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Applied Sciences
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Robotics, IoT and AI Technologies in Bioengineering
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Robotics, IoT and AI Technologies in Bioengineering

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".

Deadline for manuscript submissions: 20 February 2025 | Viewed by 4950

Special Issue Editors

Dr. Luigi Bibbò

E-Mail Website
Guest Editor
Department of Civil, Energy, Environmental and Materials Engineering (DICEAM), Mediterranean University of Reggio Calabria, Reggio Calabria, Italy
Interests: biomedical signal processing and sensors; photonics; optical fibers; MEMS; metamaterials; nanotechnology; artificial intelligence; neural network; virtual reality; augmented reality; indoor navigation
Special Issues, Collections and Topics in MDPI journals
Dr. Alessia Bramanti

E-Mail Website
Guest Editor
Department of Medicine, Surgery and Dentistry, University of Salerno, 84081 Baronissi, Italy
Interests: artificial intelligence; machine learning; image processing; neural networks; machine intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Bioengineering is a discipline that blends many aspects of traditional engineering fields with health care issues. The main objective is the creation of digital tools, devices and software platforms; as well as the implementation of advanced tools, from IoT, Artificial Intelligence and robotics to Cloud computing, smart wearables and intelligent analytics, with the ultimate aim of improving the quality and duration of life for patients. The evolution of Bioengineering is closely connected with developments in automation, nanomaterials engineering, artificial intelligence and neuroscience. From an application point of view, for example, Artificial Intelligence has proven to be efficient in many ways in the medical field, from the improvement of image-based diagnostics, analysis of biological signals, recognition of human activities through accelerometric signals, navigation guidance for subjects with cognitive problems, to the design of neuro-integrated prosthetic systems and compatible organ tissues for transplantation, surgery, prediction of behavior and nervous responses to stimuli. All this was possible thanks to the acquisition of huge volumes of digitized data and the machine learning technique. Robotics is also key branch in the field of surgery, enabling for minimally invasive surgeries and for the automatic monitoring of surgical instruments to assist the operator. Telepresence robots have also been designed to help socially isolated people as well as aid with rehabilitation. They are also used as wearable devices for injury prevention. The biomedical applications of IoT are now present in remote patient management, the monitoring of Parkinson's and Alzheimer's patients, vital data monitoring, depression monitoring via smartwatch, glucose monitoring and efficient drug management. It is essential that certain functionalities such as interoperability between all devices, platforms and technologies, and data security are ensured. In the literature, there are different application systems oriented toward health care that can help a sick person maintain or improve their independence and security.

The aim of this research topic is to improve the opportunities that different technologies can offer in improving the quality and duration of life.

Dr. Luigi Bibbò
Dr. Alessia Bramanti
Guest Editors

Manuscript Submission Information

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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. Applied Sciences 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 2400 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

  • Artificial Intelligence
  • IoT
  • human–robot interactions
  • wearable sensors
  • virtual reality/augmented reality (VR/AR)

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Published Papers (4 papers)

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Research

24 pages, 5325 KiB  
Article
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
Viewed by 318
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  
Article
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
Viewed by 1135
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 ΔZ(t), while the tetrapolar system demonstrated higher sensitivity. Distal placement of the electrodes yielded greater ΔZ(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  
Article
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 905
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  
Article
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 1 | Viewed by 1662
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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