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Bioengineering
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Advancements in Artificial Intelligence for Wearable Devices: A New Perspective...
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Advancements in Artificial Intelligence for Wearable Devices: A New Perspective on Healthcare Applications

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: 31 August 2025 | Viewed by 8615

Special Issue Editors

Dr. Maria Giovanna Bianco

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Guest Editor
Neuroscience Research Center, Department of Medical and Surgical Sciences, Magna Graecia University of Catanzaro, Viale Europa, 88100 Catanzaro, Italy
Interests: biomedical signal processing (EMG, EEG, ECoG, and LFP); wearable medical devices; machine learning; structural MRI analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Salvatore Pullano

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Guest Editor
Biomedical Applications Technologies & Sensors (BATS) Laboratory, Department of Health Sciences, Magna Graecia University of Catanzaro, Viale Europa, 88100 Catanzaro, Italy
Interests: sensors; biomedical signal processing; ion-sensitive field-effect transistors; PH sensors; sensing; wearable medical devices; electrocardiography; electrodes; textiles
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Syed Kamrul Islam

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Guest Editor
Department of Electrical Engineering and Computer Science, The University of Missouri-Columbia (MIZZOU), Columbia, MO 65211, USA
Interests: analog/mixed-signal integrated circuits; monolithic sensors; semiconductor devices
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Today, wearable devices (WDs) have emerged as an interesting frontier for innovation, poised to shape the future of human interaction with technology. The progress in sensing technologies and integrated electronic circuits has facilitated the creation of sophisticated devices capable of integrating sensors.

Sensor technology and electronic interfaces have been receiving growing interest from scientists as promising developments have been made. The high degree of miniaturization of classic measurement techniques has led to the realization of complex analytical systems including integrated sensors as in lab-on-a-chip. This new class of sensors extensively contributes to a broad range of activities with widespread applications including biology and medicine. Recent advances in artificial intelligence (AI) provide opportunities to reveal hidden information in biosignals that is not apparent using conventional methods of analysis. Beyond mere accessories, wearables play a pivotal role in healthcare, and the incorporation of AI technologies has played a crucial role in rapidly advancing the field.

This Special Issue aims to collect original scientific papers that apply novel or state-of-the-art AI technologies on wearable devices. We encourage original scientific papers about the application of machine learning and deep learning and real-time monitoring using WDs. Systematic reviews or meta-analyses are also welcome.

Dr. Maria Giovanna Bianco
Dr. Salvatore Pullano
Prof. Dr. Syed Kamrul Islam
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. Bioengineering 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 2700 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

  • wearable devices
  • artificial lntelligence
  • machine learning
  • biomedical signal processing
  • deep learning
  • bio-inspired systems
  • biosensor devices

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Published Papers (4 papers)

