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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 1213

Special Issue Editors


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Guest Editor
Politecnico di Torino, DET, 10129 Turin, Italy
Interests: artificial neural networks; smart sensors; wearable medical devices; IOT; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

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

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Keywords

  • wearable sensors
  • electronic health
  • telemedicine
  • artificial intelligence
  • machine learning
  • deep neural networks
  • human health
  • vital signs

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Published Papers (2 papers)

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Research

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 484
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
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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 494
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
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