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The Future of Healthcare: Biomedical Technology and Integrated Artificial Intelligence...
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The Future of Healthcare: Biomedical Technology and Integrated Artificial Intelligence 2nd Edition

A special issue of Technologies (ISSN 2227-7080). This special issue belongs to the section "Assistive Technologies".

Deadline for manuscript submissions: closed (31 January 2025) | Viewed by 22721

Special Issue Editors

Prof. Dr. Juvenal Rodriguez-Resendiz

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Guest Editor
Facultad de Ingeniería, Universidad Autónoma de Querétaro, Santiago de Querétaro 76010, Mexico
Interests: robotics; biomedical applications; instrumentation and measurement; signal processing
Special Issues, Collections and Topics in MDPI journals
Dr. Gerardo I. Pérez-Soto

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Guest Editor
Engineering Faculty, Universidad Autónoma de Querétaro, Querétaro 76010, Mexico
Interests: robotics; mechanical design; applied mechanics; theoretical kinematics
Special Issues, Collections and Topics in MDPI journals
Dr. Karla Anhel Camarillo-Gómez

E-Mail Website
Guest Editor
Department of Mechanical Engineering, Tecnológico Nacional de México en Celaya, Celaya 38010, México
Interests: robotics; biomechanics; control systems
Special Issues, Collections and Topics in MDPI journals
Dr. Saul Tovar-Arriaga

E-Mail Website
Guest Editor
Faculty of Engineering, Autonomous University of Queretaro, Cerro de las Campanas S/N, Santiago de Queretaro 76010, Queretaro, Mexico
Interests: image-based diagnosis; artificial intelligence; medical robotics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Biomedical technology, which is fundamental today, incorporates various disciplines such as signal, image, and data processing, greatly benefiting healthcare systems. Artificial intelligence plays a crucial role in allowing the creation of embedded systems that monitor and adjust patient treatment in real-time, ensuring more personalized and effective care. Additionally, this integration facilitates the development of automatic diagnostic techniques, such as advanced medical image analysis and biometric data interpretation, which have transformed the diagnosis and treatment of diseases, thus improving patients’ quality of life. This advance is essential to respond to the demands of a growing population with expectations of high-quality medical care.

This Special Issue aims to showcase innovators who are using artificial intelligence as a main topic for solving problems in biomedical technology through the development of technologies with integrated systems for health and quality of life.

Artificial intelligence techniques focus on the following biomedical engineering topics:

  • Machine learning applied to medicine;
  • Deep learning for disease diagnosis;
  • Deep learning for biomaterials;
  • Optimization of autonomous systems through artificial intelligence in healthcare;
  • Metaheuristic algorithms for the design of prostheses and medical devices;
  • Metaheuristic algorithms for 3D bioprinting;
  • Tissue engineering optimization algorithms;
  • Fuzzy techniques for the analysis of biomedical signals;
  • Mixed techniques for the development of intelligent systems in medical care;
  • Image processing techniques;
  • Generative models for the synthesis of medical data;
  • AI-based clinical decision algorithms for prediction;
  • Natural language analysis algorithms for the extraction of clinical information;
  • Reinforcement learning for the optimization of medical treatments.

Examples of applications with integrated artificial intelligence:

  • Health surveillance and control;
  • Diagnosis and treatment of diseases;
  • Advanced medical imaging;
  • Prosthetics and intelligent medical devices;
  • Personalized medicine;
  • Digital health;
  • Predictive analysis in public health;
  • Improving hospital efficiency;
  • AI-assisted rehabilitation;
  • Medical education and simulation;
  • Development and customization of medicines;
  • Development of scaffolds for tissue regeneration adjusting by AI;
  • Analysis of massive medical data.

Integrating artificial intelligence into biomedical technology has opened a world of possibilities to improve people's quality of life.

