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Special Issue "Ambient Intelligent Systems using Wearable Sensors"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (15 August 2019).

Special Issue Editor

Prof. Dr. Vicente Julian
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Guest Editor
Depto. de Sistemas Informáticos y Computación. DSIC., Universitat Politècnica de València, Spain
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

The Ambient Intelligence (AmI) area is currently very prolific, as it is the object of various developments and novel projects. The AmI domain is very complex and presents an important social strand that affects numerous people; thus, it is very relevant. AmI proposes systems and platforms that use technological helpers to perform daily tasks on a home environment.

Over the last few years, AmI has been prolific in providing solutions to assist the user. However, its personalization has not been widely addressed, and it is necessary to have customized systems to properly respond to users' expectations. Thus, the unification of human–computer interactions is essential to offer a more natural way of engaging with its users and translate desires into actions.

New developments like the IOT (Internet of Things) and the increasing amount of computing power of handheld and, recently, wearable devices have allowed the development of environments that were, until recently, unavailable through embedded systems. Therefore, there are a lot of implementation options open for development in this area.

With this Sensors Special Issue on “Ambient Intelligent Systems using Wearable Sensors”, we intend to provide an overview of the research being carried out in this interdisciplinary area in which the use of wearable devices facilitates interactions with users. To this end, we invite novel, high-quality contributions that demonstrate a user-centered focus and preferably (but not necessarily) come from an an engineering perspective.

Areas of interest
Key topics of interest include (but are not limited to) the following:

  • Agent & Multiagent Systems for AmI
  • Ambient-Assisted Living
  • Ambient Intelligence
  • Applications
  • Artificial Intelligence for AmI
  • Cognitive Assistants
  • Context-Aware Computing
  • Data protection and privacy
  • Domotics (Home Automation)
  • Intelligent Systems
  • Knowledge Discovery and Acquisition
  • Mobile Computing
  • Robotics
  • Ubiquitous Computing

Prof. Dr. Vicente Julian
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (3 papers)

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Research

Open AccessFeature PaperArticle
Analysis and Use of the Emotional Context with Wearable Devices for Games and Intelligent Assistants
Sensors 2019, 19(11), 2509; https://doi.org/10.3390/s19112509 - 31 May 2019
Cited by 1
Abstract
In this paper, we consider the use of wearable sensors for providing affect-based adaptation in Ambient Intelligence (AmI) systems. We begin with discussion of selected issues regarding the applications of affective computing techniques. We describe our experiments for affect change detection with a [...] Read more.
In this paper, we consider the use of wearable sensors for providing affect-based adaptation in Ambient Intelligence (AmI) systems. We begin with discussion of selected issues regarding the applications of affective computing techniques. We describe our experiments for affect change detection with a range of wearable devices, such as wristbands and the BITalino platform, and discuss an original software solution, which we developed for this purpose. Furthermore, as a test-bed application for our work, we selected computer games. We discuss the state-of-the-art in affect-based adaptation in games, described in terms of the so-called affective loop. We present our original proposal of a conceptual design framework for games, called the affective game design patterns. As a proof-of-concept realization of this approach, we discuss some original game prototypes, which we have developed, involving emotion-based control and adaptation. Finally, we comment on a software framework, that we have previously developed, for context-aware systems which uses human emotional contexts. This framework provides means for implementing adaptive systems using mobile devices with wearable sensors. Full article
(This article belongs to the Special Issue Ambient Intelligent Systems using Wearable Sensors)
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Open AccessArticle
EMERALD—Exercise Monitoring Emotional Assistant
Sensors 2019, 19(8), 1953; https://doi.org/10.3390/s19081953 - 25 Apr 2019
Cited by 2
Abstract
The increase in the elderly population in today’s society entails the need for new policies to maintain an adequate level of care without excessively increasing social spending. One of the possible options is to promote home care for the elderly. In this sense, [...] Read more.
The increase in the elderly population in today’s society entails the need for new policies to maintain an adequate level of care without excessively increasing social spending. One of the possible options is to promote home care for the elderly. In this sense, this paper introduces a personal assistant designed to help elderly people in their activities of daily living. This system, called EMERALD, is comprised of a sensing platform and different mechanisms for emotion detection and decision-making that combined produces a cognitive assistant that engages users in Active Aging. The contribution of the paper is twofold—on the one hand, the integration of low-cost sensors that among other characteristics allows for detecting the emotional state of the user at an affordable cost; on the other hand, an automatic activity suggestion module that engages the users, mainly oriented to the elderly, in a healthy lifestyle. Moreover, by continuously correcting the system using the on-line monitoring carried out through the sensors integrated in the system, the system is personalized, and, in broad terms, emotionally intelligent. A functional prototype is being currently tested in a daycare centre in the northern area of Portugal where preliminary tests show positive results. Full article
(This article belongs to the Special Issue Ambient Intelligent Systems using Wearable Sensors)
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Open AccessArticle
Classification of Gait Type Based on Deep Learning Using Various Sensors with Smart Insole
Sensors 2019, 19(8), 1757; https://doi.org/10.3390/s19081757 - 12 Apr 2019
Cited by 3
Abstract
In this paper, we proposed a gait type classification method based on deep learning using a smart insole with various sensor arrays. We measured gait data using a pressure sensor array, an acceleration sensor array, and a gyro sensor array built into a [...] Read more.
In this paper, we proposed a gait type classification method based on deep learning using a smart insole with various sensor arrays. We measured gait data using a pressure sensor array, an acceleration sensor array, and a gyro sensor array built into a smart insole. Features of gait pattern were then extracted using a deep convolution neural network (DCNN). In order to accomplish this, measurement data of continuous gait cycle were divided into unit steps. Pre-processing of data were then performed to remove noise followed by data normalization. A feature map was then extracted by constructing an independent DCNN for data obtained from each sensor array. Each of the feature maps was then combined to form a fully connected network for gait type classification. Experimental results for seven types of gait (walking, fast walking, running, stair climbing, stair descending, hill climbing, and hill descending) showed that the proposed method provided a high classification rate of more than 90%. Full article
(This article belongs to the Special Issue Ambient Intelligent Systems using Wearable Sensors)
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