Ultra-Wide Band Radar Empowered Driver Drowsiness Detection with Convolutional Spatial Feature Engineering and Artificial Intelligence

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Fundación Universitaria Internacional de Colombia > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Universidad Internacional do Cuanza > Research > Articles and books
University of La Romana > Research > Scientific Production Abierto Inglés Driving while drowsy poses significant risks, including reduced cognitive function and the potential for accidents, which can lead to severe consequences such as trauma, economic losses, injuries, or death. The use of artificial intelligence can enable effective detection of driver drowsiness, helping to prevent accidents and enhance driver performance. This research aims to address the crucial need for real-time and accurate drowsiness detection to mitigate the impact of fatigue-related accidents. Leveraging ultra-wideband radar data collected over five minutes, the dataset was segmented into one-minute chunks and transformed into grayscale images. Spatial features are retrieved from the images using a two-dimensional Convolutional Neural Network. Following that, these features were used to train and test multiple machine learning classifiers. The ensemble classifier RF-XGB-SVM, which combines Random Forest, XGBoost, and Support Vector Machine using a hard voting criterion, performed admirably with an accuracy of 96.6%. Additionally, the proposed approach was validated with a robust k-fold score of 97% and a standard deviation of 0.018, demonstrating significant results. The dataset is augmented using Generative Adversarial Networks, resulting in improved accuracies for all models. Among them, the RF-XGB-SVM model outperformed the rest with an accuracy score of 99.58%. metadata Siddiqui, Hafeez Ur Rehman and Akmal, Ambreen and Iqbal, Muhammad and Saleem, Adil Ali and Raza, Muhammad Amjad and Zafar, Kainat and Zaib, Aqsa and Dudley, Sandra and Arambarri, Jon and Kuc Castilla, Ángel Gabriel and Rustam, Furqan mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, jon.arambarri@uneatlantico.es, UNSPECIFIED, UNSPECIFIED (2024) Ultra-Wide Band Radar Empowered Driver Drowsiness Detection with Convolutional Spatial Feature Engineering and Artificial Intelligence. Sensors, 24 (12). p. 3754. ISSN 1424-8220

Text
sensors-24-03754 (1).pdf
Available under License Creative Commons Attribution.
Download (7MB)

Official URL: http://doi.org/10.3390/s24123754

Abstract

Driving while drowsy poses significant risks, including reduced cognitive function and the potential for accidents, which can lead to severe consequences such as trauma, economic losses, injuries, or death. The use of artificial intelligence can enable effective detection of driver drowsiness, helping to prevent accidents and enhance driver performance. This research aims to address the crucial need for real-time and accurate drowsiness detection to mitigate the impact of fatigue-related accidents. Leveraging ultra-wideband radar data collected over five minutes, the dataset was segmented into one-minute chunks and transformed into grayscale images. Spatial features are retrieved from the images using a two-dimensional Convolutional Neural Network. Following that, these features were used to train and test multiple machine learning classifiers. The ensemble classifier RF-XGB-SVM, which combines Random Forest, XGBoost, and Support Vector Machine using a hard voting criterion, performed admirably with an accuracy of 96.6%. Additionally, the proposed approach was validated with a robust k-fold score of 97% and a standard deviation of 0.018, demonstrating significant results. The dataset is augmented using Generative Adversarial Networks, resulting in improved accuracies for all models. Among them, the RF-XGB-SVM model outperformed the rest with an accuracy score of 99.58%.

Item Type:	Article
Uncontrolled Keywords:	drowsiness; ultra-wideband radar; convolutional neural network; spatial features; ensemble models
Subjects:	Subjects > Engineering
Divisions:	Europe University of Atlantic > Research > Scientific Production Fundación Universitaria Internacional de Colombia > Research > Scientific Production Ibero-american International University > Research > Scientific Production Universidad Internacional do Cuanza > Research > Articles and books University of La Romana > Research > Scientific Production
Date Deposited:	17 Jun 2024 23:30
Last Modified:	17 Jun 2024 23:30
URI:	https://repositorio.unic.co.ao/id/eprint/12747

Actions (login required)

View Item

open

Tensiomyography, functional movement screen and counter movement jump for the assessment of injury risk in sport: a systematic review of original studies of diagnostic tests

