Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.3
3.4
Journals
Sensors
Special Issues
Signal, Image Processing and Computer Vision in Smart Living Applications:...
sensors-logo
Submit to Sensors Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Signal, Image Processing and Computer Vision in Smart Living Applications: 3rd Edition

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

Deadline for manuscript submissions: closed (20 January 2024) | Viewed by 4237

Special Issue Editor

Dr. Alessandro Leone

E-Mail Website
Guest Editor
National Research Council of Italy, Institute for Microelectronics and Microsystems, 73100 Lecce, Italy
Interests: ambient assisted living; active & healthy ageing technologies; wearable sensors; signal processing; image processing; computer vision; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Smart spaces and ubiquitous computing extend pervasive computing capabilities to everyday objects, providing context-aware services in smart living environments. One of the main aspects is building smart environments by integrating information from independent multisensor systems, including cameras and ranging devices. “Smart Living Technologies" aims to make all the environments in which people spend their time (at home, at work, in mobility, etc.) more adapted to the needs of those persons, regardless of whether they are in good physical condition in terms of frailty and disability, disease and social exclusion, and regardless of age groups (children, adults, or elderly people).

The Special Issue refers to the use of key enabling technologies and smart system integration for the development of advanced technological solutions for the realization of products (sensors, devices, etc.) and services, which include ambient assisted living, ambient intelligence and IoT paradigms, and reframing “Smart Living” to ensure inclusion, safety, comfort, care, healthcare, and environmental sustainability. The creation of smart devices and services passes through innovation in signal processing, image processing, and computer vision techniques. The Special Issue aims to cover technological issues related to the integration of processing aspects in smart living environments. We invite papers that include but are not limited to the following topics:

  • Artificial intelligence
  • Pattern recognition/analysis
  • Biometrics
  • Human analysis
  • Behavior understanding
  • Computer vision
  • Robotics and intelligent systems
  • Document and media analysis
  • Image processing
  • Signal processing
  • Soft computing techniques
  • Ambient intelligence
  • Context-aware computing
  • Machine learning
  • Deep learning
  • Embedded systems and devices
  • Human–computer interfaces
  • Innovative sensing devices and applications
  • Sensor networks and mobile ad hoc networks
  • Security and privacy techniques

Dr. Alessandro Leone
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.

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Related Special Issue

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

22 pages, 6493 KiB  
Article
Integrating Abnormal Gait Detection with Activities of Daily Living Monitoring in Ambient Assisted Living: A 3D Vision Approach
by Giovanni Diraco, Andrea Manni and Alessandro Leone
Sensors 2024, 24(1), 82; https://doi.org/10.3390/s24010082 - 23 Dec 2023
Viewed by 1333
Abstract
Gait analysis plays a crucial role in detecting and monitoring various neurological and musculoskeletal disorders early. This paper presents a comprehensive study of the automatic detection of abnormal gait using 3D vision, with a focus on non-invasive and practical data acquisition methods suitable [...] Read more.
Gait analysis plays a crucial role in detecting and monitoring various neurological and musculoskeletal disorders early. This paper presents a comprehensive study of the automatic detection of abnormal gait using 3D vision, with a focus on non-invasive and practical data acquisition methods suitable for everyday environments. We explore various configurations, including multi-camera setups placed at different distances and angles, as well as performing daily activities in different directions. An integral component of our study involves combining gait analysis with the monitoring of activities of daily living (ADLs), given the paramount relevance of this integration in the context of Ambient Assisted Living. To achieve this, we investigate cutting-edge Deep Neural Network approaches, such as the Temporal Convolutional Network, Gated Recurrent Unit, and Long Short-Term Memory Autoencoder. Additionally, we scrutinize different data representation formats, including Euclidean-based representations, angular adjacency matrices, and rotation matrices. Our system’s performance evaluation leverages both publicly available datasets and data we collected ourselves while accounting for individual variations and environmental factors. The results underscore the effectiveness of our proposed configurations in accurately classifying abnormal gait, thus shedding light on the optimal setup for non-invasive and efficient data collection. Full article
(This article belongs to the Special Issue Signal, Image Processing and Computer Vision in Smart Living Applications: 3rd Edition)
►▼ Show Figures

Figure 1

18 pages, 5705 KiB  
Article
Classifying Unstable and Stable Walking Patterns Using Electroencephalography Signals and Machine Learning Algorithms
by Rahul Soangra, Jo Armour Smith, Sivakumar Rajagopal, Sai Viswanth Reddy Yedavalli and Erandumveetil Ramadas Anirudh
Sensors 2023, 23(13), 6005; https://doi.org/10.3390/s23136005 - 28 Jun 2023
Cited by 2 | Viewed by 2268
Abstract
Analyzing unstable gait patterns from Electroencephalography (EEG) signals is vital to develop real-time brain-computer interface (BCI) systems to prevent falls and associated injuries. This study investigates the feasibility of classification algorithms to detect walking instability utilizing EEG signals. A 64-channel Brain Vision EEG [...] Read more.
Analyzing unstable gait patterns from Electroencephalography (EEG) signals is vital to develop real-time brain-computer interface (BCI) systems to prevent falls and associated injuries. This study investigates the feasibility of classification algorithms to detect walking instability utilizing EEG signals. A 64-channel Brain Vision EEG system was used to acquire EEG signals from 13 healthy adults. Participants performed walking trials for four different stable and unstable conditions: (i) normal walking, (ii) normal walking with medial-lateral perturbation (MLP), (iii) normal walking with dual-tasking (Stroop), (iv) normal walking with center of mass visual feedback. Digital biomarkers were extracted using wavelet energy and entropies from the EEG signals. Algorithms like the ChronoNet, SVM, Random Forest, gradient boosting and recurrent neural networks (LSTM) could classify with 67 to 82% accuracy. The classification results show that it is possible to accurately classify different gait patterns (from stable to unstable) using EEG-based digital biomarkers. This study develops various machine-learning-based classification models using EEG datasets with potential applications in detecting unsteady gait neural signals and intervening by preventing falls and injuries. Full article
(This article belongs to the Special Issue Signal, Image Processing and Computer Vision in Smart Living Applications: 3rd Edition)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop