routine preferences and view data on energy consumption. In order to study the acceptance and performance of our application, a survey was conducted using a questionnaire.

The present paper provides a review on applications of Low-resolution IR array sensors in Ambient-Assisted Living (AAL) monitoring systems. The review is focused on three main scenarios: indoor people detecting, localizing and counting; fall detection; indoor human activity detection and recognition. A comparative analysis of the reported results in the latest research studies has been carried out in terms of the type and position of the low-resolution IR array sensors used, the employed methods and image processing techniques and the achieved accuracy. The analysis shows that for fall detection and human activity recognition applications, the IR array sensors with $24\times 32$ pixels or multiple sensors arrangements with $4\times 16,\ 16\times 16$ and $24\times 32$ pixels can provide sufficient accuracy and still preserving user privacy.

Discovering routine behavior patterns of the solitary elderly can provide the elderly with better assisted living services. This paper presents a method based on time, path and area association restriction, to calculate the distance between the solitary elderly daily behavior patterns and then discover the routine behavior patterns.

The aging population has resulted in interest in remote monitoring of elderly individuals’ health and well being. This paper describes a simple unsupervised monitoring system that can automatically detect if an elderly individual’s pattern of presence deviates substantially from the recent past. The proposed system uses a small set of low-cost motion sensors and analyzes the produced data to establish an individual’s typical presence pattern. Then, the algorithm uses a distance function to determine whether the individual’s observed presence for each day significantly deviates from their typical pattern. Empirically, the algorithm is validated on both synthetic data and data collected by installing our system in the residences of three older individuals. In the real-world setting, the system detected, respectively, five, four, and one deviating days in the three locations. The deviating days detected by the system could result from a health issue that requires attention. The information from the system can aid caregivers in assessing the subject’s health status and allows for a targeted intervention. Although the system can be refined, we show that otherwise hidden but relevant events (e.g., fall incident and irregular sleep patterns) are detected and reported to the caregiver. ispartof: Sensors vol:21 issue:18 ispartof: location:Switzerland status: Published online

This study aimed to determine the methodological quality of user-centered usability evaluation of Ambient Assisted Living (AAL) solutions by (i) identifying the characteristics of the AAL studies reporting on user-centered usability evaluation, (ii) systematizing the methods, procedures and instruments being used, and (iii) verifying if there is evidence of a common understanding on methods, procedures, and instruments for user-centered usability evaluation. An electronic search was conducted on Web of Science, Scopus, and IEEE Xplore databases, combining relevant keywords. Then, titles and abstracts were screened against inclusion and exclusion criteria, and the full texts of the eligible studies were retrieved and screened for inclusion. A total of 44 studies were included. The results show a great heterogeneity of methods, procedures, and instruments to evaluate the usability of AAL solutions and, in general, the researchers fail to consider and report relevant methodological aspects. Guidelines and instruments to assess the quality of the studies might help improving the experimental design and reporting of studies on user-centered usability evaluation of AAL solutions.

This paper presents a multifunctional interdisciplinary framework that makes four scientific contributions towards the development of personalized ambient assisted living (AAL), with a specific focus to address the different and dynamic needs of the diverse aging population in the future of smart living environments. First, it presents a probabilistic reasoning-based mathematical approach to model all possible forms of user interactions for any activity arising from user diversity of multiple users in such environments. Second, it presents a system that uses this approach with a machine learning method to model individual user-profiles and user-specific user interactions for detecting the dynamic indoor location of each specific user. Third, to address the need to develop highly accurate indoor localization systems for increased trust, reliance, and seamless user acceptance, the framework introduces a novel methodology where two boosting approaches—Gradient Boosting and the AdaBoost algorithm are integrated and used on a decision tree-based learning model to perform indoor localization. Fourth, the framework introduces two novel functionalities to provide semantic context to indoor localization in terms of detecting each user’s floor-specific location as well as tracking whether a specific user was located inside or outside a given spatial region in a multi-floor-based indoor setting. These novel functionalities of the proposed framework were tested on a dataset of localization-related Big Data collected from 18 different users who navigated in 3 buildings consisting of 5 floors and 254 indoor spatial regions, with an to address the limitation in prior works in this field centered around the lack of training data from diverse users. The results show that this approach of indoor localization for personalized AAL that models each specific user always achieves higher accuracy as compared to the traditional approach of modeling an average user. The results further demonstrate that the proposed framework outperforms all prior works in this field in terms of functionalities, performance characteristics, and operational features.

