Ambient Intelligence Environments Research Articles

Smart Healthcare: Exploring the Internet of Medical Things with Ambient Intelligence

Ambient Intelligence (AMI) represents a significant advancement in information technology that is perceptive, adaptable, and finely attuned to human needs. It holds immense promise across diverse domains, with particular relevance to healthcare. The integration of Artificial Intelligence (AI) with the Internet of Medical Things (IoMT) to create an AMI environment in medical contexts further enriches this concept within healthcare. This survey provides invaluable insights for both researchers and practitioners in the healthcare sector by reviewing the incorporation of AMI techniques in the IoMT. This analysis encompasses essential infrastructure, including smart environments and spectrum for both wearable and non-wearable medical devices to realize the AMI vision in healthcare settings. Furthermore, this survey provides a comprehensive overview of cutting-edge AI methodologies employed in crafting IoMT systems tailored for healthcare applications and sheds light on existing research issues, with the aim of guiding and inspiring further advancements in this dynamic field.

IoT forensics in ambient intelligence environments: Legal issues, research challenges and future directions

Due to the abundance of the Internet of Things (IoT), smart devices are widely utilized which helps to manage human surroundings and senses inside and outside environments. The huge amount of data generated from the IoT device attracts cyber-criminals in order to gain information from the significant relationship between people and smart devices. Cyber-attacks on IoT pose a severe challenge for forensic experts. Researchers have invented many techniques to solve IoT forensic challenges and to have an in-depth knowledge of all the facts internal as-well-as external architecture of IoT needs to be understood. In this paper, an attempt has been made to understand the relationship between security and forensics incorporating its strengths and weaknesses, which has not been explored till date to the best of our knowledge. An attempt has also been made to classify literature into three categories: physical level, network level, and cloud level. These include evidence sources, areas of IoT forensics, potential forensic information, evidence extraction techniques, investigation procedures, and legal issues. Also, some prominent IoT forensic use cases have been recited along with providing the key requirements for forensic investigation. Finally, possible research problems in IoT forensic have been identified.

Flexible gesture input with radars: systematic literature review and taxonomy of radar sensing integration in ambient intelligence environments

Flexible gesture input with radars: systematic literature review and taxonomy of radar sensing integration in ambient intelligence environments

Cybersecurity Issues of IoT in Ambient Intelligence (AmI) Environment

Ambient intelligence (AmI) refers to the ability of devices to interact seamlessly with their surroundings. An increased use of IoT in ambient intelligence has led to a heightened concern for cybersecurity. Vulnerabilities in the software or firmware of IoT devices have been exploited by hackers to gain control of the devices or the networks. They could also use ambient intelligence systems to collect sensitive data from IoT devices. The problem of IoT security is twofold. First, it concerns the safety of the embedded IoT system and the protection of embedded data. To protect the devices, it's essential to understand the various types of attacks that are possible and to deploy appropriate security measures. This article addresses these issues by providing an overview of potential security risks and the latest solutions and approaches that can help to tackle these problems.

Notice of Violation of IEEE Publication Principles: Secured Wireless Energy Transfer for the Internet of Everything in Ambient Intelligent Environments

