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Wang J, Zhang H, Wan W, Yang H, Zhao J. Advances in nanotechnological approaches for the detection of early markers associated with severe cardiac ailments. Nanomedicine (Lond) 2024; 19:1487-1506. [PMID: 39121377 PMCID: PMC11318751 DOI: 10.1080/17435889.2024.2364581] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Accepted: 05/31/2024] [Indexed: 08/11/2024] Open
Abstract
Mortality from cardiovascular disease (CVD) accounts for over 30% of all deaths globally, necessitating reliable diagnostic tools. Prompt identification and precise diagnosis are critical for effective personalized treatment. Nanotechnology offers promising applications in diagnostics, biosensing and drug delivery for prevalent cardiovascular diseases. Its integration into cardiovascular care enhances diagnostic accuracy, enabling early intervention and tailored treatment plans. By leveraging nanoscale innovations, healthcare professionals can address the complexities of CVD progression and customize interventions based on individual patient needs. Ongoing advancements in nanotechnology continue to shape the landscape of cardiovascular medicine, offering potential for improved patient outcomes and reduced mortality rates from these pervasive diseases.
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Affiliation(s)
- Jie Wang
- Department of Cardiac Care Unit, Yantaishan Hospital, Yantai, Shandong, 264003, China
| | - Haifeng Zhang
- Department of Cardiology, Yantai Yeda Hospital, Yantai, Shangdong, 264006, China
| | - Weiping Wan
- Department of Ultrasound, Yantaishan Hospital, Yantai, Shandong, 264003, China
| | - Haijiao Yang
- Department of Cardiac Care Unit, Yantaishan Hospital, Yantai, Shandong, 264003, China
| | - Jing Zhao
- Department of Critical Care Medicine, Yantaishan Hospital, Yantai, Shandong, 264003, China
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Joshi J, Wang K, Cho Y. PhysioKit: An Open-Source, Low-Cost Physiological Computing Toolkit for Single- and Multi-User Studies. SENSORS (BASEL, SWITZERLAND) 2023; 23:8244. [PMID: 37837074 PMCID: PMC10575364 DOI: 10.3390/s23198244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 09/12/2023] [Accepted: 09/29/2023] [Indexed: 10/15/2023]
Abstract
The proliferation of physiological sensors opens new opportunities to explore interactions, conduct experiments and evaluate the user experience with continuous monitoring of bodily functions. Commercial devices, however, can be costly or limit access to raw waveform data, while low-cost sensors are efforts-intensive to setup. To address these challenges, we introduce PhysioKit, an open-source, low-cost physiological computing toolkit. PhysioKit provides a one-stop pipeline consisting of (i) a sensing and data acquisition layer that can be configured in a modular manner per research needs, and (ii) a software application layer that enables data acquisition, real-time visualization and machine learning (ML)-enabled signal quality assessment. This also supports basic visual biofeedback configurations and synchronized acquisition for co-located or remote multi-user settings. In a validation study with 16 participants, PhysioKit shows strong agreement with research-grade sensors on measuring heart rate and heart rate variability metrics data. Furthermore, we report usability survey results from 10 small-project teams (44 individual members in total) who used PhysioKit for 4-6 weeks, providing insights into its use cases and research benefits. Lastly, we discuss the extensibility and potential impact of the toolkit on the research community.
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Nakagome K, Makinodan M, Uratani M, Kato M, Ozaki N, Miyata S, Iwamoto K, Hashimoto N, Toyomaki A, Mishima K, Ogasawara M, Takeshima M, Minato K, Fukami T, Oba M, Takeda K, Oi H. Feasibility of a wrist-worn wearable device for estimating mental health status in patients with mental illness. Front Psychiatry 2023; 14:1189765. [PMID: 37547203 PMCID: PMC10399687 DOI: 10.3389/fpsyt.2023.1189765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Accepted: 07/07/2023] [Indexed: 08/08/2023] Open
Abstract
Object Real-world data from wearable devices has the potential to understand mental health status in everyday life. We aimed to investigate the feasibility of estimating mental health status using a wrist-worn wearable device (Fitbit Sense) that measures movement using a 3D accelerometer and optical pulse photoplethysmography (PPG). Methods Participants were 110 patients with mental illnesses from different diagnostic groups. The study was undertaken between 1 October 2020 and 31 March 2021. Participants wore a Fitbit Sense on their wrist and also completed the State-Trait Anxiety Inventory (STAI), Positive and Negative Affect Schedule (PANAS), and EuroQol 5 dimensions 5-level (EQ-5D-5L) during the study period. To determine heart rate (HR) variability (HRV), we calculated the sdnn (standard deviation of the normal-to-normal interval), coefficient of variation of R-R intervals, and mean HR separately for each sleep stage and the daytime. The association between mental health status and HR and HRV was analyzed. Results The following significant correlations were found in the wake after sleep onset stage within 3 days of mental health status assessment: sdnn, HR and STAI scores, HR and PANAS scores, HR and EQ-5D-5L scores. The association between mental health status and HR and HRV was stronger the closer the temporal distance between mental health status assessment and HR measurement. Conclusion A wrist-worn wearable device that measures PPG signals was feasible for use with patients with mental illness. Resting state HR and HRV could be used as an objective assessment of mental health status within a few days of measurement.