Related SI:

The Future of Healthcare: Biomedical Technology and Integrated Artificial Intelligence

https://www.mdpi.com/journal/technologies/special_issues/H45GFE5110

Prof. Dr. Juvenal Rodriguez-Resendiz
Dr. Gerardo I. Pérez-Soto
Dr. Karla Anhel Camarillo-Gómez
Dr. Saul Tovar-Arriaga
Guest Editors

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. Technologies is an international peer-reviewed open access monthly 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 1600 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

  • healthcare
  • biomedical technology
  • machine learning
  • deep learning
  • artificial intelligence
  • image processing technique

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Published Papers (10 papers)

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Research

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23 pages, 7556 KiB  
Article
AI Diffusion Model-Based Technology for Automating the Multi-Class Labeling of Electron Microscopy Datasets of Brain Cell Organelles for Their Augmentation and Synthetic Generation
by Nikolay Sokolov, Alexandra Getmanskaya and Vadim Turlapov
Technologies 2025, 13(4), 127; https://doi.org/10.3390/technologies13040127 - 25 Mar 2025
Viewed by 198
Abstract
A technology for the automatic multi-class labeling of brain electron microscopy (EM) objects needed to create large synthetic datasets, which could be used for brain cell segmentation tasks, is proposed. The main research tools were a generative diffusion AI model and a U-Net-like [...] Read more.
A technology for the automatic multi-class labeling of brain electron microscopy (EM) objects needed to create large synthetic datasets, which could be used for brain cell segmentation tasks, is proposed. The main research tools were a generative diffusion AI model and a U-Net-like segmentation model. The technology was studied on the segmentation task of up to six brain organelles. The initial dataset used was the popular EPFL dataset labeled for the mitochondria class, which has training and test parts having 165 layers each. Our mark up for the EPFL dataset was named EPFL6 and contained six classes. The technology was implemented and studied in a two-step experiment: (1) dataset synthesis using a diffusion model trained on EPFL6; (2) evaluation of the labeling accuracy of a multi-class synthetic dataset by the segmentation accuracy on the test part of EPFL6. It was found that (1) the segmentation accuracy of the mitochondria class for the diffusion synthetic datasets corresponded to the accuracy of the original ones; (2) augmentation via geometric synthetics provided a better accuracy for underrepresented classes; (3) the naturalization of geometric synthetics by the diffusion model yielded a positive effect; (4) due to the augmentation of the 165 layers of the original EPFL dataset with diffusion synthetics, it was possible to achieve and surpass the record accuracy of Dice = 0.948, which was achieved using 3D estimation in Hive-net (2021). Full article
(This article belongs to the Special Issue The Future of Healthcare: Biomedical Technology and Integrated Artificial Intelligence 2nd Edition)
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33 pages, 15628 KiB  
Article
Towards Transparent AI in Medicine: ECG-Based Arrhythmia Detection with Explainable Deep Learning
by Oleksii Kovalchuk, Oleksandr Barmak, Pavlo Radiuk, Liliana Klymenko and Iurii Krak
Technologies 2025, 13(1), 34; https://doi.org/10.3390/technologies13010034 - 14 Jan 2025
Viewed by 2375
Abstract
Cardiovascular diseases are the leading cause of death globally, highlighting the need for accurate diagnostic tools. To address this issue, we introduce a novel approach for arrhythmia detection based on electrocardiogram (ECG) that incorporates explainable artificial intelligence through three key methods. First, we [...] Read more.
Cardiovascular diseases are the leading cause of death globally, highlighting the need for accurate diagnostic tools. To address this issue, we introduce a novel approach for arrhythmia detection based on electrocardiogram (ECG) that incorporates explainable artificial intelligence through three key methods. First, we developed an enhanced R peak detection method that integrates domain-specific knowledge into the ECG, improving peak identification accuracy by accounting for the characteristic features of R peaks. Second, we proposed an arrhythmia classification method utilizing a modified convolutional neural network (CNN) architecture with additional convolutional and batch normalization layers. This model processes a triad of cardio cycles—the preceding, current, and following cycles—to capture temporal dependencies and hidden features related to arrhythmias. Third, we implemented an interpretation method that explains CNN’s decisions using clinically relevant features, making the results understandable to clinicians. Using the MIT-BIH database, our approach achieved an accuracy of 99.43%, with F1-scores approaching 100% for major arrhythmia classes. The integration of these methods enhances both the performance and transparency of arrhythmia detection systems. Full article
(This article belongs to the Special Issue The Future of Healthcare: Biomedical Technology and Integrated Artificial Intelligence 2nd Edition)
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22 pages, 6268 KiB  
Article
Real-Time Deployment of Ultrasound Image Interpretation AI Models for Emergency Medicine Triage Using a Swine Model
