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Wearable Sensors and Artificial Intelligence for Measuring Human Vital Signs: 2nd Edition

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Wearables".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 3962

Special Issue Editors

Prof. Dr. Eros Pasero

E-Mail Website
Guest Editor
DET, Politecnico di Torino, 10129 Turin, Italy
Interests: artificial neural networks; smart sensors; wearable medical devices; IOT; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Vincenzo Randazzo

E-Mail Website
Guest Editor
Department of Electronics and Telecommunications, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy
Interests: neural networks; Artificial Intelligence; mobile health; Telemedicine; IoT; ECG; topology analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Wearable sensors can be extremely useful in providing accurate and reliable information about people’s activities and behaviors. In recent times, there has been a surge in the usage of wearable sensors, especially in the medical sciences, where they have many applications in monitoring physiological activities. In the medical field, it is possible to monitor patients’ body temperature, heart rate, brain activity, muscle motion, and other critical data. It is important for us to have very simple sensors that could be worn on the body to perform standard medical monitoring. The extraction of relevant features is the most challenging part of the mobile and wearable-sensor-based human activity recognition pipeline. Feature extraction influences the algorithm’s performance and reduces computation time and complexity. The complexity and variety of body activities makes it difficult to quickly, accurately, and automatically recognize body activities. To solve this problem, Artificial Intelligence is becoming more and more important. Following the emergence of deep learning and increased computational power, these methods have been adopted for automatic feature learning in several areas such as health, image classification, and, recently, for feature extraction and the classification of simple and complex human activity recognition information from mobile and wearable sensors. Human activity recognition technology that analyzes data acquired from various types of sensing devices, including vision sensors and embedded sensors, has motivated the development of various context-aware applications in emerging domains, e.g., the Internet of Things (IoT) and healthcare.

The objective of this Special Issue is to collect state-of-the-art research contributions, tutorials, and position papers that address the broad challenges that have been faced in the development of wearable-sensor-based solutions in the field of human health. Original papers describing completed and unpublished work that are not currently under review by any other journal, magazine, or conference are solicited.

Prof. Dr. Eros Pasero
Dr. Vincenzo Randazzo
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. Sensors 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 2600 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 sensors
  • electronic health
  • telemedicine
  • artificial intelligence
  • machine learning
  • deep neural networks
  • human health
  • vital signs

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Related Special Issue

Published Papers (4 papers)