Introduction and Objectives: The aim of this video is to describe the application of augmented reality (AR) technology of three-dimensional (3D) renal model during robot-assisted living do...

From caretaking activities for elderly people to being assistive in healthcare setup, mobile and non-mobile robots have the potential to be highly applicable and serviceable. The ongoing pandemic has shown that human-to-human contact in healthcare institutions and senior homes must be limited. In this scenario, elderlies and immunocompromised individuals must be exclusively protected. Robots are a promising way to overcome this problem in assisted living environments. In addition, the advent of AI and machine learning will pave a way for intelligent robots with cognitive abilities, while enabling them to be more aware of their surroundings. In this paper, we discuss the general perspectives, potential research opportunities, and challenges arising in the area of robots in assisted living environments and present our research work pertaining to certain application scenarios, i.e., robots in rehabilitation and robots in hospital environments and pandemics, which, in turn, exhibits the growing prospects and interdisciplinary nature of the field of robots in assisted living environment.

This chapter proposes and investigates an intelligent context-aware model that adopts a hybrid architecture with both local and cloud-based components to monitor patients, particularly COVID-19 patients with mild symptoms while they are in their homes. The cloud-based part of the system makes storing and processing easier, especially that the data generated by ambient assisted living systems are huge, particularly with patients suffering from chronic diseases and require more frequent readings. On the other hand, the local part of the system monitors the patients in case of any sudden interruptions on the internet or any failure in the cloud system. The proposed model uses context-aware techniques by monitoring different physiological signals, surrounding ambient conditions, along with the patient activities simultaneously to build a better understanding of the health status of the COVID-19 patient in real-time, as the system will help doctors to detect if the patient has symptoms of the COVID-19. The results obtained experimentally prove how our proposed model can effectively monitor patients and detect emergencies accurately in imbalanced datasets through a case study on a patient with normotensive disorder.

In spite of promising contributions of ambient assisted living (AAL) solutions for independent ageing, a gap between technologies and their uptake by users has been reported. A suboptimal user-centred approach and limited user integration in the innovation process were advanced as influencing factors. This paper presents the results from a collaborative workshop conducted at the AAL Forum 2017 with multiple stakeholders (n = 24). Data were collected on current practices, methods, challenges and solutions regarding user integration in AAL projects, and a content analysis was carried out. A moderate degree of user integration emerged as the most common practice and users are mostly included in the research and development process providing requirements and testing the solutions, holding minor participation in other innovation phases. Challenges for user integration were associated with users’ health/autonomy status and attitudes towards technology, as well as with obstacles in matching solutions with users’ requirements. The results suggest that a higher degree of user integration may still be some way distant in AAL projects. Researchers/developers face considerable challenges in user integration, while possessing a limited portfolio of solutions to address them. Insights from this paper can be exploited to design guidelines for user integration in AAL projects.

The elderly suffer from discomfort in social life owing to the decline of their physical, psychological, and social functions. The elderly who spend a lot of time indoors require an intelli...

With the world population aging at a fast rate, ambient assisted living systems focused on elderly people gather more attention. Human activity recognition (HAR) is a component connected to those systems, as it allows identification of the actions performed and their utilization on behavioral analysis. This paper aims to provide a review on recent studies focusing on HAR and abnormal behavior detection specifically for seniors. The frameworks proposed in the literature are presented. The results are also discussed and summarized, along with the datasets and metrics used. The absence of a universal evaluation framework makes direct comparison not feasible, thus an analysis is made trying to divide the literature using a taxonomy. Solutions on the challenges identified are proposed, while discussing future work.

