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22 pages, 5049 KiB

Open AccessArticle

Spatial Visual Imagery (SVI)-Based Electroencephalograph Discrimination for Natural CAD Manipulation

by Beining Cao, Hongwei Niu, Jia Hao, Xiaonan Yang and Zinian Ye

Sensors 2024, 24(3), 785; https://doi.org/10.3390/s24030785 - 25 Jan 2024

Viewed by 908

With the increasing demand for natural interactions, people have realized that an intuitive Computer-Aided Design (CAD) interaction mode can reduce the complexity of CAD operation and improve the design experience. Although interaction modes like gaze and gesture are compatible with some complex CAD [...] Read more.

With the increasing demand for natural interactions, people have realized that an intuitive Computer-Aided Design (CAD) interaction mode can reduce the complexity of CAD operation and improve the design experience. Although interaction modes like gaze and gesture are compatible with some complex CAD manipulations, they still require people to express their design intentions physically. The brain contains design intentions implicitly and controls the corresponding body parts that execute the task. Therefore, building an end-to-end channel between the brain and computer as an auxiliary mode for CAD manipulation will allow people to send design intentions mentally and make their interaction more intuitive. This work focuses on the 1-D translation scene and studies a spatial visual imagery (SVI) paradigm to provide theoretical support for building an electroencephalograph (EEG)-based brain–computer interface (BCI) for CAD manipulation. Based on the analysis of three spatial EEG features related to SVI (e.g., common spatial patterns, cross-correlation, and coherence), a multi-feature fusion-based discrimination model was built for SVI. The average accuracy of the intent discrimination of 10 subjects was 86%, and the highest accuracy was 93%. The method proposed was verified to be feasible for discriminating the intentions of CAD object translation with good classification performance. This work further proves the potential of BCI in natural CAD manipulation. Full article

(This article belongs to the Special Issue Wearable Sensors and Artificial Intelligence for Measuring Human Vital Signs)

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16 pages, 1842 KiB

Open AccessArticle

Artificial Intelligence ECG Analysis in Patients with Short QT Syndrome to Predict Life-Threatening Arrhythmic Events

by Eros Pasero, Fiorenzo Gaita, Vincenzo Randazzo, Pierre Meynet, Sergio Cannata, Philippe Maury and Carla Giustetto

Sensors 2023, 23(21), 8900; https://doi.org/10.3390/s23218900 - 1 Nov 2023

Cited by 3 | Viewed by 1862

Abstract

Short QT syndrome (SQTS) is an inherited cardiac ion-channel disease related to an increased risk of sudden cardiac death (SCD) in young and otherwise healthy individuals. SCD is often the first clinical presentation in patients with SQTS. However, arrhythmia risk stratification is presently [...] Read more.

Short QT syndrome (SQTS) is an inherited cardiac ion-channel disease related to an increased risk of sudden cardiac death (SCD) in young and otherwise healthy individuals. SCD is often the first clinical presentation in patients with SQTS. However, arrhythmia risk stratification is presently unsatisfactory in asymptomatic patients. In this context, artificial intelligence-based electrocardiogram (ECG) analysis has never been applied to refine risk stratification in patients with SQTS. The purpose of this study was to analyze ECGs from SQTS patients with the aid of different AI algorithms to evaluate their ability to discriminate between subjects with and without documented life-threatening arrhythmic events. The study group included 104 SQTS patients, 37 of whom had a documented major arrhythmic event at presentation and/or during follow-up. Thirteen ECG features were measured independently by three expert cardiologists; then, the dataset was randomly divided into three subsets (training, validation, and testing). Five shallow neural networks were trained, validated, and tested to predict subject-specific class (non-event/event) using different subsets of ECG features. Additionally, several deep learning and machine learning algorithms, such as Vision Transformer, Swin Transformer, MobileNetV3, EfficientNetV2, ConvNextTiny, Capsule Networks, and logistic regression were trained, validated, and tested directly on the scanned ECG images, without any manual feature extraction. Furthermore, a shallow neural network, a 1-D transformer classifier, and a 1-D CNN were trained, validated, and tested on ECG signals extracted from the aforementioned scanned images. Classification metrics were evaluated by means of sensitivity, specificity, positive and negative predictive values, accuracy, and area under the curve. Results prove that artificial intelligence can help clinicians in better stratifying risk of arrhythmia in patients with SQTS. In particular, shallow neural networks’ processing features showed the best performance in identifying patients that will not suffer from a potentially lethal event. This could pave the way for refined ECG-based risk stratification in this group of patients, potentially helping in saving the lives of young and otherwise healthy individuals. Full article