by Sofia I. Hernandez Torres, Lawrence Holland, Theodore Winter, Ryan Ortiz, Krysta-Lynn Amezcua, Austin Ruiz, Catherine R. Thorpe and Eric J. Snider
Technologies 2025, 13(1), 29; https://doi.org/10.3390/technologies13010029 - 11 Jan 2025
Viewed by 1851
Abstract
Ultrasound imaging is commonly used for medical triage in both civilian and military emergency medicine sectors. One specific application is the eFAST, or the extended focused assessment with sonography in trauma exam, where pneumothorax, hemothorax, or abdominal hemorrhage injuries are identified. However, the [...] Read more.
Ultrasound imaging is commonly used for medical triage in both civilian and military emergency medicine sectors. One specific application is the eFAST, or the extended focused assessment with sonography in trauma exam, where pneumothorax, hemothorax, or abdominal hemorrhage injuries are identified. However, the diagnostic accuracy of an eFAST exam depends on obtaining proper scans and making quick interpretation decisions to evacuate casualties or administer necessary interventions. To improve ultrasound interpretation, we developed AI models to identify key anatomical structures at eFAST scan sites, simplifying image acquisition by assisting with proper probe placement. These models plus image interpretation diagnostic models were paired with two real-time eFAST implementations. The first implementation was a manual AI-driven ultrasound eFAST tool that used guidance models to select correct frames prior to making any diagnostic predictions. The second implementation was a robotic imaging platform capable of providing semi-autonomous image acquisition combined with diagnostic image interpretation. We highlight the use of both real-time approaches in a swine injury model and compare their performance of this emergency medicine application. In conclusion, AI can be deployed in real time to provide rapid triage decisions, lowering the skill threshold for ultrasound imaging at or near the point of injury. Full article
(This article belongs to the Special Issue The Future of Healthcare: Biomedical Technology and Integrated Artificial Intelligence 2nd Edition)
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22 pages, 18812 KiB  
Article
Enhancing Thyroid Nodule Detection in Ultrasound Images: A Novel YOLOv8 Architecture with a C2fA Module and Optimized Loss Functions
by Shidan Wang, Zi-An Zhao, Yuze Chen, Ye-Jiao Mao and James Chung-Wai Cheung
Technologies 2025, 13(1), 28; https://doi.org/10.3390/technologies13010028 - 9 Jan 2025
Cited by 1 | Viewed by 1857
Abstract
Thyroid-related diseases, particularly thyroid cancer, are rising globally, emphasizing the critical need for the early detection and accurate screening of thyroid nodules. Ultrasound imaging has inherent limitations—high noise, low contrast, and blurred boundaries—that make manual interpretation subjective and error-prone. To address these challenges, [...] Read more.
Thyroid-related diseases, particularly thyroid cancer, are rising globally, emphasizing the critical need for the early detection and accurate screening of thyroid nodules. Ultrasound imaging has inherent limitations—high noise, low contrast, and blurred boundaries—that make manual interpretation subjective and error-prone. To address these challenges, YOLO-Thyroid, an improved model for the automatic detection of thyroid nodules in ultrasound images, is presented herein. Building upon the YOLOv8 architecture, YOLO-Thyroid introduces the C2fA module—an extension of C2f that incorporates Coordinate Attention (CA)—to enhance feature extraction. Additionally, loss functions were incorporated, including class-weighted binary cross-entropy to alleviate class imbalance and SCYLLA-IoU (SIoU) to improve localization accuracy during boundary regression. A publicly available thyroid ultrasound image dataset was optimized using format conversion and data augmentation. The experimental results demonstrate that YOLO-Thyroid outperforms mainstream object detection models across multiple metrics, achieving a higher detection precision of 54%. The recall, calculated based on the detection of nodules containing at least one feature suspected of being malignant, reaches 58.2%, while the model maintains a lightweight structure. The proposed method significantly advances ultrasound nodule detection, providing an effective and practical solution for enhancing diagnostic accuracy in medical imaging. Full article
(This article belongs to the Special Issue The Future of Healthcare: Biomedical Technology and Integrated Artificial Intelligence 2nd Edition)
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12 pages, 4513 KiB  
Article
Malaria Cell Image Classification Using Compact Deep Learning Architectures on Jetson TX2
by Adán-Antonio Alonso-Ramírez, Alejandro-Israel Barranco-Gutiérrez, Iris-Iddaly Méndez-Gurrola, Marcos Gutiérrez-López, Juan Prado-Olivarez, Francisco-Javier Pérez-Pinal, J. Jesús Villegas-Saucillo, Jorge-Alberto García-Muñoz and Carlos-Hugo García-Capulín
Technologies 2024, 12(12), 247; https://doi.org/10.3390/technologies12120247 - 27 Nov 2024
Cited by 1 | Viewed by 2213
Abstract
Malaria is a significant global health issue, especially in tropical regions. Accurate and rapid diagnosis is critical for effective treatment and reducing mortality rates. Traditional diagnostic methods, like blood smear microscopy, are time-intensive and prone to error. This study introduces a deep learning [...] Read more.
