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

Deadline for manuscript submissions: closed (20 September 2023) | Viewed by 16886

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Special Issue Editor

Prof. Dr. Manolis Tsiknakis

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Guest Editor

Department of Electric and Computer Engineering, Hellenic Mediteranean University and Computational BioMedicine Laboratory (CBML), Institute of Computer Science (ICS), Foundation for Research and Technology Hellas (FORTH), Heraklion, Greece
Interests: biomedical Informatics; ehealth; mhealth; affective computing; biomedical signal processing
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Special Issue Information

Dear Colleagues,

This Special Issue aims to provide a comprehensive overview of the current state of the art in sensor technology in Greece. We invite research articles that will consolidate our understanding in this area. The Special Issue will publish full research papers and reviews. Potential topics include, but are not limited to, the following research areas:

Advanced materials for sensing;
Internet of Things;
Industrial sensors and IoT protocols;
Physical sensors;
Chemical sensors;
Biosensors;
Remote sensors;
Sensor networks;
Smart/Intelligent sensors;
Sensor devices;
Sensor technology and application;
Sensing principles;
Optoelectronic and photonic sensors;
Optomechanical sensors;
Sensor arrays and chemometrics;
Micro- and nanosensors;
Signal processing, data fusion, and deep learning in sensor systems;
Sensor interface;
Human–Computer Interaction;
Sensing systems;
MEMS/NEMS;
Localization and object tracking.

Prof. Dr. Manolis Tsiknakis
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.

Published Papers (6 papers)

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Research

20 pages, 784 KiB

Open AccessArticle

Detecting Minor Symptoms of Parkinson’s Disease in the Wild Using Bi-LSTM with Attention Mechanism

by Vasileios Skaramagkas, Iro Boura, Cleanthi Spanaki, Emilia Michou, Georgios Karamanis, Zinovia Kefalopoulou and Manolis Tsiknakis

Sensors 2023, 23(18), 7850; https://0-doi-org.brum.beds.ac.uk/10.3390/s23187850 - 13 Sep 2023

Viewed by 1041

Abstract

Parkinson’s disease (PD) is a neurodegenerative disorder characterized by motor and nonmotor impairment with various implications on patients’ quality of life. Since currently available therapies are only symptomatic, identifying individuals with prodromal, preclinical, or early-stage PD is crucial, as they would be ideal candidates for future disease-modifying therapies. Our analysis aims to develop a robust model for accurate PD detection using accelerometer data collected from PD and non-PD individuals with mild or no tremor during phone conversations. An open-access dataset comprising accelerometer recordings from 22 PD patients and 11 healthy controls (HCs) was utilized. The data were preprocessed to extract relevant time-, frequency-, and energy-related features, and a bidirectional long short-term memory (Bi-LSTM) model with attention mechanism was employed for classification. The performance of the model was evaluated using fivefold cross-validation, and metrics of accuracy, precision, recall, specificity, and f1-score were computed. The proposed model demonstrated high accuracy (98%), precision (99%), recall (98%), specificity (96%), and f1-score (98%) in accurately distinguishing PD patients from HCs. Our findings indicate that the proposed model outperforms existing approaches and holds promise for detection of PD with subtle symptoms, like tremor, in the wild. Such symptoms can present in the early or even prodromal stage of the disease, and appropriate mobile-based applications may be a practical tool in real-life settings to alert individuals at risk to seek medical assistance or give patients feedback in monitoring their symptoms. Full article

(This article belongs to the Special Issue State-of-the-Art Sensors Technology in Greece)

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23 pages, 4153 KiB

Open AccessArticle

A High Performance and Robust FPGA Implementation of a Driver State Monitoring Application

by P. Christakos, N. Petrellis, P. Mousouliotis, G. Keramidas, C. P. Antonopoulos and N. Voros

Sensors 2023, 23(14), 6344; https://0-doi-org.brum.beds.ac.uk/10.3390/s23146344 - 12 Jul 2023

