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Intelligent Sensing Techniques in Ambient Intelligence
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Intelligent Sensing Techniques in Ambient Intelligence

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

Deadline for manuscript submissions: closed (31 August 2020) | Viewed by 12348

Special Issue Editor

Prof. Dr. Mehmet Rasit Yuce

E-Mail Website
Guest Editor
Department of Electrical and Computer Systems, Monash University, Clayton, VIC 3800, Australia
Interests: wearable devices; IoT sensors; bioelectronics; IC circuits; wireless body area networks; MEMs design; biomedial circuits; RF electronics; energy harvesting; sensor/sensor interface circuits and low-power circuits for emerging technologies in wireless communications, such as UWB technology and the Internet of Things (IoT)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Ambient intelligence (AmI) uses intelligent sensing systems, Artificial Intelligence (AI) techniques, and advanced computing and networking technologies to enable interaction between physical environments and people. Ambient intelligent environments are smart homes, offices, hospitals, schools, workplaces, touristic sites, roads, vehicles, and various other infrastructures. They provide platforms for Artificial Intelligence methods and techniques to be implemented in real time. With the emerging of Internet-of-Things (IoT) technology, intelligent sensing concepts can be utilized to obtain information and knowledge to establish better connectivity and better interaction with these environments.

Ambient Intelligence systems could use several sensing devices, including wireless sensors, cameras, wireless mobile devices, smart phones, RFID, GPS, and information and communications technology (ICT) technologies.

This Special Issue invites contributions in the areas listed below.

Topics:

  • IoT solutions for ambient intelligence
  • Intelligent sensing technology
  • Security and privacy
  • Intelligent robotic systems
  • Multiagent systems
  • Software
  • Sensing technologies to detect human presence
  • Modelling AmI environments
  • Human technology interactions
  • Intelligent interaction methods
  • Autonomous sensing
  • Intelligent sensing methods in a healthcare environment
  • AI and deep learning methods for human technology and human–environment interactions
  • Detection and identification of advanced knowledge from sensing technologies

Prof. Dr. Mehmet Yuce
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 submissions that pass pre-check are 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 2600 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.

Keywords

  • Ambient Intelligence
  • computing
  • intelligent sensing
  • Artificial Intelligence (AI)
  • computer vision
  • machine learning
  • wearable devices
  • robotic systems

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Published Papers (2 papers)

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Research

19 pages, 831 KiB  
Article
Discovering Hidden Mental States in Open Multi-Agent Systems by Leveraging Multi-Protocol Regularities with Machine Learning
by Emilio Serrano and Javier Bajo
Sensors 2020, 20(18), 5198; https://doi.org/10.3390/s20185198 - 12 Sep 2020
Cited by 5 | Viewed by 2753
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Intelligent Sensing Techniques in Ambient Intelligence)
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21 pages, 21720 KiB  
Article
Human Fall Detection Based on Body Posture Spatio-Temporal Evolution
by Jin Zhang, Cheng Wu and Yiming Wang
Sensors 2020, 20(3), 946; https://doi.org/10.3390/s20030946 - 10 Feb 2020
Cited by 58 | Viewed by 7961
Abstract
Abnormal falls in public places have significant safety hazards and can easily lead to serious consequences, such as trampling by people. Vision-driven fall event detection has the huge advantage of being non-invasive. However, in actual scenes, the fall behavior is rich in diversity, [...] Read more.
Abnormal falls in public places have significant safety hazards and can easily lead to serious consequences, such as trampling by people. Vision-driven fall event detection has the huge advantage of being non-invasive. However, in actual scenes, the fall behavior is rich in diversity, resulting in strong instability in detection. Based on the study of the stability of human body dynamics, the article proposes a new model of human posture representation of fall behavior, called the “five-point inverted pendulum model”, and uses an improved two-branch multi-stage convolutional neural network (M-CNN) to extract and construct the inverted pendulum structure of human posture in real-world complex scenes. Furthermore, we consider the continuity of the fall event in time series, use multimedia analytics to observe the time series changes of human inverted pendulum structure, and construct a spatio-temporal evolution map of human posture movement. Finally, based on the integrated results of computer vision and multimedia analytics, we reveal the visual characteristics of the spatio-temporal evolution of human posture under the potentially unstable state, and explore two key features of human fall behavior: motion rotational energy and generalized force of motion. The experimental results in actual scenes show that the method has strong robustness, wide universality, and high detection accuracy. Full article
(This article belongs to the Special Issue Intelligent Sensing Techniques in Ambient Intelligence)
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