Malaria is a significant global health issue, especially in tropical regions. Accurate and rapid diagnosis is critical for effective treatment and reducing mortality rates. Traditional diagnostic methods, like blood smear microscopy, are time-intensive and prone to error. This study introduces a deep learning approach for classifying malaria-infected cells in blood smear images using convolutional neural networks (CNNs); Six CNN models were designed and trained using a large labeled dataset of malaria cell images, both infected and uninfected, and were implemented on the Jetson TX2 board to evaluate them. The model was optimized for feature extraction and classification accuracy, achieving 97.72% accuracy, and evaluated using precision, recall, and F1-score metrics and execution time. Results indicate deep learning significantly improves diagnostic time efficiency on embedded systems. This scalable, automated solution is particularly useful in resource-limited areas without access to expert microscopic analysis. Future work will focus on clinical validation. Full article
(This article belongs to the Special Issue The Future of Healthcare: Biomedical Technology and Integrated Artificial Intelligence 2nd Edition)
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22 pages, 3718 KiB  
Article
Comparing Optical and Custom IoT Inertial Motion Capture Systems for Manual Material Handling Risk Assessment Using the NIOSH Lifting Index
by Manuel Gutierrez, Britam Gomez, Gustavo Retamal, Guisella Peña, Enrique Germany, Paulina Ortega-Bastidas and Pablo Aqueveque
Technologies 2024, 12(10), 180; https://doi.org/10.3390/technologies12100180 - 30 Sep 2024
Cited by 1 | Viewed by 2640
Abstract
Assessing musculoskeletal disorders (MSDs) in the workplace is vital for improving worker health and safety, reducing costs, and increasing productivity. Traditional hazard identification methods are often inefficient, particularly in detecting complex risks, which may compromise risk management. This study introduces a semi-automatic platform [...] Read more.
Assessing musculoskeletal disorders (MSDs) in the workplace is vital for improving worker health and safety, reducing costs, and increasing productivity. Traditional hazard identification methods are often inefficient, particularly in detecting complex risks, which may compromise risk management. This study introduces a semi-automatic platform using two motion capture systems—an optical system (OptiTrack®) and a Bluetooth Low Energy (BLE)-based system with inertial measurement units (IMUs), developed at the Biomedical Engineering Laboratory, Universidad de Concepción, Chile. These systems, tested on 20 participants (10 women and 10 men, aged 30 ± 9 years without MSDs), facilitate risk assessments via the digitized NIOSH Index method. Analysis of ergonomically significant variables (H, V, A, D) and calculation of the RWL and LI showed both systems aligned with expected ergonomic standards, although significant differences were observed in vertical displacement (V), horizontal displacement (H), and trunk rotation (A), indicating areas for improvement, especially for the BLE system. The BLE Inertial MoCap system recorded mean heights of 33.87 cm (SD = 4.46) and vertical displacements of 13.17 cm (SD = 4.75), while OptiTrack® recorded mean heights of 30.12 cm (SD = 2.91) and vertical displacements of 15.67 cm (SD = 2.63). Despite the greater variability observed in BLE system measurements, both systems accurately captured vertical vertical absolute displacement (D), with means of 32.05 cm (SD = 7.36) for BLE and 31.80 cm (SD = 3.25) for OptiTrack®. Performance analysis showed high precision for both systems, with BLE and OptiTrack® achieving precision rates of 98.5%. Sensitivity, however, was lower for BLE (97.5%) compared to OptiTrack® (98.7%). The BLE system’s F1 score was 97.9%, while OptiTrack® scored 98.6%, indicating both systems can reliably assess ergonomic risk. These findings demonstrate the potential of using BLE-based IMUs for workplace ergonomics, though further improvements in measurement accuracy are needed. The user-friendly BLE-based system and semi-automatic platform significantly enhance risk assessment efficiency across various workplace environments. Full article
(This article belongs to the Special Issue The Future of Healthcare: Biomedical Technology and Integrated Artificial Intelligence 2nd Edition)
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24 pages, 8029 KiB  
Article
Real-Time Machine Learning for Accurate Mexican Sign Language Identification: A Distal Phalanges Approach
by Gerardo García-Gil, Gabriela del Carmen López-Armas, Juan Jaime Sánchez-Escobar, Bryan Armando Salazar-Torres and Alma Nayeli Rodríguez-Vázquez
Technologies 2024, 12(9), 152; https://doi.org/10.3390/technologies12090152 - 4 Sep 2024
Viewed by 2993
Abstract
Effective communication is crucial in daily life, and for people with hearing disabilities, sign language is no exception, serving as their primary means of interaction. Various technologies, such as cochlear implants and mobile sign language translation applications, have been explored to enhance communication [...] Read more.