(This article belongs to the Special Issue Wearable Sensors and Artificial Intelligence for Measuring Human Vital Signs)

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13 pages, 785 KiB

Open AccessArticle

Transformer-Based Approach to Melanoma Detection

by Giansalvo Cirrincione, Sergio Cannata, Giovanni Cicceri, Francesco Prinzi, Tiziana Currieri, Marta Lovino, Carmelo Militello, Eros Pasero and Salvatore Vitabile

Sensors 2023, 23(12), 5677; https://doi.org/10.3390/s23125677 - 17 Jun 2023

Cited by 6 | Viewed by 2186

Abstract

Melanoma is a malignant cancer type which develops when DNA damage occurs (mainly due to environmental factors such as ultraviolet rays). Often, melanoma results in intense and aggressive cell growth that, if not caught in time, can bring one toward death. Thus, early [...] Read more.

Melanoma is a malignant cancer type which develops when DNA damage occurs (mainly due to environmental factors such as ultraviolet rays). Often, melanoma results in intense and aggressive cell growth that, if not caught in time, can bring one toward death. Thus, early identification at the initial stage is fundamental to stopping the spread of cancer. In this paper, a ViT-based architecture able to classify melanoma versus non-cancerous lesions is presented. The proposed predictive model is trained and tested on public skin cancer data from the ISIC challenge, and the obtained results are highly promising. Different classifier configurations are considered and analyzed in order to find the most discriminating one. The best one reached an accuracy of 0.948, sensitivity of 0.928, specificity of 0.967, and AUROC of 0.948. Full article

(This article belongs to the Special Issue Wearable Sensors and Artificial Intelligence for Measuring Human Vital Signs)

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13 pages, 1854 KiB

Open AccessArticle

Exploring the Applicability of Physiological Monitoring to Manage Physical Fatigue in Firefighters

by Denisse Bustos, Ricardo Cardoso, Diogo D. Carvalho, Joana Guedes, Mário Vaz, José Torres Costa, João Santos Baptista and Ricardo J. Fernandes

Sensors 2023, 23(11), 5127; https://doi.org/10.3390/s23115127 - 27 May 2023

Cited by 1 | Viewed by 1665

Abstract

Physical fatigue reduces productivity and quality of work while increasing the risk of injuries and accidents among safety-sensitive professionals. To prevent its adverse effects, researchers are developing automated assessment methods that, despite being highly accurate, require a comprehensive understanding of underlying mechanisms and [...] Read more.

Physical fatigue reduces productivity and quality of work while increasing the risk of injuries and accidents among safety-sensitive professionals. To prevent its adverse effects, researchers are developing automated assessment methods that, despite being highly accurate, require a comprehensive understanding of underlying mechanisms and variables’ contributions to determine their real-life applicability. This work aims to evaluate the performance variations of a previously developed four-level physical fatigue model when alternating its inputs to have a comprehensive view of the impact of each physiological variable on the model’s functioning. Data from heart rate, breathing rate, core temperature and personal characteristics from 24 firefighters during an incremental running protocol were used to develop the physical fatigue model based on an XGBoosted tree classifier. The model was trained 11 times with different input combinations resulting from alternating four groups of features. Performance measures from each case showed that heart rate is the most relevant signal for estimating physical fatigue. Breathing rate and core temperature enhanced the model when combined with heart rate but showed poor performance individually. Overall, this study highlights the advantage of using more than one physiological measure for improving physical fatigue modelling. The findings can contribute to variables and sensor selection in occupational applications and as the foundation for further field research. Full article