Notice of Violation of IEEE Publication Principles<br>BR>"Secured Wireless Energy Transfer for the Internet of Everything in Ambient Intelligent Environments," <br>by Dinesh Kumar Sah; M. Poongodi; Praveen Kumar Donta; Mounir Hamdi; Korhan Cengiz; M. M. Kamruzzam, Hafiz Tayyab Rauf <br>in IEEE Internet of Things Magazine, vol. 5, no. 1, pp. 62-66, March 2022 <br> After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE’s Publication Principles. <br><br>This paper contains significant portions of text from the paper cited below that were paraphrased without attribution. BR><br>"Massive Wireless Energy Transfer: Enabling Sustainable IoT Toward 6G Era," <br>by O. L. A. López, H. Alves, R. D. Souza, S. Montejo-Sánchez, E. M. G. Fernández and M. Latva-Aho, <br><br>in IEEE Internet of Things Journal, vol. 8, no. 11, pp. 8816-8835, June 2021 BR><br> <br/> The Internet of Everything (IoE), as the primary driving force in our society's digital transformation, constitutes resource-constrained physical objects connected as a network. Energy provisioning is one of the bottlenecks to these resource-constrained devices because they operate using limited power batteries. Wireless energy transfer (WET) is a promising technology to address the issue because it can transfer power without any physical connection between the energy-constrained devices and the power sources. WET is equipped with an unmanned aerial vehicle (UAV), and it can travel in an ambient intelligence environment (AIE) to recharge the batteries of IoE. There are several challenges imposed while scheduling the UAVs for charging and discharging in the AIE. In this context, this article discusses various challenges imposed while scheduling the WET-equipped UAVs in the network, including privacy and security vulnerabilities. This article also compares the performance variance while using different energy sources for WET, along with the future scope.

Ambient Intelligence System Enabling People With Blindness to Develop Electrotechnical Components and Their Drivers

Along with the development of new assistive technologies, including ambient intelligence (AmI) environments, new job opportunities are created for people with blindness or visual impairment. Whereas research on software development for people with blindness has been conducted since the 1960s, the design and development of electrotechnical equipment still lacks any way to compensate for the disability which would enable people with blindness to perform even the activities pertaining to this field. This article aims to introduce these new technological procedures enabling the compensation of the disability of people with blindness when designing and developing electrotechnical components (or prototypes) and their drivers by using the AmI system RUDO modules and other tools. This includes the modules enabling measuring electrotechnical quantities, tracing of oscilloscope curves, and ensuring a unified user interface for programming and operation of the devices connected. Other approaches are introduced herein that focused on the production of mechanical components for device construction. This article also contains an illustrative video of practical use of these technological procedures by a person with blindness. To assess the usability of the designed technological procedures to the work of a designer with blindness and one without visual impairment, the cognitive walkthrough method was used. The main contribution of this article is to broaden the knowledge base by the principles of involving people with blindness in the development and construction of electrotechnical components (or prototypes). These new possibilities can be used practically, for instance, in computer education, which can offer new curricula for visually impaired students and focused on more practical issues where hardware and software approaches meet.

Clinically-validated technologies for assisted living

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.

An edge detection–based eGAN model for connectivity in ambient intelligence environments

In generative adversarial networks (GANs), a generator network and discriminator network compete in deep learning tasks to generate real images. To reduce the difference between the generated image and actual image, an edge GAN (eGAN) model using edge detection was proposed. This eGAN model can utilize ambient intelligence, a human-centered technology that includes IoT, smart cities, and autonomous driving. Ambient intelligence is essential for the interconnection between humans and objects. The eGAN model was used to make this connectivity more accurate and reliable. Edge detection is an edge feature that extracts the boundaries of an image and generate images in a fast manner; however, because its threshold is arbitrarily set, the connectivity may be unstable. To solve this problem and improve the performance of the eGAN model, we analyzed various GAN models and edge detection methods and proposed a new edge detection technology using threshold settings. This edge detection method sets the threshold value for images, thereby increasing the accuracy of edge connection and reducing the loss error between the image generated by the eGAN model and actual image. To evaluate the performance of the eGAN model, the error between the generated image and actual image was compared by applying the GAN and eGAN models to the same image dataset. Consequently, it was found that the performance of the eGAN model improved by 21% in comparison to the existing GAN model.

