Ambient Intelligence Technologies Research Articles

Navigating the landscape: Safeguarding privacy and security in the era of ambient intelligence within healthcare settings

Ambient intelligence technologies have the potential to transform healthcare by providing personalized, context-aware, and proactive support for patients and healthcare providers. However, the use of these technologies in healthcare settings raises important privacy and security concerns that must be addressed to ensure patient trust and acceptance. This paper explores the privacy and security considerations related to the utilization of ambient intelligence in healthcare, aiming to address the associated risks and establish a robust security infrastructure. By reviewing the inherent privacy and security risks in healthcare settings employing ambient intelligence, discussing the ethical and legal considerations, and proposing mitigation strategies, the focus is on ensuring patient trust and acceptance.The architecture that is being presented is a comprehensive one with interconnected layers that guarantees data confidentiality, integrity, and privacy in the ambient intelligence healthcare system. This protects sensitive data and maintains its continuous availability. This research helps to establish a safe environment that supports the transformational potential of ambient intelligence in healthcare while putting patient privacy and data protection first by thoroughly addressing privacy and security concerns.

Decentralized recommender system for ambient intelligence of tourism destinations serious game using known and unknown rating approach

Tourism destinations serious game (TDSG) requires the ability to respond to players through recommendations for selecting appropriate tourist destinations for them as potential tourists. This research utilizes ambient intelligence technology to regulate the response visualized through a choice of serious game scenarios. This research uses the Multi-Criteria Recommender System (MCRS) to produce recommendations for selecting tourist destinations as a reference for selecting scenario visualizations. Recommender systems require a decentralized, distributed, and secure data-sharing concept to distribute data and assignments between nodes. We propose using the Ethereum blockchain platform to handle data circulation between parts of the system and implement decentralized technology. We also use the known and unknown rating (KUR) approach to improve the system's ability to generate recommendations for players who can provide rating values or those who cannot. This study uses the tourism theme of Batu City, Indonesia, so we use personal characteristics (PC) and rating of destinations attribute (RDA) data for tourists in that city. The test results show that the blockchain can handle decentralized data-sharing well to ensure PC and RDA data circulation between nodes. MCRS has produced recommendations for players based on the KUR approach, indicating that the known rating has better accuracy than the unknown rating. Furthermore, the player can choose and run the tour visualization through game scenarios that appear based on the recommendation ranking results.

Math-VR: Mathematics Serious Game for Madrasah Students using Combination of Virtual Reality and Ambient Intelligence

The challenge to increasing understanding of mathematics lessons for students in madrasah schools makes the learning process require the support of adaptive alternative learning media. In this study, we propose a serious game-based learning media supported by virtual reality and ambient intelligence technology to equip students with adaptive responses to subject matter scenarios. Ambient intelligence works based on recommendations generated by the Multi-Criteria Recommender System (MCRS). In calculating a similarity between users and reference data, MCRS uses cosine-based similarity calculations, and average similarity is used for ranking. We developed this learning media experiment called Math-VR using the Unity game engine. The experimental test results show that MCRS-based ambient intelligence technology can provide an adaptive response to the choice of geometry subject matter recommendations for students according to their pre-test results. The analysis results show that the recommendation system as part of ambient intelligence has the highest accuracy rate of 0.92 when using 80 reference data.

Enabling Ambient Intelligence of Things (AIoT) healthcare system architectures

During the last decade, significant research has been conducted in the fields of healthcare and the administration of cutting-edge ambient intelligence (AmI) technology. Healthcare experts explored smart gadgets and other medical technologies, as well as ambient intelligence using Internet of Things (IoT) (AIoT). The potential for linking the two regions and improving care for individuals living in rural and distant areas is apparent. The healthcare industry has seen tremendous advances in efficiency, affordability, and usefulness as a result of new growth possibilities and drastic cost reductions. This includes a how-to guide on how medical advancements may be both helpful and detrimental as a result of (AIoT-based) technology. The AIoT-H application is anticipated to be covered in the last section since it has the ability to assist with current and varied technologies, and relevant approaches for solving healthcare problems are examined. Furthermore, many potential problems and inconsistencies with the AIoT-H method are often identified. Current and future academics interested in the subject will be able to examine a wide range of possible AIoT-H implementations in order to get a better understanding of the effect and influence on any of the many AIoT applications.