Cited by 1 | Viewed by 1350

Abstract

A high-performance Driver State Monitoring (DSM) application for the detection of driver drowsiness is presented in this paper. The popular Ensemble of Regression Trees (ERTs) machine learning method has been employed for the alignment of 68 facial landmarks. Open-source implementation of ERTs for facial shape alignment has been ported to different platforms and adapted for the acceleration of the frame processing speed using reconfigurable hardware. Reducing the frame processing latency saves time that can be used to apply frame-to-frame facial shape coherency rules. False face detection and false shape estimations can be ignored for higher robustness and accuracy in the operation of the DSM application without sacrificing the frame processing rate that can reach 65 frames per second. The sensitivity and precision in yawning recognition can reach 93% and 97%, respectively. The implementation of the employed DSM algorithm in reconfigurable hardware is challenging since the kernel arguments require large data transfers and the degree of data reuse in the computational kernel is low. Hence, unconventional hardware acceleration techniques have been employed that can also be useful for the acceleration of several other machine learning applications that require large data transfers to their kernels with low reusability. Full article

(This article belongs to the Special Issue State-of-the-Art Sensors Technology in Greece)

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26 pages, 7099 KiB

Open AccessArticle

A Training Smartphone Application for the Simulation of Outdoor Blind Pedestrian Navigation: Usability, UX Evaluation, Sentiment Analysis

by Paraskevi Theodorou, Kleomenis Tsiligkos, Apostolos Meliones and Costas Filios

Sensors 2023, 23(1), 367; https://0-doi-org.brum.beds.ac.uk/10.3390/s23010367 - 29 Dec 2022

Cited by 2 | Viewed by 2306

Abstract

Training blind and visually impaired individuals is an important but often neglected aspect of Assistive Technology solutions (ATs) that can benefit from systems utilizing multiple sensors and hardware devices. Training serves a dual purpose as it not only enables the target group to effectively utilize the ATs but, also, helps in improving their low acceptance rate. In this paper, we present the design, implementation, and validation of a smartphone-based training application. It is a form of immersive system that enables users to learn the features of an outdoor blind pedestrian navigation application and, simultaneously, to help them develop long-term Orientation and Mobility (O&M) skills. The system consists of an Android application leveraging, as data sources, an external high-accuracy GPS sensor for real-time pedestrian mobility tracking, a second custom-made device attached to traffic lights for identifying their status, and an ultra-sonic sensor for detecting near-field obstacles on the navigation path of the users. The training version running as an Android application employs route simulation with audio and haptic feedback, is functionally equivalent to the main application, and was used in the context of specially designed user-centered training sessions. A Usability and User Experience (UX) evaluation revealed the positive attitude of the users towards the training version as well as their satisfaction with the skills acquired during their training sessions (SUS = 69.1, UEQ+ = 1.53). Further confirming the positive attitude was the conduct of a Recursive Neural Network (RNN)-based sentiment analysis on user responses with a score of 3 on a scale from 0 to 4. Finally, we conclude with the lessons learned and the proposal of general design guidelines concerning the observed lack of accessibility and non-universal interfaces. Full article

(This article belongs to the Special Issue State-of-the-Art Sensors Technology in Greece)

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21 pages, 3521 KiB

Open AccessArticle

A Distributed Big Data Analytics Architecture for Vehicle Sensor Data

by Theodoros Alexakis, Nikolaos Peppes, Konstantinos Demestichas and Evgenia Adamopoulou

Sensors 2023, 23(1), 357; https://0-doi-org.brum.beds.ac.uk/10.3390/s23010357 - 29 Dec 2022

Cited by 7 | Viewed by 2249

Abstract

The unceasingly increasing needs for data acquisition, storage and analysis in transportation systems have led to the adoption of new technologies and methods in order to provide efficient and reliable solutions. Both highways and vehicles, nowadays, host a vast variety of sensors collecting different types of highly fluctuating data such as speed, acceleration, direction, and so on. From the vast volume and variety of these data emerges the need for the employment of big data techniques and analytics in the context of state-of-the-art intelligent transportation systems (ITS). Moreover, the scalability needs of fleet and traffic management systems point to the direction of designing and deploying distributed architecture solutions that can be expanded in order to avoid technological and/or technical entrapments. Based on the needs and gaps detected in the literature as well as the available technologies for data gathering, storage and analysis for ITS, the aim of this study is to provide a distributed architecture platform to address these deficiencies. The architectural design of the system proposed, engages big data frameworks and tools (e.g., NoSQL Mongo DB, Apache Hadoop, etc.) as well as analytics tools (e.g., Apache Spark). The main contribution of this study is the introduction of a holistic platform that can be used for the needs of the ITS domain offering continuous collection, storage and data analysis capabilities. To achieve that, different modules of state-of-the-art methods and tools were utilized and combined in a unified platform that supports the entire cycle of data acquisition, storage and analysis in a single point. This leads to a complete solution for ITS applications which lifts the limitations imposed in legacy and current systems by the vast amounts of rapidly changing data, while offering a reliable system for acquisition, storage as well as timely analysis and reporting capabilities of these data. Full article