Improving Stress Management and Sleep Hygiene in Intelligent Homes

High stress levels and sleep deprivation may cause several mental or physical health issues, such as depression, impaired memory, decreased motivation, obesity, etc. The COVID-19 pandemic has produced unprecedented changes in our lives, generating significant stress, and worries about health, social isolation, employment, and finances. To this end, nowadays more than ever, it is crucial to deliver solutions that can help people to manage and control their stress, as well as to reduce sleep disturbances, so as to improve their health and overall quality of life. Technology, and in particular Ambient Intelligence Environments, can help towards that direction, when considering that they are able to understand the needs of their users, identify their behavior, learn their preferences, and act and react in their interest. This work presents two systems that have been designed and developed in the context of an Intelligent Home, namely CaLmi and HypnOS, which aim to assist users that struggle with stress and poor sleep quality, respectively. Both of the systems rely on real-time data collected by wearable devices, as well as contextual information retrieved from the ambient facilities of the Intelligent Home, so as to offer appropriate pervasive relaxation programs (CaLmi) or provide personalized insights regarding sleep hygiene (HypnOS) to the residents. This article will describe the design process that was followed, the functionality of both systems, the results of the user studies that were conducted for the evaluation of their end-user applications, and a discussion about future plans.

An adaptive model to support biofeedback in AmI environments: a case study in breathing training for autism

Biofeedback systems have shown promising clinical results in regulating the autonomic nervous system (ANS) of individuals. However, they typically offer a “one-size-fits-all” solution in which the personalization of the stimuli to the needs and capabilities of its users has been largely neglected. Personalization is paramount in vulnerable populations like children with autism given their sensory diversity. Ambient intelligence (AmI) environments enable creating effective adaptive mechanisms in biofeedback to adjust the stimuli to each user’s performance. Yet, biofeedback models with adaptive mechanisms are scarce in the AmI literature. In this paper, we propose an adaptive model to support biofeedback that takes the user’s physiological data, user’s adherence to therapy, and environmental data to personalize its parameters and stimuli. Based on the proposed model, we present EtherealBreathing, a biofeedback system designed to help children with autism practice box breathing. We used the data from 20 children with autism using EtheralBreating without adaptation mechanisms to feed an adaptive model that automatically adapts the visual and audible stimuli of EtherealBreathing according to changes in each user’s physiological data. We present two scenarios showing how EtherealBreathing is capable of personalizing the stimuli, difficulty level, or supporting the therapist decisions. Results are promising in terms of performance and personalization of each user model, showing the importance of personalization for AmI technology. Finally, we discuss challenges and opportunities in using adaptive models to support biofeedback in AmI environments.

User interface design patterns and ontology models for adaptive mobile applications

Mobile applications are an essential element in pervasive and ubiquitous computing, and they face many challenges during their generation process from the analysis of user needs to the design of specific mobile interfaces and their development in several technological platforms. Moreover, the rise of Ambient Intelligent and context-aware environments also introduces multiple interaction aspects to be considered when using mobile devices in this kind of scenarios. The present work seeks to examine the role of design patterns and ontology models in order to help with the generation of mobile applications, which can be adapted at runtime to the various user needs, different context scenarios, interactive design modes, or technology requirements. In this way, an ontology-based framework is introduced to represent, design, and support the adaptation of user interfaces in mobile applications by using design patterns according to these user needs or preferences and the context around them. This framework provides developers with a client-server architecture that enables the access to an expert knowledge base of user, context, and pattern information together with a set of inference rules, which allow the dynamic selection of interface design patterns and the runtime adaptation of the user interface features. These ontology models and inference rules are key components of the proposed framework, and their implementation has helped to produce an example of mobile application supporting user interface adaptation processes for disabled people, which can be required in Ambient Intelligent environments. Three examples of user scenarios have been considered to assess the framework potential, and usability dimensions have been tested by a limited set of users through the produced mobile application, making the usefulness of generated adaptive user interfaces apparent.