Background: Scientific research should be carried out to prevent sports injuries. For this purpose, new assessment technologies must be used to analyze and identify the risk factors for injury. The main objective of this systematic review was to compile, synthesize and integrate international research published in different scientific databases on Countermovement Jump (CMJ), Functional Movement Screen (FMS) and Tensiomyography (TMG) tests and technologies for the assessment of injury risk in sport. This way, this review determines the current state of the knowledge about this topic and allows a better understanding of the existing problems, making easier the development of future lines of research. Methodology: A structured search was carried out following the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines and the PICOS model until November 30, 2024, in the MEDLINE/PubMed, Web of Science (WOS), ScienceDirect, Cochrane Library, SciELO, EMBASE, SPORTDiscus and Scopus databases. The risk of bias was assessed and the PEDro scale was used to analyze methodological quality. Results: A total of 510 articles were obtained in the initial search. After inclusion and exclusion criteria, the final sample was 40 articles. These studies maintained a high standard of quality. This revealed the effects of the CMJ, FMS and TMG methods for sports injury assessment, indicating the sample population, sport modality, assessment methods, type of research design, study variables, main findings and intervention effects. Conclusions: The CMJ vertical jump allows us to evaluate the power capacity of the lower extremities, both unilaterally and bilaterally, detect neuromuscular asymmetries and evaluate fatigue. Likewise, FMS could be used to assess an athlete's basic movement patterns, mobility and postural stability. Finally, TMG is a non-invasive method to assess the contractile properties of superficial muscles, monitor the effects of training, detect muscle asymmetries, symmetries, provide information on muscle tone and evaluate fatigue. Therefore, they should be considered as assessment tests and technologies to individualize training programs and identify injury risk factors.

Producción Científica

Álvaro Velarde-Sotres mail alvaro.velarde@uneatlantico.es, Antonio Bores-Cerezal mail antonio.bores@uneatlantico.es, Josep Alemany Iturriaga mail josep.alemany@uneatlantico.es, Julio Calleja-González mail ,

Velarde-Sotres

open

Harnessing AI forward and backward chaining with telemetry data for enhanced diagnostics and prognostics of smart devices

In the rapidly evolving landscape of artificial intelligence (AI) and the Internet of Things (IoT), the significance of device diagnostics and prognostics is paramount for guaranteeing the dependable operation and upkeep of intricate systems. The capacity to precisely diagnose and preemptively predict potential failures holds the potential to considerably amplify maintenance efficiency, diminish downtime, and optimize resource allocation. The wealth of information offered by telemetry data gathered from IoT devices presents an opportunity for diagnostics and prognostics applications. However, extracting valuable insights and making well-timed decisions from this extensive data reservoir remains a formidable challenge. This study proposes a novel AI-driven framework that integrates forward chaining and backward chaining algorithms to analyze telemetry data from IoT devices. The proposed methodology utilizes rule-based inference to detect real-time anomalies and predict potential future failures, providing a dual-layered approach for diagnostics and prognostics. The results show that the diagnostics engine using forward chaining detects real-time issues like “High Temperature” and “Low Pressure,” while the prognostics engine with backward chaining predicts potential future occurrences of these issues, enabling proactive prevention measures. The experimental results demonstrate that adopting this approach could offer valuable assistance to authorities and stakeholders. Accurate early diagnosis and prediction of potential failures have the capability to greatly improve maintenance efficiency, minimize downtime, and optimize cost.

Producción Científica

Muhammad Shoaib Farooq mail , Rizwan Pervez Mir mail , Atif Alvi mail , Kilian Tutusaus mail kilian.tutusaus@uneatlantico.es, Eduardo García Villena mail eduardo.garcia@uneatlantico.es, Fadwa Alrowais mail , Hanen Karamti mail , Imran Ashraf mail ,

Farooq

open

Efficient CNN architecture with image sensing and algorithmic channeling for dataset harmonization

The process of image formulation uses semantic analysis to extract influential vectors from image components. The proposed approach integrates DenseNet with ResNet-50, VGG-19, and GoogLeNet using an innovative bonding process that establishes algorithmic channeling between these models. The goal targets compact efficient image feature vectors that process data in parallel regardless of input color or grayscale consistency and work across different datasets and semantic categories. Image patching techniques with corner straddling and isolated responses help detect peaks and junctions while addressing anisotropic noise through curvature-based computations and auto-correlation calculations. An integrated channeled algorithm processes the refined features by uniting local-global features with primitive-parameterized features and regioned feature vectors. Using K-nearest neighbor indexing methods analyze and retrieve images from the harmonized signature collection effectively. Extensive experimentation is performed on the state-of-the-art datasets including Caltech-101, Cifar-10, Caltech-256, Cifar-100, Corel-10000, 17-Flowers, COIL-100, FTVL Tropical Fruits, Corel-1000, and Zubud. This contribution finally endorses its standing at the peak of deep and complex image sensing analysis. A state-of-the-art deep image sensing analysis method delivers optimal channeling accuracy together with robust dataset harmonization performance.