(This article belongs to the Special Issue State-of-the-Art Sensors Technology in Greece)

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11 pages, 992 KiB

Open AccessArticle

A Quality Control Methodology for Heterogeneous Vehicular Data Streams

by Konstantina Remoundou, Theodoros Alexakis, Nikolaos Peppes, Konstantinos Demestichas and Evgenia Adamopoulou

Sensors 2022, 22(4), 1550; https://0-doi-org.brum.beds.ac.uk/10.3390/s22041550 - 18 Feb 2022

Cited by 1 | Viewed by 1788

Abstract

The rapid evolution of sensors and communication technologies has led to the production and transfer of mass data streams from vehicles either inside their electronic units or to the outside world using the internet infrastructure. The “outside world”, in most cases, consists of third-party applications, such as fleet or traffic management control centers, which utilize vehicular data for reporting and monitoring functionalities. Such applications, in most cases, in order to facilitate their needs, require the exchange and processing of vast amounts of data which can be handled by the so-called Big Data technologies. The purpose of this study is to present a hybrid platform suitable for data collection, storing and analysis enhanced with quality control actions. In particular, the collected data contain various formats originating from different vehicle sensors and are stored in the aforementioned platform in a continuous way. The stored data in this platform must be checked in order to determine and validate them in terms of quality. To do so, certain actions, such as missing values checks, format checks, range checks, etc., must be carried out. The results of the quality control functions are presented herein, and useful conclusions are drawn in order to avoid possible data quality problems which may occur in further analysis and use of the data, e.g., for training of artificial intelligence models. Full article

(This article belongs to the Special Issue State-of-the-Art Sensors Technology in Greece)

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22 pages, 2182 KiB

Open AccessArticle

The Smart-Insole Dataset: Gait Analysis Using Wearable Sensors with a Focus on Elderly and Parkinson’s Patients

by Chariklia Chatzaki, Vasileios Skaramagkas, Nikolaos Tachos, Georgios Christodoulakis, Evangelia Maniadi, Zinovia Kefalopoulou, Dimitrios I. Fotiadis and Manolis Tsiknakis

Sensors 2021, 21(8), 2821; https://0-doi-org.brum.beds.ac.uk/10.3390/s21082821 - 16 Apr 2021

Cited by 41 | Viewed by 7126

Abstract

Gait analysis is crucial for the detection and management of various neurological and musculoskeletal disorders. The identification of gait events is valuable for enhancing gait analysis, developing accurate monitoring systems, and evaluating treatments for pathological gait. The aim of this work is to introduce the Smart-Insole Dataset to be used for the development and evaluation of computational methods focusing on gait analysis. Towards this objective, temporal and spatial characteristics of gait have been estimated as the first insight of pathology. The Smart-Insole dataset includes data derived from pressure sensor insoles, while 29 participants (healthy adults, elderly, Parkinson’s disease patients) performed two different sets of tests: The Walk Straight and Turn test, and a modified version of the Timed Up and Go test. A neurologist specialized in movement disorders evaluated the performance of the participants by rating four items of the MDS-Unified Parkinson’s Disease Rating Scale. The annotation of the dataset was performed by a team of experienced computer scientists, manually and using a gait event detection algorithm. The results evidence the discrimination between the different groups, and the verification of established assumptions regarding gait characteristics of the elderly and patients suffering from Parkinson’s disease. Full article

(This article belongs to the Special Issue State-of-the-Art Sensors Technology in Greece)

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