Human gaze-aware attentive object detection for ambient intelligence

Understanding human behavior and the surrounding environment is essential for realizing ambient intelligence (AmI), for which eye gaze and object information are reliable cues. In this study, the authors propose a novel human gaze-aware attentive object detection framework as an elemental technology for AmI. The proposed framework detects users’ attentive objects and shows more precise and robust performance against object-scale variations. A novel Adaptive-3D-Region-of-Interest (Ada-3D-RoI) scheme is designed as a front-end module, and scalable detection network structures are proposed to maximize cost-efficiency. The experiments show that the detection rate is improved up to 97.6% on small objects (14.1% on average), and it is selectively tunable with a tradeoff between accuracy and computational complexity. In addition, the qualitative results demonstrate that the proposed framework detects a user’s single object-of-interest only, even when the target object is occluded or extremely small. Complementary matters for follow-up study are presented as suggestions to extend the results of the proposed framework to further practical AmI applications. This study will help develop advanced AmI applications that demand a higher-level understanding of scene context and human behavior such as human–robot symbiosis, remote-/autonomous control, and augmented/mixed reality.

Effect of User Personality on Efficacy of a Mental Support System Based on Ambient Intelligence: A Case Study

One solution supporting a healthy mental state for humans is controlling the environment with ambient intelligence technology. We are developing a mental support system for healthy people that automatically changes environmental conditions, such as sound volume and light color, depending on the user’s mental state, which is monitored according to physiological signals such as sympathetic nerve activity. In our previous basic study under laboratory-controlled conditions, the system was applied to improve the user’s concentration level as they performed calculation tasks. Results indicated that the system improved the task performance, but individual variations existed, with some users improving greatly but others much less. For the future practical application of the system, determining the causes of the variation in efficacy is important. Considering that the brain structure and activity differ according to an individual’s personality, we investigated the relationship between the user’s personality and task performance with our system’s support. The results showed a clear correlation between the extraversion score and task performance. Our study presents an example where the system’s efficacy is sensitive to the user’s personality and indicates the importance of considering the user’s personality when designing a mental support system based on ambient intelligence.

Car Tourist Trajectory Prediction Based on Bidirectional LSTM Neural Network

COVID-19 has greatly affected the tourist industry and ways of travel. According to the UNTWO predictions, the number of international tourist arrivals will be slowly growing by the end of 2021. One of the ways to keep tourists safe during travel is to use a personal car or car-sharing service. The sensor-based information collected from the tourist’s smartphone during the trip allows his/her behaviour analysis. For this purpose, we propose to use the Internet of Things with ambient intelligence technologies, which allows information processing using the surrounding devices. The paper describes a solution to the car tourist trajectory prediction, which has been the demanding subject of different research studies in recent years. We present an approach based on the usage of the bidirectional LSTM neural network model. We show the reference model of the tourist support system for car-based attraction-visiting trips. The sensor data acquisition process and the bidirectional LSTM model construction, training and evaluation are demonstrated. We propose a system architecture that uses the tourist’s smartphone for data acquisition as well as more powerful surrounding devices for information processing. The obtained results can be used for tourist trip behaviour analysis.

A Survey of Ambient Intelligence

Ambient Intelligence (AmI) is the application and embedding of artificial intelligence into everyday environments to seamlessly provide assistive and predictive support in a multitude of scenarios via an invisible user interface. These can be as diverse as autonomous vehicles, smart homes, industrial settings, and healthcare facilities—referred to as Ambient Assistive Living. This survey gives an overview of the field; defines key terms; discusses social, cultural, and ethical issues; and outlines the state of the art in AmI technology, and where opportunities for further research exist. We guide the reader through AmI from its inception more than 20 years ago, focussing on the important topics and research achievements of the past 10 years since the last major survey, before finally detailing the most recents research trends and forecasting where this technology is likely to develop. This survey covers domains, use cases, scenarios, and datasets; cultural concerns and usability issues; security, privacy, and ethics; interaction and recognition; prediction and intelligence; and hardware, infrastructure, and mobile devices. This survey serves as an introduction for researchers and the technical layperson into the topic of AmI and identifies notable opportunities for further research.

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.