(This article belongs to the Special Issue Wearable Sensors and Artificial Intelligence for Measuring Human Vital Signs)

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16 pages, 702 KiB

Open AccessArticle

A Method for Stress Detection Using Empatica E4 Bracelet and Machine-Learning Techniques

by Sara Campanella, Ayham Altaleb, Alberto Belli, Paola Pierleoni and Lorenzo Palma

Sensors 2023, 23(7), 3565; https://doi.org/10.3390/s23073565 - 29 Mar 2023

Cited by 10 | Viewed by 5380

Abstract

In response to challenging circumstances, the human body can experience marked levels of anxiety and distress. To prevent stress-related complications, timely identification of stress symptoms is crucial, necessitating the need for continuous stress monitoring. Wearable devices offer a means of real-time and ongoing [...] Read more.

In response to challenging circumstances, the human body can experience marked levels of anxiety and distress. To prevent stress-related complications, timely identification of stress symptoms is crucial, necessitating the need for continuous stress monitoring. Wearable devices offer a means of real-time and ongoing data collection, facilitating personalized stress monitoring. Based on our protocol for data pre-processing, this study proposes to analyze signals obtained from the Empatica E4 bracelet using machine-learning algorithms (Random Forest, SVM, and Logistic Regression) to determine the efficacy of the abovementioned techniques in differentiating between stressful and non-stressful situations. Photoplethysmographic and electrodermal activity signals were collected from 29 subjects to extract 27 features which were then fed into three different machine-learning algorithms for binary classification. Using MATLAB after applying the chi-square test and Pearson’s correlation coefficient on WEKA for features’ importance ranking, the results demonstrated that the Random Forest model has the highest stability (accuracy of 76.5%) using all the features. Moreover, the Random Forest applying the chi-test for feature selection reached consistent results in terms of stress evaluation based on precision, recall, and F1-measure (71%, 60%, 65%, respectively). Full article

(This article belongs to the Special Issue Wearable Sensors and Artificial Intelligence for Measuring Human Vital Signs)

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26 pages, 4081 KiB

Open AccessArticle

AI-Enabled Smart Wristband Providing Real-Time Vital Signs and Stress Monitoring

by Nikos Mitro, Katerina Argyri, Lampros Pavlopoulos, Dimitrios Kosyvas, Lazaros Karagiannidis, Margarita Kostovasili, Fay Misichroni, Eleftherios Ouzounoglou and Angelos Amditis

Sensors 2023, 23(5), 2821; https://doi.org/10.3390/s23052821 - 4 Mar 2023

Cited by 6 | Viewed by 5959

Abstract

This work introduces the design, architecture, implementation, and testing of a low-cost and machine-learning-enabled device to be worn on the wrist. The suggested wearable device has been developed for use during emergency incidents of large passenger ship evacuations, and enables the real-time monitoring [...] Read more.

This work introduces the design, architecture, implementation, and testing of a low-cost and machine-learning-enabled device to be worn on the wrist. The suggested wearable device has been developed for use during emergency incidents of large passenger ship evacuations, and enables the real-time monitoring of the passengers’ physiological state, and stress detection. Based on a properly preprocessed PPG signal, the device provides essential biometric data (pulse rate and oxygen saturation level) and an efficient unimodal machine learning pipeline. The stress detecting machine learning pipeline is based on ultra-short-term pulse rate variability, and has been successfully integrated into the microcontroller of the developed embedded device. As a result, the presented smart wristband is able to provide real-time stress detection. The stress detection system has been trained with the use of the publicly available WESAD dataset, and its performance has been tested through a two-stage process. Initially, evaluation of the lightweight machine learning pipeline on a previously unseen subset of the WESAD dataset was performed, reaching an accuracy score equal to 91%. Subsequently, external validation was conducted, through a dedicated laboratory study of 15 volunteers subjected to well-acknowledged cognitive stressors while wearing the smart wristband, which yielded an accuracy score equal to 76%. Full article

(This article belongs to the Special Issue Wearable Sensors and Artificial Intelligence for Measuring Human Vital Signs)