Effective communication is crucial in daily life, and for people with hearing disabilities, sign language is no exception, serving as their primary means of interaction. Various technologies, such as cochlear implants and mobile sign language translation applications, have been explored to enhance communication and improve the quality of life of the deaf community. This article presents a new, innovative method that uses real-time machine learning (ML) to accurately identify Mexican sign language (MSL) and is adaptable to any sign language. Our method is based on analyzing six features that represent the angles between the distal phalanges and the palm, thus eliminating the need for complex image processing. Our ML approach achieves accurate sign language identification in real-time, with an accuracy and F1 score of 99%. These results demonstrate that a simple approach can effectively identify sign language. This advance is significant, as it offers an effective and accessible solution to improve communication for people with hearing impairments. Furthermore, the proposed method has the potential to be implemented in mobile applications and other devices to provide practical support to the deaf community. Full article
(This article belongs to the Special Issue The Future of Healthcare: Biomedical Technology and Integrated Artificial Intelligence 2nd Edition)
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17 pages, 5043 KiB  
Article
Enhancing Diagnostic Accuracy for Skin Cancer and COVID-19 Detection: A Comparative Study Using a Stacked Ensemble Method
by Hafza Qayyum, Syed Tahir Hussain Rizvi, Muddasar Naeem, Umamah bint Khalid, Musarat Abbas and Antonio Coronato
Technologies 2024, 12(9), 142; https://doi.org/10.3390/technologies12090142 - 27 Aug 2024
Viewed by 2264
Abstract
In recent years, COVID-19 and skin cancer have become two prevalent illnesses with severe consequences if untreated. This research represents a significant step toward leveraging machine learning (ML) and ensemble techniques to improve the accuracy and efficiency of medical image diagnosis for critical [...] Read more.
In recent years, COVID-19 and skin cancer have become two prevalent illnesses with severe consequences if untreated. This research represents a significant step toward leveraging machine learning (ML) and ensemble techniques to improve the accuracy and efficiency of medical image diagnosis for critical diseases such as COVID-19 (grayscale images) and skin cancer (RGB images). In this paper, a stacked ensemble learning approach is proposed to enhance the precision and effectiveness of diagnosis of both COVID-19 and skin cancer. The proposed method combines pretrained models of convolutional neural networks (CNNs) including ResNet101, DenseNet121, and VGG16 for feature extraction of grayscale (COVID-19) and RGB (skin cancer) images. The performance of the model is evaluated using both individual CNNs and a combination of feature vectors generated from ResNet101, DenseNet121, and VGG16 architectures. The feature vectors obtained through transfer learning are then fed into base-learner models consisting of five different ML algorithms. In the final step, the predictions from the base-learner models, the ensemble validation dataset, and the feature vectors extracted from neural networks are assembled and applied as input for the meta-learner model to obtain final predictions. The performance metrics of the stacked ensemble model show high accuracy for COVID-19 diagnosis and intermediate accuracy for skin cancer. Full article
(This article belongs to the Special Issue The Future of Healthcare: Biomedical Technology and Integrated Artificial Intelligence 2nd Edition)
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12 pages, 1418 KiB  
Article
The Measurement of Contrast Sensitivity in Near Vision: The Use of a Digital System vs. a Conventional Printed Test
by Kevin J. Mena-Guevara, David P. Piñero, María José Luque and Dolores de Fez
Technologies 2024, 12(7), 108; https://doi.org/10.3390/technologies12070108 - 9 Jul 2024
Viewed by 2279
Abstract
In recent years, there has been intense development of digital diagnostic tests for vision. All of these tests must be validated for clinical use. The current study enrolled 51 healthy individuals (age 19–72 years) in which achromatic contrast sensitivity function (CSF) in near [...] Read more.
In recent years, there has been intense development of digital diagnostic tests for vision. All of these tests must be validated for clinical use. The current study enrolled 51 healthy individuals (age 19–72 years) in which achromatic contrast sensitivity function (CSF) in near vision was measured with the printed Vistech VCTS test (Stereo Optical Co., Inc., Chicago, IL, USA) and the Optopad-CSF (developed by our research group to be used on an iPad). Likewise, chromatic CSF was evaluated with a digital test. Statistically significant differences between tests were only found for the two higher spatial frequencies evaluated (p = 0.012 and <0.001, respectively). The mean achromatic index of contrast sensitivity (ICS) was 0.02 ± 1.07 and −0.76 ± 1.63 for the Vistech VCTS and Optopad tests, respectively (p < 0.001). The ranges of agreement between tests were 0.55, 0.76, 0.78, and 0.69 log units for the spatial frequencies of 1.5, 3, 6, and 12 cpd, respectively. The mean chromatic ICS values were −20.56 ± 0.96 and −0.16 ± 0.99 for the CSF-T and CSF-D plates, respectively (p < 0.001). Furthermore, better achromatic, red–green, and blue–yellow CSF values were found in the youngest groups. The digital test allows the fast measurement of near-achromatic and chromatic CSF using a colorimetrically calibrated iPad, but the achromatic measures cannot be used interchangeably with those obtained with a conventional printed test. Full article
(This article belongs to the Special Issue The Future of Healthcare: Biomedical Technology and Integrated Artificial Intelligence 2nd Edition)
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Review

