Public health organizations play a crucial role in disease prevention, health promotion, and health education. However, they face significant challenges due to fragmented data sources, delayed data collection, and inadequate analytical tools. This paper proposes a comprehensive, data-driven socio-economic model that empowers public health agencies with a global overview of disease spread and community health trends. By deploying advanced AI-driven models, the proposed solution enables predictive analytics, uncovering patterns and risk factors to forecast disease outbreaks and prioritize health interventions effectively. The system was deployed in five pilot sites (Barcelona, Birmingham, New York, Paris, and Singapore) focusing on asthma as a case study, collecting health and environmental data from 1,832 participants. Through this deployment, we demonstrated the potential for real-time data collection, AI insights and predictive analytics to enhance health outcomes and decision-making.

An Ant Colony Optimization (ACO) based approach enhances trip planning by dynamically selecting routes that are adapted to end users. As the global population ages, older adults face various physical, sensory, and cognitive challenges that affect their mobility. In our research we adopted ACO tailored for age-friendly trip planning using the Isula framework. Our proposed method focuses on optimizing trip routes by considering older adult related factors such as distance and slope, which are critical for this population. The method was applied in the first stage to the Lennoxville district of Sherbrooke, Quebec, with promising results.

Abstract Dashboards are powerful electronic tools that can provide actionable insights for healthcare professionals, especially in support of the increasing senior population. With advancements in technology and IoT infrastructure, remote patient monitoring has become a feasible option for healthcare professionals through dashboards. To best serve the diverse needs of healthcare professionals, dashboards should be tailored for each user, considering their roles, interests, and priorities. In this study we proposed AMI-Dash, a solution allows for dynamic design and information visualization to address the diversity in needs and priorities among different dashboard users while maintaining a high-level of performance, as evaluated through several technical aspects.

Abstract Advancements on the Internet of Things (IoT) have enabled the development of advanced monitoring systems that can track human behavior and vital signs in real-time, which can have a real impact in the way healthcare is provided. This paper presents a system implementation to monitor and analyze a subject's behavior changes over time using IoT, with the objective of detecting the impact of an inhibitor drug on the subject's activity levels. In this research we present a case study by which we showed it is possible to follow the effect of an anticholinergic drug by means of an unobtrusive IoT system. We have monitored the physical activity of a subject in his residence for seven consecutive months to study the effect of the inhibiting drug doses introduced at three known specific timestamps. Following, we compared our detection results for the subject’s physical activity change timestamps with the medical staff medication doses timestamps. Our results show that we can detect the physical activity change at close timestamps compared to those indicated by the medical staff.

Abstract The fast growth of the IoT and the unlimited possibilities in terms of applications and processing brought forth by the 5G which is around the corner is making IoT an active part of the activity of daily living. Those massive architectures become then the target of security issues, broken services, broken hardware or malfunctioning of applications. Moreover, in a system of million connected devices, operating each one of them is impossible making the platform unmaintainable. In this study, we present our attempt to achieve the autonomy of IoT infrastructure and we present some of the existing and recurrent issues undermining the IoT architecture. Then we review existing self-healing techniques that enable a system to be autonomous and solve issues. We also described our IoT platform that targets the self-healing concern. Finally, we point out some recommendations to make an overall reliable, resilient IoT system with cognitive entities for self-management.

Abstract The rapid advancement of the Internet of Things (IoT) has reshaped the industrial system, agricultural system, healthcare systems, and even our daily livelihoods, as the number of IoT applications is surging in these fields. Still, numerous challenges are imposed when putting in place such technology at large scale. In a system of millions of connected devices, operating each one of them manually is impossible, making IoT platforms unmaintainable. In this study, we present our attempt to achieve the autonomy of IoT infrastructure by building a platform that targets a dynamic and quick Plug and Play (PnP) deployment of the system at any given location, using predefined pipelines. The platform also supports real-time data processing, which enables the users to have reliable and real-time data visualization in a dynamic dashboard.

Abstract Mobility is essential for older adults to keep a good level of socialization, health and well-being. Still, aging is often accompanied by multiple mobility-related challenges. Hence, these mobility limitations make travel and use of public transport a big challenge. Numerous mobility trip planner tools can be found now days. However, they are not adapted to older adults’ needs and preferences. We discuss in this paper our attempt to develop a one-stop platform to help older adults in their mobility where, when, and how they want. We introduce our co-creation approach, our software architecture, as well as our first prototype.

