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Computer Science
Distributed Systems

Artificial intelligence and ambient intelligence

Profile image of Irene Y.H. GuIrene Y.H. Gu

2019, Journal of Ambient Intelligence and Smart Environments

https://doi.org/10.3233/AIS-180508
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Abstract

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.

Figures (8)
Fig. 1. Application areas of ambient intelligence (own source).
Fig. 1. Application areas of ambient intelligence (own source).
Fig. 2. Internet users per 100 inhabitants 1997 to 2007; ICT Data and Statistics (IDS), International Telecommunication Union (ITU). Retrieved 25 May 2015. Archived 17 May 2015 at the Wayback Ma- chine.
Fig. 2. Internet users per 100 inhabitants 1997 to 2007; ICT Data and Statistics (IDS), International Telecommunication Union (ITU). Retrieved 25 May 2015. Archived 17 May 2015 at the Wayback Ma- chine.
Fig. 3. Error of DNNs on ImageNet competition over time. Source: devblogs.nvidia.com.
Fig. 3. Error of DNNs on ImageNet competition over time. Source: devblogs.nvidia.com.
Fig. 4. The key factors for ambient intelligence in assisted living (from [83]). As shown in Fig. 4, when creating a human-centric smart environment for assisted living, two of the most fundamental tasks are to (a) classify normal daily ac- tivities; and (b) detect abnormal activities. Examples of ambient assisted living for the elderly include detect- ing anomalies like falling, robbery or fire at home, rec- ognizing daily living patterns, and obtaining the statis- tics associated with various daily activities over time. Among all the activities, detecting a fall is one of the topics attracting the most attention, due to the associ- ated risks such as bone fracture, stroke or even death. Triggering emergency help is often necessary, espe- cially for people who live alone. Many different meth- ods have been developed by exploiting different types of sensors, e.g., smart watches, sound sensors, wear- able motion devices (gyroscopes, speedometers, ac- celerometers), visual, range and infrared (IR) cameras. Each sensor has its pros and cons, for example, a wear- able device is straightforward; however, it requires reg-
Fig. 4. The key factors for ambient intelligence in assisted living (from [83]). As shown in Fig. 4, when creating a human-centric smart environment for assisted living, two of the most fundamental tasks are to (a) classify normal daily ac- tivities; and (b) detect abnormal activities. Examples of ambient assisted living for the elderly include detect- ing anomalies like falling, robbery or fire at home, rec- ognizing daily living patterns, and obtaining the statis- tics associated with various daily activities over time. Among all the activities, detecting a fall is one of the topics attracting the most attention, due to the associ- ated risks such as bone fracture, stroke or even death. Triggering emergency help is often necessary, espe- cially for people who live alone. Many different meth- ods have been developed by exploiting different types of sensors, e.g., smart watches, sound sensors, wear- able motion devices (gyroscopes, speedometers, ac- celerometers), visual, range and infrared (IR) cameras. Each sensor has its pros and cons, for example, a wear- able device is straightforward; however, it requires reg-
Fig. 5. GUI for a sample assisted-living system that recognizes typ- ical activities of the elderly at home (from [82]).
Fig. 5. GUI for a sample assisted-living system that recognizes typ- ical activities of the elderly at home (from [82]).
Fig. 6. A 2D convolutional network in combination with the mul- timodal information fusion of MRI for the grading of brain-tumour gliomas (from [31]).
Fig. 6. A 2D convolutional network in combination with the mul- timodal information fusion of MRI for the grading of brain-tumour gliomas (from [31]).
Fig. 7. Screenshots of an AmI-assisted e-learning system to assess the learner’s responses as attentive (left) and distracted (right) on a tablet PC (from [67]). Besides facial analytics (FA), there are many AmlI- assisted or intelligent tutoring systems [66] that em- ploy AmI methods, including classification algorithms
Fig. 7. Screenshots of an AmI-assisted e-learning system to assess the learner’s responses as attentive (left) and distracted (right) on a tablet PC (from [67]). Besides facial analytics (FA), there are many AmlI- assisted or intelligent tutoring systems [66] that em- ploy AmI methods, including classification algorithms
Fig. 8. Plots of the actual versus predicted hourly energy consump- tion for a specific floor of a smart-building application using a deep-learning network (from [1]).
Fig. 8. Plots of the actual versus predicted hourly energy consump- tion for a specific floor of a smart-building application using a deep-learning network (from [1]).

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Carlos Ramos

Lecture Notes in Computer Science

Ambient Intelligence (AmI) deals with a new world where computing devices are spread everywhere (ubiquity), allowing the human being to interact in physical world environments in an intelligent and unobtrusive way. These environments should be aware of the needs of people, customizing requirements and forecasting behaviours. AmI environments may be so diverse, such as homes, offices, meeting rooms, schools, hospitals, control centers, transports, touristic attractions, stores, sport installations, music devices, etc. In the aims of Ambient Intelligence, research envisages to include more intelligence in the AmI environments, allowing a better support to the human being and the access to the essential knowledge to make better decisions when interacting with these environments. This paper can be seen as a State of the Art of Ambient Intelligence, according to an Artificial Intelligence (AI) perspective. We will define Ambient Intelligence; refer some of their prototype and systems; and to analyze how the main Artificial Intelligence areas can be applied.