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22 pages, 1702 KiB

Open AccessArticle

Development of an Intelligent System for the Monitoring and Diagnosis of the Well-Being

by Lizeth-Guadalupe Machado-Jaimes, Martin Rogelio Bustamante-Bello, Amadeo-José Argüelles-Cruz and Mariel Alfaro-Ponce

Sensors 2022, 22(24), 9719; https://doi.org/10.3390/s22249719 - 12 Dec 2022

Cited by 3 | Viewed by 2482

Abstract

Today, society is more aware of their well-being and health, making wearable devices a new and affordable way to track them continuously. Smartwatches allow access to daily vital physiological measurements, which help people to be aware of their health status. Even though these [...] Read more.

Today, society is more aware of their well-being and health, making wearable devices a new and affordable way to track them continuously. Smartwatches allow access to daily vital physiological measurements, which help people to be aware of their health status. Even though these technologies allow the following of different health conditions, their application in health is still limited to the following physical parameters to allow physicians treatment and diagnosis. This paper presents LM Research, a smart monitoring system mainly composed of a web page, REST APIs, machine learning algorithms, psychological questionnaire, and smartwatches. The system introduces the continuous monitoring of the users’ physical and mental indicators to prevent a wellness crisis; the mental indicators and the system’s continuous feedback to the user could be, in the future, a tool for medical specialists treating well-being. For this purpose, it collects psychological parameters on smartwatches and mental health data using a psychological questionnaire to develop a supervised machine learning wellness model that predicts the wellness of smartwatch users. The full construction of the database and the technology employed for its development is presented. Moreover, six machine learning algorithms (Decision Tree, Random Forest, Naive Bayes, Neural Networks, Support Vector Machine, and K-nearest neighbor) were applied to the database to test which classifies better the information obtained by the proposed system. In order to integrate this algorithm into LM Research, Random Forest being the one with the higher accuracy of 88%. Full article

(This article belongs to the Special Issue Wearable Sensors and Artificial Intelligence for Measuring Human Vital Signs)

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13 pages, 279 KiB

Open AccessArticle

Recommendations for Identifying Valid Wear for Consumer-Level Wrist-Worn Activity Trackers and Acceptability of Extended Device Deployment in Children

by David Wing, Job G. Godino, Fiona C. Baker, Rongguang Yang, Guillaume Chevance, Wesley K. Thompson, Chase Reuter, Hauke Bartsch, Aimee Wilbur, Lisa K. Straub, Norma Castro, Michael Higgins, Ian M. Colrain, Massimiliano de Zambotti, Natasha E. Wade, Krista M. Lisdahl, Lindsay M. Squeglia, Joseph Ortigara, Bernard Fuemmeler, Kevin Patrick, Michael J. Mason, Susan F. Tapert and Kara S. Bagot Show full author list Hide full author list

Sensors 2022, 22(23), 9189; https://doi.org/10.3390/s22239189 - 26 Nov 2022

Cited by 3 | Viewed by 1793

Abstract

Background: Self-reported physical activity is often inaccurate. Wearable devices utilizing multiple sensors are now widespread. The aim of this study was to determine acceptability of Fitbit Charge HR for children and their families, and to determine best practices for processing its objective data. [...] Read more.

Background: Self-reported physical activity is often inaccurate. Wearable devices utilizing multiple sensors are now widespread. The aim of this study was to determine acceptability of Fitbit Charge HR for children and their families, and to determine best practices for processing its objective data. Methods: Data were collected via Fitbit Charge HR continuously over the course of 3 weeks. Questionnaires were given to each child and their parent/guardian to determine the perceived usability of the device. Patterns of data were evaluated and best practice inclusion criteria recommended. Results: Best practices were established to extract, filter, and process data to evaluate device wear, r and establish minimum wear time to evaluate behavioral patterns. This resulted in usable data available from 137 (89%) of the sample. Conclusions: Activity trackers are highly acceptable in the target population and can provide objective data over longer periods of wear. Best practice inclusion protocols that reflect physical activity in youth are provided. Full article

(This article belongs to the Special Issue Wearable Sensors and Artificial Intelligence for Measuring Human Vital Signs)

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