Contactless monitoring of vital signs, e.g., heart rate (HR) attracts the researcher's attention due to its convenience and affordability. Among others, under-mattress ballistocardiogram (BCG) sensors have proved effective for contactless remote monitoring of HR. Nevertheless, HR detection from BCG sensors is a challenging task because the signal morphology can vary between and within-subjects. In this paper, we studied the potential of two wavelet-based methods, i.e., the multiresolution analysis of the maximal overlap discrete wavelet transform (MODWT-MRA) and continuous wavelet transform (CWT) for HR detection via a microbend fiber optic sensor (MFOS). BCG signals were gathered from ten sleep apnea patients during overnight polysomnography (PSG) study. The MFOS was placed under the bed mattress and the PSG electrocardiogram (ECG) signals were used as a reference to evaluate the proposed HR detection algorithms. Overall, CWT with derivative of Gaussian provided (Gaus2) slightly better results compared with the MODWT-MRA, CWT (frequency Bspline), and CWT (Shannon). Across the ten patients, the mean absolute error, mean absolute percentage error, and root mean square error metrics were as follows: 4.71 (1.22), 7.58% (2.17), and 5.58 (1.20), and 4.71 (1.07), 7.61% (1.65), and 5.59 (1.02), for Gau2 and MODWT-MRA, respectively. That said, the total precision for MODWT-MRA, i.e., 80.22 (19.01) was higher than Gaus2, i.e., 78.83 (17.84).

Aging often involves a significant change in roles and social positions. The greatest health risk for seniors is the adoption of a sedentary lifestyle that causes isolation, depression, and many diseases. However, convincing an older adult to regularly do physical activities is not generally a simple mission. This paper proposes a personalized and contextualized persuasion system to promote physical activities for older adults. In fact, our approach considers the personal and health profile of the older adult. It also considers different context parameters (context-awareness). This intelligence is guaranteed thanks to the use of the semantic modeling and reasoning, which from different types of information would be able to decide the best moment to trigger notifications from our persuasive system to the participating older adults.

Ballistocardiogram signals describe the mechanical activity of the heart. It can be measured by an intelligent mattress in a totally unobtrusive way during periods of rest in bed or sitting on a chair. The BCG signals are highly vulnerable to artefacts such as noise and movement making useful information like respiratory activities difficult to extract. The purpose of this study is to investigate a classification method to distinguish between seven types of respiratory activities such as normal breathing, cough and hold breath. We propose a feature selection method based on a spectral analysis namely spectral flatness measure (SFM) and spectral centroid (SC). The classification is carried out using the nearest neighbor classifier. The proposed method is able to discriminate between the seven classes with the accuracy of 94% which shows its usefulness in context of Telemedicine.

Although emerging IoT paradigms in sleep tracking have a substantial contribution to enhancing current healthcare systems, there are several privacy and security considerations that end-users need to consider. End-users can be susceptible to malicious threats when they allow permission to potentially vulnerable or leaky third-party apps. Since the data is migrated to the cloud, it goes over insecure communication channels, all of which have their security concerns. Moreover, there are alternative data violation concerns when the data projects into the proprietor’s cloud storage facility. In this study, we present some of the existing IoT sleep trackers, also we discuss the most common features associated with these sleep trackers. As the majority of end-users are not aware of the privacy and security concerns affiliated with emerging IoT sleep trackers. We review existing solutions that can apply to IoT sleep tracker architecture. Also, we describe a deployed IoT platform that can address these concerns. Finally, we provide some of the recommendations to end-users and service providers to ensure a safer approach while leveraging the IoT sleep tracker in caregiving. This incorporates recommendations for software updates, awareness programs, software installation, and social engineering.

Today, numerous factors are causing a demographic change in many countries in the world. This change is producing a nearly balanced society share between the young and aging population. The noticeable increasing aging population is causing different economical, logistical and societal problems. In fact, aging is associated with chronic diseases in addition to physical, psychological, cognitive and societal changes. These changes are considered as indicators of aging peoples’ frailty. It is therefore important to early detected these changes to prevent isolation, sedentary lifestyle, and even diseases in order to delay the frailty period. This paper presents an experiment deployment of an Internet of Thing solution for the continuous monitoring and detection of elderly people’s behavior changes. The objective is to help geriatricians detect sedentary lifestyle and health-related problems at an early stage.