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Ambient Intelligence—the Next Step for Artificial Intelligence
Carlos Ramos

IEEE Expert / IEEE Intelligent Systems, 2008

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Ambient Intelligence
Carlos Ramos

Ambient Intelligence (AmI) deals with a new world where computing devices are spread everywhere (ubiquity), allowing the human being to interact in physical world environments in an intelligent an d unobtrusive way. These environments should be aware of the needs of people, customizing requirements and forecasting behaviors. AmI environments may be so diverse, such as homes, offices, meeting rooms, schools, hospitals, control centers, transports, touristic attractions, stores, sport installations, music dev ices, etc. In the aims of Ambient Intelligence, research envisages to include more in telligence in the AmI environments, allowing a better support to the huma n being and the access to the essential knowledge to make better decisions when i nter-acting with these environments. This paper can be seen as a State of the Art of Ambient Intelligence, according to an Artificial Intelligen ce (AI) perspective. We will define Ambient Intelligence; refer some of their pr ototype and syst...

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[Lecture Notes in Computer Science] Ambient Intelligence Volume 5859 ||
Manfred #Tscheligi

2009

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Related topics

  • Computer Science
  • Distributed Computing

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    Smart parking management system with dynamic pricing
    Dr. Zeenat Rehena AU

    Journal of Ambient Intelligence and Smart Environments

    Smart parking is becoming more and more an integral part of smart city initiatives. Utilizing and managing parking areas is a challenging task as space is often limited, finding empty spaces are hard and citizens want to park their vehicles close to their preferred places. This becomes worse in important/posh areas of major metropolitan cities during rush hour. Due to unavailability of proper parking management system, citizens have to roam around a lot in order to find a suitable parking area. This leads to the wastage of valuable time, unnecessary fuel consumption and environmental pollution. This paper proposes a smart parking management system (SPMS) based on multiple criteria based parking space reservation algorithm (MCPR) that allows the driver/owner of vehicles to find and reserve most appropriate parking space from anywhere at any time. The system also considers the concept of dynamic pricing strategy for calculating parking charge in order to gain more revenue by the gover...

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    Indian government E-learning initiatives in response to COVID-19 crisis: A case study on online learning in Indian higher education system
    Adebayo Sulaimon

    Research Article, 2021

    Recently, the whole world has faced the deadliest and dangerous consequences due to the transmission of infectious novel coronavirus (nCov). With the outbreak of COVID-19, the education learners, practitioners, and other stakeholders were at the sake of a loss, as it causes the suspension of physical classes and physical interaction of the learners. In these circumstances, Electronic learning (E-learning), Online learning, and the use of Information and Communications Technology (ICT) tools came in handy. It helped the learners in the dissemination of ideas, conducting online classes, making online discussion forums, and taking online examinations. Like the government of each country, the Indian government was also caught off-guard but the existing E-learning infrastructure was able to leverage on while devising plans to tailor them to new situations and launching new ones. The initiatives at the forefront of this noble battle launched by the Department of School Education and Literacy, Ministry of Human Resources Development (MHRD) includes Diksha, Swayam Prabha Channel, Shiksha Van, E-Pathshala, and National Repository of Open Educational Resources (NROER). It worth noting that apart from the Indian central government efforts, each state has various online education initiatives that are tailored to their needs. This research evaluated each of these initiatives commenced by central and state governments and present a detailed analysis of most of the relevant initiatives. Additionally, a survey is conducted to get insights of learners in concern to online learning. Despite the issues raised in this learning, the outcomes come to be satisfactorily favoring online learning.

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    Incorporating artificial intelligence in knowledge creation processes in organizations
    Andrei Stefan Nestian

    Proceedings of the International Conference on Business Excellence, 2020

    In organizations, the knowledge creation remained an exclusive attribute of humans until the emergence of artificial intelligence. The role of information systems in organizations expanded continuously, including recording, organizing, processing, storage, dissemination and making available the information, until the revolutionary emergence of artificial intelligence. The role of knowledge creation is no longer exclusive to humans. Artificial intelligence becomes a partner entity of employees in knowledge creation processes in organizations. This article investigates how artificial intelligence can be incorporated in knowledge creation processes. Starting from the study of scientific literature, this article presents concepts and taxonomies associated with the use of artificial intelligence in the processes of knowledge creation in organizations. The study is a first step in a larger research, which aims to identify the main consequences, on employees and organizations, of the incor...

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    Modeling and “smart” prototyping human-in-the-loop interactions for AmI environments
    Manoli Albert

    Personal and Ubiquitous Computing, 2021

    Autonomous capabilities are required in AmI environments in order to adapt systems to new environmental conditions and situations. However, keeping the human in the loop and in control of such systems is still necessary because of the diversity of systems, domains, environments, context situations, and social and legal constraints, which makes full autonomy a utopia within the short or medium term. Human-system integration introduces an important number of challenges and problems that have to be solved. On the one hand, humans should interact with systems even in those situations where their attentional, cognitive, and physical resources are limited in order to perform the interaction. On the other hand, systems must avoid overwhelming the user with unnecessary actions. Therefore, appropriate user-centered methods for AmI development should be used to help designers analyze and design human-in-the-loop interactions in AmI environments. This paper presents a user-centered design meth...

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    RECITE: A framework for user trajectory analysis in cultural sites
    Mercedes Valdés

    Journal of Ambient Intelligence and Smart Environments, 2021

    The Internet of Things (IoT) has recently been applied in the domain of cultural exhibition enabling the cultural sites to provide more personal and proactive experiences to their visitors. To come up with valuable services, several solutions to analyze the spatio-temporal trajectories of visitors have been put forward. However, they neither consider the inherent uncertainty of the underlying indoor positioning technologies – Bluetooth Low Energy (BLE), RFID, etc. – nor other visitors’ features apart from the spatio-temporal ones (e.g. the level of interaction with the museum displays). For that reason, the present work introduces RECITE, a framework to classify trajectories representing visitors’ actions that copes with the aforementioned limitations of existing solutions. Firstly, RECITE states a novel mapping process for a BLE-based indoor positioning system to accurately detect the visitors’ locations. On top of this mechanism, RECITE includes an ensemble of fuzzy rule classifie...

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