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Research

17 pages, 5344 KiB  
Article
The Effects of Competition on Exercise Intensity and the User Experience of Exercise during Virtual Reality Bicycling for Young Adults
by John L. Palmieri and Judith E. Deutsch
Sensors 2024, 24(21), 6873; https://doi.org/10.3390/s24216873 - 26 Oct 2024
Viewed by 947
Abstract
Background: Regular moderate–vigorous intensity exercise is recommended for adults as it can improve longevity and reduce health risks associated with a sedentary lifestyle. However, there are barriers to achieving intense exercise that may be addressed using virtual reality (VR) as a tool to [...] Read more.
Background: Regular moderate–vigorous intensity exercise is recommended for adults as it can improve longevity and reduce health risks associated with a sedentary lifestyle. However, there are barriers to achieving intense exercise that may be addressed using virtual reality (VR) as a tool to promote exercise intensity and adherence, particularly through visual feedback and competition. The purpose of this work is to compare visual feedback and competition within fully immersive VR to enhance exercise intensity and user experience of exercise for young adults; and to describe and compare visual attention during each of the conditions. Methods: Young adults (21–34 years old) bicycled in three 5 min VR conditions (visual feedback, self-competition, and competition against others). Exercise intensity (cycling cadence and % of maximum heart rate) and visual attention (derived from a wearable eye tracking sensor) were measured continuously. User experience was measured by an intrinsic motivation questionnaire, perceived effort, and participant preference. A repeated-measures ANOVA with paired t-test post hoc tests was conducted to detect differences between conditions. Results: Participants exercised at a higher intensity and had higher intrinsic motivation in the two competitive conditions compared to visual feedback. Further, participants preferred the competitive conditions and only reached a vigorous exercise intensity during self-competition. Visual exploration was higher in visual feedback compared to self-competition. Conclusions: For young adults bicycling in VR, competition promoted higher exercise intensity and motivation compared to visual feedback. Full article
(This article belongs to the Special Issue Wearable Sensors and Artificial Intelligence for Measuring Human Vital Signs: 2nd Edition)
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10 pages, 509 KiB  
Communication
Computer Vision-Driven Movement Annotations to Advance fNIRS Pre-Processing Algorithms
by Andrea Bizzego, Alessandro Carollo, Burak Senay, Seraphina Fong, Cesare Furlanello and Gianluca Esposito
Sensors 2024, 24(21), 6821; https://doi.org/10.3390/s24216821 - 24 Oct 2024
Viewed by 635
Abstract
Functional near-infrared spectroscopy (fNIRS) is beneficial for studying brain activity in naturalistic settings due to its tolerance for movement. However, residual motion artifacts still compromise fNIRS data quality and might lead to spurious results. Although some motion artifact correction algorithms have been proposed [...] Read more.
Functional near-infrared spectroscopy (fNIRS) is beneficial for studying brain activity in naturalistic settings due to its tolerance for movement. However, residual motion artifacts still compromise fNIRS data quality and might lead to spurious results. Although some motion artifact correction algorithms have been proposed in the literature, their development and accurate evaluation have been challenged by the lack of ground truth information. This is because ground truth information is time- and labor-intensive to manually annotate. This work investigates the feasibility and reliability of a deep learning computer vision (CV) approach for automated detection and annotation of head movements from video recordings. Fifteen participants performed controlled head movements across three main rotational axes (head up/down, head left/right, bend left/right) at two speeds (fast and slow), and in different ways (half, complete, repeated movement). Sessions were video recorded and head movement information was obtained using a CV approach. A 1-dimensional UNet model (1D-UNet) that detects head movements from head orientation signals extracted via a pre-trained model (SynergyNet) was implemented. Movements were manually annotated as a ground truth for model evaluation. The model’s performance was evaluated using the Jaccard index. The model showed comparable performance between the training and test sets (J train = 0.954; J test = 0.865). Moreover, it demonstrated good and consistent performance at annotating movement across movement axes and speeds. However, performance varied by movement type, with the best results being obtained for repeated (J test = 0.941), followed by complete (J test = 0.872), and then half movements (J test = 0.826). This study suggests that the proposed CV approach provides accurate ground truth movement information. Future research can rely on this CV approach to evaluate and improve fNIRS motion artifact correction algorithms. Full article
(This article belongs to the Special Issue Wearable Sensors and Artificial Intelligence for Measuring Human Vital Signs: 2nd Edition)
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13 pages, 2020 KiB  
Article
Multilayer Perceptron-Based Wearable Exercise-Related Heart Rate Variability Predicts Anxiety and Depression in College Students
by Xiongfeng Li, Limin Zou and Haojie Li
Sensors 2024, 24(13), 4203; https://doi.org/10.3390/s24134203 - 28 Jun 2024
Viewed by 1043
Abstract
(1) Background: This study aims to investigate the correlation between heart rate variability (HRV) during exercise and recovery periods and the levels of anxiety and depression among college students. Additionally, the study assesses the accuracy of a multilayer perceptron-based HRV analysis in predicting [...] Read more.
(1) Background: This study aims to investigate the correlation between heart rate variability (HRV) during exercise and recovery periods and the levels of anxiety and depression among college students. Additionally, the study assesses the accuracy of a multilayer perceptron-based HRV analysis in predicting these emotional states. (2) Methods: A total of 845 healthy college students, aged between 18 and 22, participated in the study. Participants completed self-assessment scales for anxiety and depression (SAS and PHQ-9). HRV data were collected during exercise and for a 5-min period post-exercise. The multilayer perceptron neural network model, which included several branches with identical configurations, was employed for data processing. (3) Results: Through a 5-fold cross-validation approach, the average accuracy of HRV in predicting anxiety levels was 89.3% for no anxiety, 83.6% for mild anxiety, and 74.9% for moderate to severe anxiety. For depression levels, the average accuracy was 90.1% for no depression, 84.2% for mild depression, and 82.1% for moderate to severe depression. The predictive R-squared values for anxiety and depression scores were 0.62 and 0.41, respectively. (4) Conclusions: The study demonstrated that HRV during exercise and recovery in college students can effectively predict levels of anxiety and depression. However, the accuracy of score prediction requires further improvement. HRV related to exercise can serve as a non-invasive biomarker for assessing psychological health. Full article
(This article belongs to the Special Issue Wearable Sensors and Artificial Intelligence for Measuring Human Vital Signs: 2nd Edition)
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10 pages, 1087 KiB  
Article
Temporal Convolutional Neural Network-Based Prediction of Vascular Health in Elderly Women Using Photoplethysmography-Derived Pulse Wave during Exercise
by Yue Xiao, Guixian Wang and Haojie Li
Sensors 2024, 24(13), 4198; https://doi.org/10.3390/s24134198 - 28 Jun 2024
Viewed by 847
Abstract
(1) Background: The objective of this study was to predict the vascular health status of elderly women during exercise using pulse wave data and Temporal Convolutional Neural Networks (TCN); (2) Methods: A total of 492 healthy elderly women aged 60–75 years were recruited [...] Read more.
(1) Background: The objective of this study was to predict the vascular health status of elderly women during exercise using pulse wave data and Temporal Convolutional Neural Networks (TCN); (2) Methods: A total of 492 healthy elderly women aged 60–75 years were recruited for the study. The study utilized a cross-sectional design. Vascular endothelial function was assessed non-invasively using Flow-Mediated Dilation (FMD). Pulse wave characteristics were quantified using photoplethysmography (PPG) sensors, and motion-induced noise in the PPG signals was mitigated through the application of a recursive least squares (RLS) adaptive filtering algorithm. A fixed-load cycling exercise protocol was employed. A TCN was constructed to classify flow-mediated dilation (FMD) into “optimal”, “impaired”, and “at risk” levels; (3) Results: TCN achieved an average accuracy of 79.3%, 84.8%, and 83.2% in predicting FMD at the “optimal”, “impaired”, and “at risk” levels, respectively. The results of the analysis of variance (ANOVA) comparison demonstrated that the accuracy of the TCN in predicting FMD at the impaired and at-risk levels was significantly higher than that of Long Short-Term Memory (LSTM) networks and Random Forest algorithms; (4) Conclusions: The use of pulse wave data during exercise combined with the TCN for predicting the vascular health status of elderly women demonstrated high accuracy, particularly in predicting impaired and at-risk FMD levels. This indicates that the integration of exercise pulse wave data with TCN can serve as an effective tool for the assessment and monitoring of the vascular health of elderly women. Full article
(This article belongs to the Special Issue Wearable Sensors and Artificial Intelligence for Measuring Human Vital Signs: 2nd Edition)
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