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Intelligent Systems and Sensors for Assistive Technology—2nd Edition
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Intelligent Systems and Sensors for Assistive Technology—2nd Edition

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

Deadline for manuscript submissions: closed (15 December 2023) | Viewed by 3357

Special Issue Editor

Dr. Marco Leo

E-Mail Website
Guest Editor
Institute of Applied Sciences and Intelligent Systems (ISASI), National Research Council of Italy (CNR), 80078 Pozzuoli, Italy
Interests: computer vision; machine learning; signal processing; assistive technology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

According to the World Health Organization, Assistive technology (AT) enables and promotes inclusion and participation, especially of persons with disabilities, aging populations, and people who have conditions such as diabetes and stroke. Examples of assistive products include hearing aids, wheelchairs, spectacles, prostheses, and devices that support memory, among many others.

In a broader sense, AT can refer to any instrumentation or system aimed at making daily activities easier, such as driving and working, or ensuring safety and security; at the other end of the spectrum, it can be an enabler of new capabilities, such as supporting remote diagnosis or surgery in medical contexts.

In the past few decades, scientific progress has led to new AT solutions that leverage multidisciplinary knowledge in the fields of micro–nano sensors, embedded systems, robotics, computer vision, the Internet of Things (IoT), psychology, wireless networks, medicine, human–machine interaction, advanced materials for sensing, image and signal processing, data fusion, machine learning, and so on.

This Special Issue aims to present a collection of studies describing the latest advances in AT. We welcome contributions in all fields of AT, including new systems and algorithms, as well as those considering new applications. These include but are not limited to:

  • Education;
  • Healthcare;
  • Human–machine interaction (HMI);
  • Remote surgery;
  • Rehabilitation;
  • Safety;
  • Security.

Dr. Marco Leo
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.

Related Special Issue

Published Papers (3 papers)

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Research

16 pages, 1441 KiB  
Article
Wrist-to-Tibia/Shoe Inertial Measurement Results Translation Using Neural Networks
by Marcin Kolakowski, Vitomir Djaja-Josko, Jerzy Kolakowski and Jacek Cichocki
Sensors 2024, 24(1), 293; https://doi.org/10.3390/s24010293 - 03 Jan 2024
Viewed by 744
Abstract
Most of the established gait evaluation methods use inertial sensors mounted in the lower limb area (tibias, ankles, shoes). Such sensor placement gives good results in laboratory conditions but is hard to apply in everyday scenarios due to the sensors’ fragility and the [...] Read more.
Most of the established gait evaluation methods use inertial sensors mounted in the lower limb area (tibias, ankles, shoes). Such sensor placement gives good results in laboratory conditions but is hard to apply in everyday scenarios due to the sensors’ fragility and the user’s comfort. The paper presents an algorithm that enables translation of the inertial signal measurements (acceleration and angular velocity) registered with a wrist-worn sensor to signals, which would be obtained if the sensor was worn on a tibia or a shoe. Four different neural network architectures are considered for that purpose: Dense and CNN autoencoders, a CNN-LSTM hybrid, and a U-Net-based model. The performed experiments have shown that the CNN autoencoder and U-Net can be successfully applied for inertial signal translation purposes. Estimating gait parameters based on the translated signals yielded similar results to those obtained based on shoe-sensor signals. Full article
(This article belongs to the Special Issue Intelligent Systems and Sensors for Assistive Technology—2nd Edition)
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13 pages, 1640 KiB  
Article
The Accuracy and Absolute Reliability of a Knee Surgery Assistance System Based on ArUco-Type Sensors
by Vicente J. León-Muñoz, Fernando Santonja-Medina, Francisco Lajara-Marco, Alonso J. Lisón-Almagro, Jesús Jiménez-Olivares, Carmelo Marín-Martínez, Salvador Amor-Jiménez, Elena Galián-Muñoz, Mirian López-López and Joaquín Moya-Angeler
Sensors 2023, 23(19), 8091; https://doi.org/10.3390/s23198091 - 26 Sep 2023
Cited by 1 | Viewed by 1097
Abstract
Recent advances allow the use of Augmented Reality (AR) for many medical procedures. AR via optical navigators to aid various knee surgery techniques (e.g., femoral and tibial osteotomies, ligament reconstructions or menisci transplants) is becoming increasingly frequent. Accuracy in these procedures is essential, [...] Read more.
Recent advances allow the use of Augmented Reality (AR) for many medical procedures. AR via optical navigators to aid various knee surgery techniques (e.g., femoral and tibial osteotomies, ligament reconstructions or menisci transplants) is becoming increasingly frequent. Accuracy in these procedures is essential, but evaluations of this technology still need to be made. Our study aimed to evaluate the system’s accuracy using an in vitro protocol. We hypothesised that the system’s accuracy was equal to or less than 1 mm and 1° for distance and angular measurements, respectively. Our research was an in vitro laboratory with a 316 L steel model. Absolute reliability was assessed according to the Hopkins criteria by seven independent evaluators. Each observer measured the thirty palpation points and the trademarks to acquire direct angular measurements on three occasions separated by at least two weeks. The system’s accuracy in assessing distances had a mean error of 1.203 mm and an uncertainty of 2.062, and for the angular values, a mean error of 0.778° and an uncertainty of 1.438. The intraclass correlation coefficient was for all intra-observer and inter-observers, almost perfect or perfect. The mean error for the distance’s determination was statistically larger than 1 mm (1.203 mm) but with a trivial effect size. The mean error assessing angular values was statistically less than 1°. Our results are similar to those published by other authors in accuracy analyses of AR systems. Full article
(This article belongs to the Special Issue Intelligent Systems and Sensors for Assistive Technology—2nd Edition)
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23 pages, 6664 KiB  
Article
Design and Evaluation of the Extended FBS Model Based Gaze-Control Power Wheelchair for Individuals Facing Manual Control Challenges
by Xiaochen Zhang, Jiazhen Li, Lingling Jin, Jie Zhao, Qianbo Huang, Ziyang Song, Xinyu Liu and Ding-Bang Luh
Sensors 2023, 23(12), 5571; https://doi.org/10.3390/s23125571 - 14 Jun 2023
Viewed by 1146
Abstract
This study addresses the challenges faced by individuals with upper limb disadvantages in operating power wheelchair joysticks by utilizing the extended Function–Behavior–Structure (FBS) model to identify design requirements for an alternative wheelchair control system. A gaze-controlled wheelchair system is proposed based on design [...] Read more.
This study addresses the challenges faced by individuals with upper limb disadvantages in operating power wheelchair joysticks by utilizing the extended Function–Behavior–Structure (FBS) model to identify design requirements for an alternative wheelchair control system. A gaze-controlled wheelchair system is proposed based on design requirements from the extended FBS model and prioritized using the MosCow method. This innovative system relies on the user’s natural gaze and comprises three levels: perception, decision making, and execution. The perception layer senses and acquires information from the environment, including user eye movements and driving context. The decision-making layer processes this information to determine the user’s intended direction, while the execution layer controls the wheelchair’s movement accordingly. The system’s effectiveness was validated through indoor field testing, with an average driving drift of less than 20 cm for participates. Additionally, the user experience scale revealed overall positive user experiences and perceptions of the system’s usability, ease of use, and satisfaction. Full article
(This article belongs to the Special Issue Intelligent Systems and Sensors for Assistive Technology—2nd Edition)
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