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4 pages, 176 KiB

Open AccessEditorial

Robotics, IoT and AI Technologies in Bioengineering

by Luigi Bibbò and Alessia Bramanti

Appl. Sci. 2025, 15(9), 5070; https://doi.org/10.3390/app15095070 - 2 May 2025

Viewed by 473

Abstract

Bioengineering is a discipline that integrates aspects of traditional engineering with health-related issues [...] Full article

(This article belongs to the Special Issue Robotics, IoT and AI Technologies in Bioengineering)

Research

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18 pages, 7376 KiB

Open AccessArticle

Smart Electronic Device-Based Monitoring of SAR and Temperature Variations in Indoor Human Tissue Interaction

by Filippo Laganà, Luigi Bibbò, Salvatore Calcagno, Domenico De Carlo, Salvatore A. Pullano, Danilo Pratticò and Giovanni Angiulli

Appl. Sci. 2025, 15(5), 2439; https://doi.org/10.3390/app15052439 - 25 Feb 2025

Cited by 12 | Viewed by 864

Abstract

The daily use of devices generating electric and magnetic fields has led to potential human overexposure in home and work environments. This paper assesses the possible effects of electric fields on human health at low and high frequencies. It presents an electronic monitoring [...] Read more.

The daily use of devices generating electric and magnetic fields has led to potential human overexposure in home and work environments. This paper assesses the possible effects of electric fields on human health at low and high frequencies. It presents an electronic monitoring device that captures the incidence of specific absorption rate (SAR) and temperature variation (∆T) on the human body. The system transmits data to a cloud platform, where a feedforward neural network (FFNN) processes the received information. SAR and surface temperature values are detected in an indoor environment, monitoring stationary and moving subjects. The results effectively assess temperature distribution due to electromagnetic fields. The prototype detected temperature peaks and high SAR values when the subjects remained motionless. Predictive analysis confirms the need for workplaces with materials shielding external electromagnetic signals and attenuating internal sources. Moderate mobile phone use could lower SAR and temperature values. Full article

(This article belongs to the Special Issue Robotics, IoT and AI Technologies in Bioengineering)

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24 pages, 5325 KiB

Open AccessArticle

Improved Honey Badger Algorithm and Its Application to K-Means Clustering

by Shuhao Jiang, Huimin Gao, Yizi Lu, Haoran Song, Yong Zhang and Mengqian Wang

Appl. Sci. 2025, 15(2), 718; https://doi.org/10.3390/app15020718 - 13 Jan 2025

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Abstract

As big data continues to evolve, cluster analysis still has a place. Among them, the K-means algorithm is the most widely used method in the field of clustering, which can cause unstable clustering results due to the random selection of the initial clustering [...] Read more.

As big data continues to evolve, cluster analysis still has a place. Among them, the K-means algorithm is the most widely used method in the field of clustering, which can cause unstable clustering results due to the random selection of the initial clustering center of mass. In this paper, an improved honey badger optimization algorithm is proposed: (1) The population is initialized using sin chaos to make the population uniformly distributed. (2) The density factor is improved to enhance the optimization accuracy of the population. (3) A nonlinear inertia weight factor is introduced to prevent honey badger individuals from relying on the behavior of past individuals during position updating. (4) To improve the diversity of solutions, random opposition learning is performed on the optimal individuals. The improved algorithm outperforms the comparison algorithm in terms of performance through experiments on 23 benchmark test functions. Finally, in this paper, the improved algorithm is applied to K-means clustering and experiments are conducted on three data sets from the UCI data set. The results show that the improved honey badger optimized K-means algorithm improves the clustering effect over the traditional K-means algorithm. Full article

(This article belongs to the Special Issue Robotics, IoT and AI Technologies in Bioengineering)

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22 pages, 2339 KiB

Open AccessArticle

Signal Acquisition and Algorithm Design for Bioimpedance-Based Heart Rate Estimation from the Wrist

by Didzis Lapsa, Margus Metshein, Andrei Krivošei, Rims Janeliukstis, Olev Märtens and Atis Elsts

Appl. Sci. 2024, 14(21), 9632; https://doi.org/10.3390/app14219632 - 22 Oct 2024

Cited by 1 | Viewed by 1641

Abstract

Background: Heart rate (HR) is a critical biomarker that provides insights into overall health, stress levels, and the autonomic nervous system. Pulse wave signals contain valuable information about the cardiovascular system and heart status. However, signal acquisition in wearables poses challenges, particularly when [...] Read more.