Recognizing Activities of Daily Living using 1D Convolutional Neural Networks for Efficient Smart Homes

Human activity recognition is considered a challenging task in sensor-based monitoring systems. In ambient intelligent environments, such as smart homes, collecting data from ambient sensors is useful for recognizing activities of daily living, which can then be used to provide assistance to inhabitants. Activities of daily living are composed of complex multivariable time series data that has high dimensionality, is huge in size, and is updated constantly. Thus, developing methods for analyzing time series data to extract meaningful features and specific characteristics would help solve the problem of human activity recognition. Based on the noticeable success of deep learning in the field of time series classification, we developed a model called a deep one-dimensional convolutional neural network (Deep 1d-CNN) for recognizing activities of daily living in smart homes. Our model contains several one-dimensional convolution layers coupled with max-pooling technique to learn the internal representation of time series data and automatically generate very deep features for recognizing different activity types. For the performance evaluation, we tested our deep model on the new real-life dataset, ContextAct@A4H, and the results showed that our model achieved a high F1 score (0.90). We also extended our study to show the potential energy saving in smart homes through recognizing activities of daily living. We built a recommendation system based on the activities recognized by our deep model to detect the devices that are wasting energy, and recommend the user to execute energy optimization actions. The experiment indicated that recognizing activities of daily living can result in energy savings of around 50%.

Discovering Hidden Mental States in Open Multi-Agent Systems by Leveraging Multi-Protocol Regularities with Machine Learning.

The agent paradigm and multi-agent systems are a perfect match for the design of smart cities because of some of their essential features such as decentralization, openness, and heterogeneity. However, these major advantages also come at a great cost. Since agents’ mental states are hidden when the implementation is not known and available, intelligent services of smart cities cannot leverage information from them. We contribute with a proposal for the analysis and prediction of hidden agents’ mental states in a multi-agent system using machine learning methods that learn from past agents’ interactions. The approach employs agent communication languages, which is a core property of these multi-agent systems, to infer theories and models about agents’ mental states that are not accessible in an open system. These mental state models can be used on their own or combined to build protocol models, allowing agents (and their developers) to predict future agents’ behavior for various tasks such as testing and debugging them or making communications more efficient, which is essential in an ambient intelligence environment. This paper’s main contribution is to explore the problem of building these agents’ mental state models not from one, but from several interaction protocols, even when the protocols could have different purposes and provide distinct ambient intelligence services.

Mixed-dependency models for multi-resident activity recognition in smart homes

Recent growing interest in ambient intelligent environments has driven a desire for effective models to reason about activities of multiple residents. Such models are the keystone for the future of smart homes where occupants can be assisted with non-intrusive technologies. Much attention has been put on this research, however current works tend to focus on developing statistical algorithms for prediction, whilst there still lacks a study to fully understand the relations of residents’ behaviours and how they are reflected through the sensors’ states. In this paper we investigate the dependencies of the activities from residents and their interaction with the environments. We represent such dependencies in Bayesian networks that leads to construction of six variants of Hidden Markov Models (HMMs). Furthermore, we argue that a complete model should embody more than one type of dependency. Therefore, we propose an ensemble of HMMs, and then generalize it to a novel mixed-dependency model. In the experiments we perform intensive evaluation of our study on multi-resident activity recognition task. The results show that the proposed models outperform other models in three smart home environments, thus asserting our hypothesis.

Recognizing Activities of Daily Living in Ambient Intelligence Environments using 1D Convolutional Neural Networks

Recognizing Activities of Daily Living in Ambient Intelligence Environments using 1D Convolutional Neural Networks

Ambient Intelligence At-Home Laboratory for Human Everyday Life

The relation of artificial intelligence (AI) and human intelligence (HI) still needs better understanding. In this article, the authors develop the fourth brain concept where AI is considered as a specific form of HI evolution. The concept development considers evolutionary and ontogenetic insights on intelligence. The forth brain can be implemented using ambient intelligence (AMI) environments that surround humans in everyday life. AMI environment allows scaffolding and other assistance information services to compensate for the HI function decay. This article proposes an at-home laboratory (AHL) design where an AMI environment is deployed at home and using everyday devices. In this setting, AHL provides a functionally equivalent alternative to the professional healthcare laboratory, though less accurate and advanced in regard to data measurements and the assistance information value.