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30 pages, 1427 KiB  
Review
Wearable Fall Detectors Based on Low Power Transmission Systems: A Systematic Review
by Manny Villa and Eduardo Casilari
Technologies 2024, 12(9), 166; https://doi.org/10.3390/technologies12090166 - 13 Sep 2024
Cited by 1 | Viewed by 3282
Abstract
Early attention to individuals who suffer falls is a critical aspect when determining the consequences of such accidents, which are among the leading causes of mortality and disability in older adults. For this reason and considering the high number of older adults living [...] Read more.
Early attention to individuals who suffer falls is a critical aspect when determining the consequences of such accidents, which are among the leading causes of mortality and disability in older adults. For this reason and considering the high number of older adults living alone, the development of automatic fall alerting systems has garnered significant research attention over the past decade. A key element for deploying a fall detection system (FDS) based on wearables is the wireless transmission method employed to transmit the medical alarms. In this regard, the vast majority of prototypes in the related literature utilize short-range technologies, such as Bluetooth, which must be complemented by the existence of a gateway device (e.g., a smartphone). In other studies, standards like Wi-Fi or 3G communications are proposed, which offer greater range but come with high power consumption, which can be unsuitable for most wearables, and higher service fees. In addition, they require reliable radio coverage, which is not always guaranteed in all application scenarios. An interesting alternative to these standards is Low Power Wide Area Network (LPWAN) technologies, which minimize both energy consumption and hardware costs while maximizing transmission range. This article provides a comprehensive search and review of that works in the literature that have implemented and evaluated wearable FDSs utilizing LPWAN interfaces to transmit alarms. The review systematically examines these proposals, considering various operational aspects and identifying key areas that have not yet been adequately addressed for the viable implementation of such detectors. Full article
(This article belongs to the Special Issue The Future of Healthcare: Biomedical Technology and Integrated Artificial Intelligence 2nd Edition)
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