Background: Heart rate (HR) is a critical biomarker that provides insights into overall health, stress levels, and the autonomic nervous system. Pulse wave signals contain valuable information about the cardiovascular system and heart status. However, signal acquisition in wearables poses challenges, particularly when using electrical sensors, due to factors like the distance from the heart, body movement, and suboptimal electrode placement. Methods: Electrical bioimpedance (EBI) measurements using bipolar and tetrapolar electrode systems were employed for pulse wave signal acquisition from the wrist in both perpendicular and distal configurations. Signal preprocessing techniques, including baseline removal via Hankel matrix methods, normalization, cross-correlation, and peak detection, were applied to improve signal quality. This study describes the combination of sensor-level signal acquisition and processing for accurate wearable HR estimation. Results: The bipolar system was shown to produce larger

Δ Z (t)

, while the tetrapolar system demonstrated higher sensitivity. Distal placement of the electrodes yielded greater

Δ Z (t)

(up to 0.231

Ω

) when targeting both wrist arteries. Bandpass filtering resulted in a better signal-to-noise ratio (SNR), achieving 3.6 dB for the best bipolar setup and 4.8 dB for the tetrapolar setup, compared to 2.6 and 3.3 dB SNR, respectively, with the Savitzky–Golay filter. The custom HR estimation algorithm presented in this paper demonstrated improved accuracy over a reference method, achieving an average error of 1.8 beats per minute for the best bipolar setup, with a mean absolute percentage error (MAPE) of 8%. Conclusions: The analysis supports the feasibility of using bipolar electrode setups on the wrist and highlights the importance of electrode positioning relative to the arteries. The proposed signal processing method, featuring a preprocessing pipeline and HR estimation algorithm, provides a proof-of-concept demonstration for HR estimation from EBI signals acquired at the wrist. Full article

(This article belongs to the Special Issue Robotics, IoT and AI Technologies in Bioengineering)

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23 pages, 11097 KiB

Open AccessArticle

Multimodal Framework for Fine and Gross Upper-Limb Motor Coordination Assessment Using Serious Games and Robotics

by Edwin Daniel Oña, Norali Pernalete and Alberto Jardón

Appl. Sci. 2024, 14(18), 8175; https://doi.org/10.3390/app14188175 - 11 Sep 2024

Viewed by 1221

Abstract

A critical element of neurological function is eye–hand coordination: the ability of our vision system to coordinate the information received through the eyes to control, guide, and direct the hands to accomplish a task. Recent evidence shows that this ability can be disturbed [...] Read more.

A critical element of neurological function is eye–hand coordination: the ability of our vision system to coordinate the information received through the eyes to control, guide, and direct the hands to accomplish a task. Recent evidence shows that this ability can be disturbed by strokes or other neurological disorders, with critical consequences for motor behaviour. This paper presents a system based on serious games and multimodal devices aimed at improving the assessment of eye–hand coordination. The system implements gameplay that involves drawing specific patterns (labyrinths) to capture hand trajectories. The user can draw the path using multimodal devices such as a mouse, a stylus with a tablet, or robotic devices. Multimodal input devices can allow for the evaluation of complex coordinated movements of the upper limb that involve the synergistic motion of arm joints, depending on the device. A preliminary test of technological validation with healthy volunteers was conducted in the laboratory. The Dynamic Time Warping (DTW) index was used to compare hand trajectories without considering time-series lag. The results suggest that this multimodal framework allows for measuring differences between fine and gross motor skills. Moreover, the results support the viability of this system for developing a high-resolution metric for measuring eye–hand coordination in neurorehabilitation. Full article

(This article belongs to the Special Issue Robotics, IoT and AI Technologies in Bioengineering)

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25 pages, 2289 KiB

Open AccessArticle

Federated Learning: Centralized and P2P for a Siamese Deep Learning Model for Diabetes Foot Ulcer Classification

by Mohammud Shaad Ally Toofanee, Mohamed Hamroun, Sabeena Dowlut, Karim Tamine, Vincent Petit, Anh Kiet Duong and Damien Sauveron

Appl. Sci. 2023, 13(23), 12776; https://doi.org/10.3390/app132312776 - 28 Nov 2023

Cited by 3 | Viewed by 1990

Abstract

It is a known fact that AI models need massive amounts of data for training. In the medical field, the data are not necessarily available at a single site but are distributed over several sites. In the field of medical data sharing, particularly [...] Read more.