Auditory Noise Can Facilitate Body's Peripheral Temperature Switchovers

Home is the context of an ambient-intelligence environment. Nonetheless, one can downsize the environment. For example, the human body as an environment, and by reading all possible bio-signals, this article can create control loops where many of these bio-signals can be used as sensory inputs to make humans aware of their current perceptual-cognitive state. In this article, the authors present an example where the peripheral temperature is used as a marker to know when a human switchover from a stress state to a calm state happens. The switchovers are controlled by the sympathetic and parasympathetic nervous system. The authors showed that finger temperature can be modulated by an effective auditory noise, and in four of the six tested subjects, 70 dBSPL was the optimal noise. These results open the possibility of making personalized, adaptive and anticipatory devices capable of modulating the switchover from a stress state to a calm state.

Grand challenges for ambient intelligence and implications for design contexts and smart societies

This paper highlights selected grand challenges that concern especially the social and the design dimensions of research and development in Ambient Intelligence (AmI) and Smart Environments (SmE). Due to the increasing deployment and usage of ‘smart’ technologies determining a wide range of everyday life activities, there is an urgent need to reconsider their societal implications and how to address these implications with appropriate design methods. The paper presents four perspectives on the subject grounded in different approaches. First, introducing and reflecting on the implications of the ‘smart-everything’ paradigm, the resulting design trade-offs and their application to smart cities. Second, discussing the potential of non-verbal communication for informing the design of spatial interfaces for AmI design practices. Third, reflecting on the role of new data categories such as ‘future data’ and the role of uncertainty and their implications for the next generation of AmI environments. Finally, debating the merits and shortfalls of the world’s largest professional engineering community effort to craft a global standards body on ethically aligned design for autonomous and intelligent systems. The paper benefits from taking different perspectives on common issues, provides commonalities and relationships between them and provides anchor points for important challenges in the field of ambient intelligence.

Clustering-based and QoS-aware services composition algorithm for ambient intelligence

Due to the dynamic nature of ubiquitous computing and ambient intelligence (AmI) environments, a challenging issue that needs to be addressed is how to construct composite services that satisfy users’ requirements in terms of quality of service (QoS). In this paper, a clustering-based and QoS-aware services composition algorithm (CQCA) is proposed. To increase the composition optimality and reduce the composition time, the candidate services are first partitioned into clusters, where each cluster represents a QoS level. In addition, a new formulation of the utility function based on the use of the characteristics of the resulting clusters is proposed to remove unpromising candidate services in terms of QoS. A lexicographic optimization method is then exploited to filter out candidate services that have low QoS attributes values. Finally, a search tree is constructed to find near-to-optimal compositions. The obtained performance shows that the proposed algorithm outperforms other composition approaches by finding very near-to-optimal compositions in a reduced composition time.

Augmenting natural interaction with physical paper in ambient intelligence environments

Physical paper, in its various forms (e.g. books, leaflets, catalogues), is extensively used in everyday activities, despite any advancements in digital technology and ICT. Inspired by the popularity of this medium, several research efforts have envisioned and pursued a new era of interactive paper, however several challenges still remain to be addressed. On the other hand, recent advancements towards Ambient Intelligence (AmI) and Smart environments, bear the promise of seamless integration of the physical and digital worlds in an intuitive and user-friendly manner. This paper presents InPrinted, a systematic and generic framework supporting physical paper augmentation and user interaction in Ambient intelligence environments. InPrinted enables natural multimodal user interaction with any kind of printed matter in smart environments, providing context aware and anticipation mechanisms, as well as tools and interaction techniques that support the development of applications incorporating printed matter augmentation. InPrinted has been put to practice in the development of various systems, including the Interactive Maps system, facilitating interaction with printed maps as well as their augmentation with digital information. The results of an in-situ observation experiment of the Interactive Maps system are reported, highlighting that interacting with augmented paper is quite easy and natural, while the overall User Experience is positive.