Emotion recognition in low-resource settings: An evaluation of automatic feature selection methods

Research in automatic affect recognition has seldom addressed the issue of computational resource utilization. With the advent of ambient intelligence technology which employs a variety of low-power, resource-constrained devices, this issue is increasingly gaining interest. This is especially the case in the context of health and elderly care technologies, where interventions may rely on monitoring of emotional status to provide support or alert carers as appropriate. This paper focuses on emotion recognition from speech data, in settings where it is desirable to minimize memory and computational requirements. Reducing the number of features for inductive inference is a route towards this goal. In this study, we evaluate three different state-of-the-art feature selection methods: Infinite Latent Feature Selection (ILFS), ReliefF and Fisher (generalized Fisher score), and compare them to our recently proposed feature selection method named ‘Active Feature Selection’ (AFS). The evaluation is performed on three emotion recognition data sets (EmoDB, SAVEE and EMOVO) using two standard acoustic paralinguistic feature sets (i.e. eGeMAPs and emobase). The results show that similar or better accuracy can be achieved using subsets of features substantially smaller than the entire feature set. A machine learning model trained on a smaller feature set will reduce the memory and computational resources of an emotion recognition system which can result in lowering the barriers for use of health monitoring technology.

RSS-Based Indoor Localization Using Belief Function Theory

Received signal strength (RSS) is a simple and low-cost method of localization in wireless sensor networks (WSNs) and is of significant interest in ambient intelligence technologies. However, RSS-based indoor localization poses important challenges due to the intrinsic characteristics of RSS measurements. This paper proposes a localization approach that accounts for the imperfection of RSS measurements and the reliability of RSS sources to estimate the target node position in an indoor WSN environment. Non-Gaussian probability density functions are used to model RSS deviations more realistically in the context of indoor environments. In addition, the proposed approach uses the Dempster–Shafer theory to represent and combine separate pieces of information (evidence) provided by more or less reliable or conflicting RSS sources (anchor nodes) on the same hypotheses regarding the target node position. Experiments conducted in two different indoor environments demonstrate the effectiveness of the proposed approach in terms of its accuracy, robustness, and computation time and its superiority compared with state-of-the-art methods. Note to Practitioners —This paper was motivated by the problem of indoor localization in the context of ambient intelligence applications. The localization technique proposed in this paper exploits RSS measurements to estimate the target node position. This technology is very attractive to system designers, due to its simplicity and low cost. This paper also suggests a new approach using, on the one hand, the belief function theory to represent and manage the imperfection of RSS measurements and the reliability of the RSS sources, and, on the other hand, a more realistic modeling of the variability of RSS measurements due to interference and attenuation phenomena that strongly affect signal propagation in indoor environments. Experimental results obtained in two different indoor environments (a residential apartment and a laboratory) are provided to demonstrate the effectiveness of the proposed approach and its superiority compared to state-of-the-art localization techniques.

Computational intelligence technologies for ambient intelligence

Computational intelligence technologies for ambient intelligence

Active vehicle: a new approach integrating AmI technology for trust management in VANET

Ambient intelligence (AmI) is a new concept that interests many researchers in different domains that require highly innovative services, such as vehicular ad hoc networks (VANETs). In fact, concerning vehicle trust management, the majority of the proposed solutions are dependent on road equipments and the investigation into a scalable infrastructure in all the roads is unreasonable. Therefore, road security should be managed by vehicles themselves. Hence, we have introduced the concept of 'active vehicle' that combines the power of AmI and the V2V technologies. In this paper, we propose a new secured communication model between active vehicles for alert spreading. Through this new model, traffic accident alerts are verified depending on the trust level of the sender. A trust management system has the responsibility to cut out the spread of false warnings through the network.

Complex behavioral pattern mining in non-intrusive sensor-based smart homes using an intelligent activity inference engine

With the help of advanced software, hardware and cutting-edge communication supports, and the future living environments such as smart homes are expected to provide intelligent services and excellent user experience much better than ever. Ideally, indoor multi-modal interactions and ambient changes happened in smart homes could be monitored using ambient intelligence technology for instantaneous human behavior analysis and following human-centered interactions. Such quick feedbacks are significant to the elderly and disabled people, because a successful smart living environment could greatly reduce the burden on their home cares by offering appropriate assistance according to recognized human behaviors. However, due to varied behavioral patterns and imprecise data acquisition, realizing complex human activity in real-time without controversy over privacy is one of the biggest challenges for scientific community. In this paper, we propose an innovative inference engine based on formal concept analysis to recognize complex human activities, including sequential, interleaved, and concurrent patterns performed by an occupant in non-intrusive smart homes. Besides the on-the-fly recognition, our engine adopts a loose coupling architecture that strengthens the software robustness, reliability, and knowledge reusability.