It is a known fact that AI models need massive amounts of data for training. In the medical field, the data are not necessarily available at a single site but are distributed over several sites. In the field of medical data sharing, particularly among healthcare institutions, the need to maintain the confidentiality of sensitive information often restricts the comprehensive utilization of real-world data in machine learning. To address this challenge, our study experiments with an innovative approach using federated learning to enable collaborative model training without compromising data confidentiality and privacy. We present an adaptation of the federated averaging algorithm, a predominant centralized learning algorithm, to a peer-to-peer federated learning environment. This adaptation led to the development of two extended algorithms: Federated Averaging Peer-to-Peer and Federated Stochastic Gradient Descent Peer-to-Peer. These algorithms were applied to train deep neural network models for the detection and monitoring of diabetic foot ulcers, a critical health condition among diabetic patients. This study compares the performance of Federated Averaging Peer-to-Peer and Federated Stochastic Gradient Descent Peer-to-Peer with their centralized counterparts in terms of model convergence and communication costs. Additionally, we explore enhancements to these algorithms using targeted heuristics based on client identities and f1-scores for each class. The results indicate that models utilizing peer-to-peer federated averaging achieve a level of convergence that is comparable to that of models trained via conventional centralized federated learning approaches. This represents a notable progression in the field of ensuring the confidentiality and privacy of medical data for training machine learning models. Full article

(This article belongs to the Special Issue Robotics, IoT and AI Technologies in Bioengineering)

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Review

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19 pages, 691 KiB

Open AccessReview

Artificial Intelligence in Decoding Ocular Enigmas: A Literature Review of Choroidal Nevus and Choroidal Melanoma Assessment

by Konstantina-Eleni Karamanli, Eirini Maliagkani, Petros Petrou, Elpiniki Papageorgiou and Ilias Georgalas

Appl. Sci. 2025, 15(7), 3565; https://doi.org/10.3390/app15073565 - 25 Mar 2025

Viewed by 575

Abstract

This review examines the role of artificial intelligence (AI) in differentiating choroidal nevus (CN) from choroidal melanoma (CM), focusing on diagnosis, classification, segmentation, and prediction of malignant transformation. A literature search was performed in PubMed, Google Scholar, and Science Direct up to December [...] Read more.

This review examines the role of artificial intelligence (AI) in differentiating choroidal nevus (CN) from choroidal melanoma (CM), focusing on diagnosis, classification, segmentation, and prediction of malignant transformation. A literature search was performed in PubMed, Google Scholar, and Science Direct up to December 2024. Eight studies met all the inclusion criteria, evaluating machine learning (ML) and deep learning (DL) applications for CN and CM assessment using various ophthalmic imaging modalities. Performance varied across AI models. U-Net achieved 100% sensitivity and an AUC of 0.88, while DenseNet121 reached an AUC of 0.9781, LASSO logistic regression an AUC of 0.88, RETFound (a self-supervised learning model) had an AUCROC of 0.92, and ResNet50 an accuracy of 92.65% in classification tasks. DeepLabv3 achieved Dice scores of 0.87 (melanoma) and 0.81 (nevi) in lesion-based segmentation, while nnU-Net yielded a Dice score of 0.78 and a recall of 0.77 for pigmented lesion segmentation. SAINT (XGBoost-based) demonstrated a strong predictive performance (AUC: 0.910), confirming its effectiveness in ophthalmic imaging. These results highlight the potential of AI models as effective diagnostic tools in ocular oncology. However, further research is needed to improve model generalizability, enhance dataset diversity, and facilitate clinical integration. Full article

(This article belongs to the Special Issue Robotics, IoT and AI Technologies in Bioengineering)

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16 pages, 1842 KiB

Open AccessReview

Artificial Intelligence-Based Methods for Drug Repurposing and Development in Cancer

by Sara Herráiz-Gil, Elisa Nygren-Jiménez, Diana N. Acosta-Alonso, Carlos León and Sara Guerrero-Aspizua

Appl. Sci. 2025, 15(5), 2798; https://doi.org/10.3390/app15052798 - 5 Mar 2025

Viewed by 3604

Abstract

Drug discovery and development remains a complex and time-consuming process, often hindered by high costs and low success rates. In the big data era, artificial intelligence (AI) has emerged as a promising tool to accelerate and optimize these processes, particularly in the field [...] Read more.

Drug discovery and development remains a complex and time-consuming process, often hindered by high costs and low success rates. In the big data era, artificial intelligence (AI) has emerged as a promising tool to accelerate and optimize these processes, particularly in the field of oncology. This review explores the application of AI-based methods for drug repurposing and natural product-inspired drug design in cancer, focusing on their potential to address the challenges and limitations of traditional drug discovery approaches. We delve into various AI-based approaches (machine learning, deep learning, and others) that are currently being employed for these purposes, and the role of experimental techniques in these approaches. By systematically reviewing the literature, we aim to provide a comprehensive overview of the current state of AI-assisted cancer drug discovery workflows, highlighting AI’s contributions to accelerating drug development, reducing costs, and improving therapeutic outcomes. This review also discusses the challenges and opportunities associated with the integration of AI into the drug discovery pipeline, such as data quality, interpretability, and ethical considerations. Full article

(This article belongs to the Special Issue Robotics, IoT and AI Technologies in Bioengineering)

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