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19 pages, 1144 KiB  
Article
Optimizing Input Feature Sets Using Catch-22 and Personalization for an Accurate and Reliable Estimation of Continuous, Cuffless Blood Pressure
by Rajesh S. Kasbekar, Srinivasan Radhakrishnan, Songbai Ji, Anita Goel and Edward A. Clancy
Bioengineering 2025, 12(5), 493; https://doi.org/10.3390/bioengineering12050493 - 6 May 2025
Viewed by 494
Abstract
Nocturnal monitoring of continuous, cuffless blood pressure (BP) can unleash a whole new world for the prognostication of cardiovascular and other diseases due to its strong predictive capability. Nevertheless, the lack of an accurate and reliable method, primarily due to confounding variables, has [...] Read more.
Nocturnal monitoring of continuous, cuffless blood pressure (BP) can unleash a whole new world for the prognostication of cardiovascular and other diseases due to its strong predictive capability. Nevertheless, the lack of an accurate and reliable method, primarily due to confounding variables, has prevented its widespread clinical adoption. Herein, we demonstrate how optimized machine learning using the Catch-22 features, when applied to the photoplethysmogram waveform and personalized with direct BP data through transfer learning, can accurately estimate systolic and diastolic BP. After training with a hemodynamically compromised VitalDB “calibration-free” dataset (n = 1293), the systolic and diastolic BP tested on a distinct VitalDB dataset that met AAMI criteria (n = 116) had acceptable error biases of −1.85 mm Hg and 0.11 mm Hg, respectively [within the 5 mm Hg IEC/ANSI/AAMI 80601-2-30, 2018 standard], but standard deviation (SD) errors of 19.55 mm Hg and 11.55 mm Hg, respectively [exceeding the stipulated 8 mm Hg limit]. However, personalization using an initial calibration data segment and subsequent use of transfer learning to fine-tune the pretrained model produced acceptable mean (−1.31 mm Hg and 0.10 mm Hg) and SD (7.91 mm Hg and 4.59 mm Hg) errors for systolic and diastolic BP, respectively. Levene’s test for variance found that the personalization method significantly outperformed (p < 0.05) the calibration-free method, but there was no difference between three machine learning methods. Optimized multimodal Catch-22 features, coupled with personalization, demonstrate great promise in the clinical adoption of continuous, cuffless blood pressure estimation in applications such as nocturnal BP monitoring. Full article
(This article belongs to the Special Issue Advancements in Artificial Intelligence for Wearable Devices: A New Perspective on Healthcare Applications)
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14 pages, 1739 KiB  
Article
Older Adult Fall Risk Prediction with Deep Learning and Timed Up and Go (TUG) Test Data
by Josu Maiora, Chloe Rezola-Pardo, Guillermo García, Begoña Sanz and Manuel Graña
Bioengineering 2024, 11(10), 1000; https://doi.org/10.3390/bioengineering11101000 - 5 Oct 2024
Cited by 1 | Viewed by 2981
Abstract
Falls are a major health hazard for older adults; therefore, in the context of an aging population, predicting the risk of a patient suffering falls in the near future is of great impact for health care systems. Currently, the standard prospective fall risk [...] Read more.
Falls are a major health hazard for older adults; therefore, in the context of an aging population, predicting the risk of a patient suffering falls in the near future is of great impact for health care systems. Currently, the standard prospective fall risk assessment instrument relies on a set of clinical and functional mobility assessment tools, one of them being the Timed Up and Go (TUG) test. Recently, wearable inertial measurement units (IMUs) have been proposed to capture motion data that would allow for the building of estimates of fall risk. The hypothesis of this study is that the data gathered from IMU readings while the patient is performing the TUG test can be used to build a predictive model that would provide an estimate of the probability of suffering a fall in the near future, i.e., assessing prospective fall risk. This study applies deep learning convolutional neural networks (CNN) and recurrent neural networks (RNN) to build such predictive models based on features extracted from IMU data acquired during TUG test realizations. Data were obtained from a cohort of 106 older adults wearing wireless IMU sensors with sampling frequencies of 100 Hz while performing the TUG test. The dependent variable is a binary variable that is true if the patient suffered a fall in the six-month follow-up period. This variable was used as the output variable for the supervised training and validations of the deep learning architectures and competing machine learning approaches. A hold-out validation process using 75 subjects for training and 31 subjects for testing was repeated one hundred times to obtain robust estimations of model performances At each repetition, 5-fold cross-validation was carried out to select the best model over the training subset. Best results were achieved by a bidirectional long short-term memory (BLSTM), obtaining an accuracy of 0.83 and AUC of 0.73 with good sensitivity and specificity values. Full article
(This article belongs to the Special Issue Advancements in Artificial Intelligence for Wearable Devices: A New Perspective on Healthcare Applications)
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21 pages, 360 KiB  
Article
Automatic Classification of Anomalous ECG Heartbeats from Samples Acquired by Compressed Sensing
by Enrico Picariello, Francesco Picariello, Ioan Tudosa, Sreeraman Rajan and Luca De Vito
Bioengineering 2024, 11(9), 883; https://doi.org/10.3390/bioengineering11090883 - 31 Aug 2024
Viewed by 1593
Abstract
In this paper, a method for the classification of anomalous heartbeats from compressed ECG signals is proposed. The method operating on signals acquired by compressed sensing is based on a feature extraction stage consisting of the evaluation of the Discrete Cosine Transform (DCT) [...] Read more.
In this paper, a method for the classification of anomalous heartbeats from compressed ECG signals is proposed. The method operating on signals acquired by compressed sensing is based on a feature extraction stage consisting of the evaluation of the Discrete Cosine Transform (DCT) coefficients of the compressed signal and a classification stage performed by means of a set of k-nearest neighbor ensemble classifiers. The method was preliminarily tested on five classes of anomalous heartbeats, and it achieved a classification accuracy of 99.40%. Full article
(This article belongs to the Special Issue Advancements in Artificial Intelligence for Wearable Devices: A New Perspective on Healthcare Applications)
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18 pages, 6176 KiB  
Article
On Automated Object Grasping for Intelligent Prosthetic Hands Using Machine Learning
by Jethro Odeyemi, Akinola Ogbeyemi, Kelvin Wong and Wenjun Zhang
Bioengineering 2024, 11(2), 108; https://doi.org/10.3390/bioengineering11020108 - 24 Jan 2024
Cited by 3 | Viewed by 2558
Abstract
Prosthetic technology has witnessed remarkable advancements, yet challenges persist in achieving autonomous grasping control while ensuring the user’s experience is not compromised. Current electronic prosthetics often require extensive training for users to gain fine motor control over the prosthetic fingers, hindering their usability [...] Read more.
Prosthetic technology has witnessed remarkable advancements, yet challenges persist in achieving autonomous grasping control while ensuring the user’s experience is not compromised. Current electronic prosthetics often require extensive training for users to gain fine motor control over the prosthetic fingers, hindering their usability and acceptance. To address this challenge and improve the autonomy of prosthetics, this paper proposes an automated method that leverages computer vision-based techniques and machine learning algorithms. In this study, three reinforcement learning algorithms, namely Soft Actor-Critic (SAC), Deep Q-Network (DQN), and Proximal Policy Optimization (PPO), are employed to train agents for automated grasping tasks. The results indicate that the SAC algorithm achieves the highest success rate of 99% among the three algorithms at just under 200,000 timesteps. This research also shows that an object’s physical characteristics can affect the agent’s ability to learn an optimal policy. Moreover, the findings highlight the potential of the SAC algorithm in developing intelligent prosthetic hands with automatic object-gripping capabilities. Full article
(This article belongs to the Special Issue Advancements in Artificial Intelligence for Wearable Devices: A New Perspective on Healthcare Applications)
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