Background: The eye features like eye-blink and eyeball movements can be used as a module in assisted living systems that allow a class of physically challenged people speaks – using their eyes. The objective of this work is to design a real-time customized keyboard to be used by a physically challenged person to speak to the outside world, for example, to enable a computer to read a story or a document, do gaming and exercise of nerves, etc., through eye features tracking Method: In a paralyzed person environment, the right-left, up-down eyeball movements act like a scroll and eye blink as a nod. The eye features are tracked using Support Vector Machines (SVMs). Results: A prototype keyboard is custom-designed to work with eye-blink detection and eyeball-movement tracking using Support Vector Machines (SVMs) and tested in a typical paralyzed person-environment under varied lighting conditions. Tests performed on male and female subjects of different ages showed results with a success rate of 92%. Conclusions: Since the system needs about 2 seconds to process one command, real-time use is not required. The efficiency can be improved through the use of a depth sensor camera, faster processor environment, or motion estimation.

Nowadays, accelerometers have become very popular sensors because they offer new possibilities for developing humancentered applications in different domains such as ambient assisted living or rehabilitation [1]. These sensors allow monitoring human activities [2] in a non-invasive way because they are included in many smart devices, like smartphones, smartwatches or fit bands.

Ambient intelligence (AmI) is intrinsically and thoroughly connected with artificial intelligence (AI). Some even say that it is, in essence, AI in the environment. AI, on the other hand, owes its success to the phenomenal development of the information and communication technologies (ICTs), based on principles such as Moore’s law. In this paper we give an overview of the progress in AI and AmI interconnected with ICT through information-society laws, superintelligence, and several related disciplines, such as multi-agent systems and the Semantic Web, ambient assisted living and e-healthcare, AmI for assisting medical diagnosis, ambient intelligence for e-learning and ambient intelligence for smart cities. Besides a short history and a description of the current state, the frontiers and the future of AmI and AI are also considered in the paper.

Activity recognition(AR) is a popular subject of research in the recent past. Recognition of activities performed by human beings, enables the addressing of challenges posed by many real-world applications such as health monitoring, providing security etc. Segmentation plays a vital role in AR. This paper evaluates the efficiency of Area-Based Segmentation using different performance measures. Area-Based segmentation was proposed in our earlier research work. The evaluation of the Area-Based segmentation technique is conducted on four real world datasets viz. Aruba17, Shib010, HH102, and HH113 comprising of data pertaining to an individual, living in the test bed home. Machine learning classifiers, SVM-R, SVM-P, NB and KNN are adopted to validate the performance of Area-Based segmentation. Amongst the four chosen classification algorithms SVM-R exhibits better in all the four datasets. Area-Based segmentation recognise the four test bed activities with accuracies of 0.74, 0.98, 0.66, and 0.99 respectively. The results reveal that Area based segmentation can efficiently segment sensor data stream which aids in accurate recognition of smart home activities.

Electricity disaggregation is the process of separating the total electrical load of a single household into appliance specific loads. This can be achieved either by intrusive monitoring of each appliance via individual device load meters or by employing Non-Intrusive Load Monitoring (NILM) techniques, that perform a detailed analysis of the current and voltage of the total load, typically measured at the interface to the power source. NILM techniques are becoming recognized tools for several applications, from which their use in Home Energy Management Systems and Ambient Assisted Living clearly stand out. The objective of this paper is that, by reviewing the most recent research and development in NILM and in its applications, to foster further interest in this technology.

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.

International audience; Human action recognition in videos is an important issue in computer vision. We propose an approach based on the integration of partial or global human body segmentation in the classification process to deal with partial movements and immobility. Experimentation on UCF101 public dataset output competitive recognition accuracy related state of the art.

The shortage in health care professionals is expected to create demands for Ambient Assisted Living (AAL) technology that helps to prolong independent living of individuals facing various challenges (e.g., the elderly, people with various disabilities, etc.). It will allow health care professionals to remotely monitor and support these individuals more efficiently. Our proposal is to leverage Ambient Intelligence in AAL environments, by exploiting all the recent advances in AI/ML.