We discuss in this paper a study of a smart and unobtrusive mattress in a clinical setting on a population with cardiorespiratory problems. Up to recently, the vast majority of studies with unobtrusive sensors are done with healthy populations. The unobtrusive monitoring of the Respiratory Rate (RR) is essential for proposing better diagnoses. Thus, new industrial and research activity on smart mattresses is targeting respiratory rate in an Internet-of-Things (IoT) context. In our work, we are interested in the performances of a microbend fiber optic sensor (FOS) mattress on 81 subjects admitted in the Cardiac Intensive Care Unit (CICU) by estimating the RR from their ballistocardiograms (BCG). Our study proposes a new RR estimator, based on harmonic plus noise models (HNM) and compares it with known estimators such as MODWT and CLIE. The goal is to examine, using a more representative and bigger dataset, the performances of these methods and of the smart mattress in general. Results of applying these three estimators on the BCG show that MODWT is more accurate with an average mean absolute error (MAE) of $$1.97\,\pm \,2.12\;\text {BPM}$$ . However, the HNM estimator has space for improvements with estimation errors of $$2.91 \pm 4.07\;\text {BPM}$$ . The smart mattress works well within a standard RR range of 10–20 breaths-per-minute (BPM) but gets less accurate with a bigger range of estimation. These results highlight the need to test these sensors in much more realistic contexts .

Kitchen safety is a highly important concern for daily living activities. Cooking, usually, is accompanied with several risks particularly for elderly people, due to aging associated impairments. Therefore, cooking-safe environment is required to enhance safety. In this paper, we present our cooking-safe smart oven system which manages the detection of risk situations and determines their severity levels according to the contextual information around oven. The context is gathered via sensors deployed in the kitchen environment. The cooking-safe system is composed of sensor nodes, actuators, microcontroller, and a computing unit. Cooking related risks are managed by the fuzzy logic based reasoning engine of the cooking-safe system. We also present in details our risk prevention algorithms which constitute the basic concepts of the reasoning engine. We discuss the system evaluation in real-world environment, and the interventions via interactive interfaces with users.

Presents author name corrections in the paper, “Adaptable context-aware cooking-safe system,” (Yared, R. and Abdulrazak, B.), IEEE Trans. Services Comput., vol. 11, no. 2, pp. 236–248,Mar./Apr. 2018.

Rule-based reasoning engines have proven their value for Context-Awareness in Ambient Intelligence. However, the definition of rules is often prone to human-caused inconsistencies or sensor failures. In this paper, we propose to assist the definition of rules for context-awareness by automatically validating the consistency of the rules. Using our approach, we are able to detect conflicts between rules for context-awareness, as well as recommend adding sensors to improve the reasoner outcomes. We propose to define rules as a finite set of conditions to be verified, and provide consistency-checking capabilities to these conditions. We discuss in this paper our approach as well as results and validation.

Modeling frailty is a key issue for better addressing the needs of aging people. In our research, we introduce a multidimensional frailty model to quantify the level of frailty of aging people in Ambient Assisted Living solutions. The proposed frailty model takes into account three dimensions: activities of daily living, physical, and social. The proposed model has been harmonized with the existing qualitative medical assessments and the International Classification of Functioning, Disability, and Health (IFC). The model has been tested through the deployment of UbiSMART (an AAL framework) during 16 months in 8 real scenarios of aging in place (homes and nursing homes). In this paper, we present the process to convert sensor data into numerical frailty values: the definition of the frailty items, the technical design, the implementation, and the numerical treatment of the data. We also present the evaluation of the model and the results obtained during the deployments.

A Rapid deployment solution, which enables to discover surrounding context and construct the semantic knowledge, is a major enabler for large scale deployment of Ambient Assisted Living (AAL)1 solutions in aging people environments. Therefore, we built UbiSMART framework, an AAL ontology/web solution based on a shared cloud-based semantic reasoner, to enable easy and quick deployment of hardware and software component. UbiSMART uses two ontologies: 1) a generic ontology (abstract model) that contains the shared properties and 2) a specific ontology (instance model) that contains the details of each end-user environment. UbiSMART is complemented by a simple and user-friendly services to populate and modify the instance model. This solution does not need any prior knowledge of the system or technical expertise. In this paper, we present the description of our approach, the technical design, and its implementation. We also detail the validation in real scenarios and the obtained results, as well as the lessons learned and the future perspectives.

This document presents our proposal for a workshop on service delivery/provision in smart environment. The proposed type of workshop is a mini-track, with expected 10 to 12 authors/participants, divided in three presentation sessions.