Effects of Ambient Lighting Displays on Peripheral Activity Awareness

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

Automatic Supervision of Temperature, Humidity, and Luminance with an Assistant Personal Robot

Smart environments and Ambient Intelligence (AmI) technologies are defining the future society where energy optimization and intelligent management are essential for a sustainable advance. Mobile robotics is also making an important contribution to this advance with the integration of sensors and intelligent processing algorithms. This paper presents the application of an Assistant Personal Robot (APR) as an autonomous agent for temperature, humidity, and luminance supervision in human-frequented areas. The robot multiagent capabilities allow gathering sensor information while exploring or performing specific tasks and then verifying human comfortability levels. The proposed methodology creates information maps with the distribution of temperature, humidity, and luminance and interprets such information in terms of comfort and warns about corrective actuations if required.

Artificial Intelligence as a Means to Moral Enhancement

AbstractThis paper critically assesses the possibility of moral enhancement with ambient intelligence technologies and artificial intelligence presented in Savulescu and Maslen (2015). The main problem with their proposal is that it is not robust enough to play a normative role in users’ behavior. A more promising approach, and the one presented in the paper, relies on an artificial moral reasoning engine, which is designed to present its users with moral arguments grounded in first-order normative theories, such as Kantianism or utilitarianism, that reason-responsive people can be persuaded by. This proposal can play a normative role and it is also a more promising avenue towards moral enhancement. It is more promising because such a system can be designed to take advantage of the sometimes undue trust that people put in automated technologies. We could therefore expect a well-designed moral reasoner system to be able to persuade people that may not be persuaded by similar arguments from other people. So, all things considered, there is hope in artificial intelligence for moral enhancement, but not in artificial intelligence that relies solely on ambient intelligence technologies.

The quest for engaging AmI: Patient engagement and experience design tools to promote effective assisted living

Recent research highlights that patient engagement, conceived as a patient’s behavioral, cognitive and emotional commitment to his own care management, is a key issue while implementing new technologies in the healthcare process. Indeed, eHealth interventions may systematically fail when the patient’s subjective experience has not been taken into consideration since the first steps of the technology design. In the present contribution, we argue that such an issue is more and more crucial as regarded to the field of Ambient Intelligence (AmI). Specifically, the exact concept of technologies embedded in the patients’ surrounding environment implies a strong impact on their everyday life, which can be perceived as a limitation to autonomy and privacy, and therefore refused or even openly opposed by the final users. The present contribution tackles this issue directly, highlighting: (1) a theoretical framework to include patient engagement in the design of AmI technologies; (2) assessment measures for patient engagement while developing and testing the effectiveness of AmI prototypes for healthcare. Finally (3) this contribution provides an overview of the main issues emerging while implementing AmI technologies and suggests specific design solutions to address them.

A Systematic Review of Proposals for the Social Integration of Elderly People Using Ambient Intelligence and Social Networking Sites

Providing solutions to current issues associated with an aging population has become a major challenge for the scientific community. In late adulthood, social isolation issues are the most frequent and the most threatening for the independent living and cognitive health of elderly adults. In this context, ambient intelligence (AmI) and social networking sites (SNSs) can promote independent living and social integration, improve their health, reduce the chances for developing cognitive decline, and ultimately prevent earlier death. The aim of this paper was to review the literature about how AmI and SNS technologies have been applied in the social integration of elderly adults. Therefore, a systematic review was conducted, which analyzed and classified computational approaches to address the issues of social isolation and social integration of elderly adults. This study revealed a wide variety of research works that are based on AmI and SNS technologies to keep older adults socially active. The paper also identifies the main technological resources and strategies to encourage social integration in this segment of the population. We concluded that this research field is still emerging and there is room for significant contributions.

Flexibility Support for Homecare Applications Based on Models and Multi-Agent Technology.

In developed countries, public health systems are under pressure due to the increasing percentage of population over 65. In this context, homecare based on ambient intelligence technology seems to be a suitable solution to allow elderly people to continue to enjoy the comforts of home and help optimize medical resources. Thus, current technological developments make it possible to build complex homecare applications that demand, among others, flexibility mechanisms for being able to evolve as context does (adaptability), as well as avoiding service disruptions in the case of node failure (availability). The solution proposed in this paper copes with these flexibility requirements through the whole life-cycle of the target applications: from design phase to runtime. The proposed domain modeling approach allows medical staff to design customized applications, taking into account the adaptability needs. It also guides software developers during system implementation. The application execution is managed by a multi-agent based middleware, making it possible to meet adaptation requirements, assuring at the same time the availability of the system even for stateful applications.