Producción Científica

Khadija Kanwal mail , Khawaja Tehseen Ahmad mail , Aiza Shabir mail , Li Jing mail , Helena Garay mail helena.garay@uneatlantico.es, Luis Eduardo Prado González mail uis.prado@uneatlantico.es, Hanen Karamti mail , Imran Ashraf mail ,

Kanwal

open

Mediterranean Diet and Quality of Life in Adults: A Systematic Review

Background/Objectives: With the increasing life expectancy and, as a result, the aging of the global population, there has been a rise in the prevalence of chronic conditions, which can significantly impact individuals’ health-related quality of life, a multidimensional concept that comprises an individual’s physical, mental, and social wellbeing. While a balanced, nutrient-dense diet, such as Mediterranean diet, is widely recognized for its role in chronic disease prevention, particularly in reducing the risk of cardiovascular diseases and certain cancers, its potential benefits extend beyond these well-known effects, showing promise in improving physical and mental wellbeing, and promoting health-related quality of life. Methods: A systematic search of the scientific literature in electronic databases (Pubmed/Medline) was performed to identify potentially eligible studies reporting on the relation between adherence to the Mediterranean diet and health-related quality of life, published up to December 2024. Results: A total of 28 studies were included in this systematic review, comprising 13 studies conducted among the general population and 15 studies involving various types of patients. Overall, most studies showed a significant association between adherence to the Mediterranean diet and HRQoL, with the most significant results retrieved for physical domains of quality of life, suggesting that diet seems to play a relevant role in both the general population and people affected by chronic conditions with an inflammatory basis. Conclusions: Adherence to the Mediterranean diet provides significant benefits in preventing and managing various chronic diseases commonly associated with aging populations. Furthermore, it enhances the overall health and quality of life of aging individuals, ultimately supporting more effective and less invasive treatment approaches for chronic diseases.

Producción Científica

Justyna Godos mail , Monica Guglielmetti mail , Cinzia Ferraris mail , Evelyn Frias-Toral mail , Irma Dominguez Azpíroz mail irma.dominguez@unini.edu.mx, Vivian Lipari mail vivian.lipari@uneatlantico.es, Andrea Di Mauro mail , Fabrizio Furnari mail , Sabrina Castellano mail , Fabio Galvano mail , Licia Iacoviello mail , Marialaura Bonaccio mail , Giuseppe Grosso mail ,

Godos

open

Novel Transfer Learning Approach for Detecting Infected and Healthy Maize Crop Using Leaf Images

Maize is a staple crop worldwide, essential for food security, livestock feed, and industrial uses. Its health directly impacts agricultural productivity and economic stability. Effective detection of maize crop health is crucial for preventing disease spread and ensuring high yields. This study presents VG-GNBNet, an innovative transfer learning model that accurately detects healthy and infected maize crops through a two-step feature extraction process. The proposed model begins by leveraging the visual geometry group (VGG-16) network to extract initial pixel-based spatial features from the crop images. These features are then further refined using the Gaussian Naive Bayes (GNB) model and feature decomposition-based matrix factorization mechanism, which generates more informative features for classification purposes. This study incorporates machine learning models to ensure a comprehensive evaluation. By comparing VG-GNBNet's performance against these models, we validate its robustness and accuracy. Integrating deep learning and machine learning techniques allows VG-GNBNet to capitalize on the strengths of both approaches, leading to superior performance. Extensive experiments demonstrate that the proposed VG-GNBNet+GNB model significantly outperforms other models, achieving an impressive accuracy score of 99.85%. This high accuracy highlights the model's potential for practical application in the agricultural sector, where the precise detection of crop health is crucial for effective disease management and yield optimization.

Producción Científica

Muhammad Usama Tanveer mail , Kashif Munir mail , Ali Raza mail , Laith Abualigah mail , Helena Garay mail helena.garay@uneatlantico.es, Luis Eduardo Prado González mail uis.prado@uneatlantico.es, Imran Ashraf mail ,

Tanveer

Enlaces de interés

Enlaces de interés

Ultra-Wide Band Radar Empowered Driver Drowsiness Detection with Convolutional Spatial Feature Engineering and Artificial Intelligence

Abstract

Actions (login required)

KNOWLEDGE AREA

ACCESS

LANGUAGE

Filter