Service discovery is very important in realizing the concept of pervasive computing. Consequently, service discovery protocols must be able to work in the heterogeneous environment offered by pervasive computing. Remote service discovery in particular has not been properly achieved so far. In an attempt to remedy this we propose a new architecture for enabling typical (local) service discovery mechanisms (without the ability of remote service discovery) to discover services remotely. Our architecture uses Universal Plug and Play (UPnP) as an example of local service discovery protocols, and Gnutella as an example of peer-to-peer distributed search protocols. We introduce a module called service mirror builder to the UPnP protocol, and a remote communication protocol over a Gnutella network. As a consequence, UPnP networks become able to discover services in remote networks (that is, remote service discovery).

Complexity in ubiquitous environments is an important problem that leads to high implementation and management costs. In some cases, the complexity of the environments' systems are so important that it become impossible for a single human to understand or manage them. The software self-organization simplifies this complexity by reducing the user manipulations and reasoning on the ubiquitous environments' system during software management and organization. This paper presents the self-organization middleware model and architecture, as well as results coming from tests and validation scenarios carried out on the middleware implementation.

Autonomic Computing Self-Configuration /Organization can simplify the complexity of software deployment, management and utilization in pervasive environments such as smart spaces. Software management complexity in pervasive environments is a serious problem, which leads to high management costs and slow down the broad utilization of the pervasive technologies. This paper presents our work on a self-organization middleware that reduce user manipulations and reasoning during software deployment and organization in smart spaces. Our approach is based on Fuzzy Logic and semantic description in order to support the self-organization reasoning on the environment context.

Context-aware software provides adapted services to users or other software components. On the other hand, Autonomic Pervasive Computing uses Context to reduce the complexity of pervasive system utilization, management and maintenance. This paper introduces two Context-awareness models, the macro and micro approaches, that define and integrate contextual views of individual pervasive components (micro level) and global knowledge of the system (macro level). These models are presented and compared with respect to their ability to meet the requirements of the Autonomic Pervasive Computing concept of the four selves.

The growth of ubiquitous computing and its integration in smart environments constitutes a major challenge. However, many issues related to this technology were raised, and many efforts have been made to facilitate the implementation of such technology. Information management, sharing and reuse are examples of these issues. Ontologies are used in this context as promising solution to deal with these issues. Nevertheless, ontologies developed to date in the context of smart environments are limited and do not address all aspects of smart environments such as the problem of referentiality and the environmental change. In this paper, we propose a universal ontology for smart environments aiming firstly to overcome the limitations of the existing ontologies, and secondly extend its capabilities by adding new environmental aspects such as those mentioned previously. In this paper, we also demonstrate how our ontology can be used to describe domains as well as applications.

The Autonomic Pervasive Computing can simplify the complexity of the configuration, maintenance and management of pervasive environments such as smart spaces. The Pervasive Computing is a paradigm where the information processes are distributed in the environment components, bringing processes more closer to the information context. On the other hand, the Autonomic Computing allows to simplify the complexity caused by the heterogeneity of the space components, there large number, the dynamic relations between them, etc.

Smart living spaces provide autonomy and assistance to people with disabilities and elderly people in order to increase their independence. In this paper we present our approach to personalize a complex living environment for users with special needs. It provides an appropriate middleware for supporting assistive services in dynamic environments. It is built upon formalisms based on Description logic (DL) named Semantic Matching Framework (SMF) and designed on the matching of two models: an environment model, and a user model. SMF was implemented and integrated into a demonstrator, which is validated under laboratory conditions. Performance results are presented to highlight the time response of SMF.

Approaching Context-awareness problems in Open intelligence space requires applications that work outside of traditional Pervasive space boundaries. Context-aware software built for Open Intelligent Space can follow users wherever they go, with varying quality of service. We present our vision: Controlled intelligent spaces are a special case of Open intelligent spaces; software designed for the latter covers the needs of the former; an agent-centered and choreographed approach is preferable to a user-centered and orchestrated approach in Open space.

The design of robot dedicated to person with disabilities necessitate users implication in all steps of product development: design solution, prototyping the system, choice of users interfaces, and testing it with users in real conditions. However, before any design of any system, it is necessary to understand and meet the needs of the disabled users. In this paper, we describe our research activity on the integration of a robotic arm in the environment of disabled people who have lost the abilities to use their proper arms to perform daily living tasks and who are able to use an adapted robot to compensate, even partly, the problems of manipulation of objects in their environments. This paper presents our contribution for designing adaptable, configurable and personalized robot system based on the Manus robot. Some preliminary evaluation results are also presented.