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Affiliation(s)
- Kazuyuki Nakagome
- Department of Psychiatry, National Center of Neurology and Psychiatry, Kodaira, Japan
| | - Manabu Makinodan
- Department of Psychiatry, Nara Medical University, Kashihara, Japan
| | | | - Masaki Kato
- Department of Neuropsychiatry, Kansai Medical University, Hirakata, Japan
| | - Norio Ozaki
- Pathophysiology of Mental Disorders, Nagoya University Graduate School of Medicine, Nagoya, Japan
- Department of Psychiatry, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Seiko Miyata
- Department of Psychiatry, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Kunihiro Iwamoto
- Department of Psychiatry, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Naoki Hashimoto
- Department of Psychiatry, Hokkaido University Graduate School of Medicine, Sapporo, Japan
| | - Atsuhito Toyomaki
- Department of Psychiatry, Hokkaido University Graduate School of Medicine, Sapporo, Japan
| | - Kazuo Mishima
- Department of Neuropsychiatry, Akita University Graduate School of Medicine, Akita, Japan
| | - Masaya Ogasawara
- Department of Neuropsychiatry, Akita University Graduate School of Medicine, Akita, Japan
| | - Masahiro Takeshima
- Department of Neuropsychiatry, Akita University Graduate School of Medicine, Akita, Japan
| | | | | | - Mari Oba
- Department of Clinical Data Science, National Center of Neurology and Psychiatry, Kodaira, Japan
| | - Kazuyoshi Takeda
- Department of Clinical Data Science, National Center of Neurology and Psychiatry, Kodaira, Japan
| | - Hideki Oi
- Department of Clinical Data Science, National Center of Neurology and Psychiatry, Kodaira, Japan
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Gerostathi M, Doukakis S. Proposal for Monitoring Students' Self-Efficacy Using Neurophysiological Measures and Self-Report Scales. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2023; 1425:635-643. [PMID: 37581837 DOI: 10.1007/978-3-031-31986-0_62] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/16/2023]
Abstract
The role of STEM-science, technology, engineering, mathematics-education is internationally recognized as critical to both the personal development of students and their future contribution to a country's economy as through this education they are equipped with the necessary twenty-first-century skills. As a result, there is a need to study the way in which such education affects students. In particular, the study of the self-efficacy factor is a contribution in this direction. Self-efficacy is a fundamental concept in the learning process as it contributes to shaping learning outcomes. Self-report scales are commonly used to measure self-efficacy; however, concerns in research circles have been raised regarding their limitations. On the other hand, there is a growing research interest in neurophysiological measures in the field of education, which seem to offer promising possibilities for understanding learning. Therefore, to better determine the impact of STEM education on students, a combination of self-report scales and neurophysiological measures is proposed to measure self-efficacy.
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Lopes L, Rodrigues A, Cabral D, Campos P. From Monitoring to Assisting: A Systematic Review towards Healthier Workplaces. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:16197. [PMID: 36498272 PMCID: PMC9740988 DOI: 10.3390/ijerph192316197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 11/21/2022] [Accepted: 11/25/2022] [Indexed: 06/17/2023]
Abstract
Long-term stress is associated with a decline in global health, affecting social, intellectual, and economic development alike. Although comprehensive action plans have been implemented to provide people access to mental health services and promote mental well-being, employees' mental health generally takes second place to productivity and profit in business settings. This review paper offers an overview of the current interactive approaches used for relieving work-related stress associated with mental health. Results from the 38 included studies show that affective computing is used mainly for monitoring purposes and is usually combined with tangible interfaces that collect workers' physiological changes. Although the ability to sense and predict employees' affective states can potentially improve mental health in the workplace, there is a substantial disparity between monitoring one's health and the delivery of practical interventions to mitigate stress found in the surveyed studies. Designing systems that capitalize on embodied interaction principles is paramount, especially in the post-pandemic context, as the concepts of physical and mental safety take on new meanings that must be consciously and carefully addressed, particularly in workplace settings. Finally, this paper highlights the main design implications for the effective implementation of interfaces to help mitigate stress in the workplace.
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Affiliation(s)
- Laís Lopes
- Interactive Technologies Institute, Laboratory of Robotics and Engineering Systems (ITI/LARSyS), 9020-105 Funchal, Portugal
- Faculty of Exact Sciences and Engineering, University of Madeira, 9020-105 Funchal, Portugal
| | - Ana Rodrigues
- Interactive Technologies Institute, Laboratory of Robotics and Engineering Systems (ITI/LARSyS), 9020-105 Funchal, Portugal
- Faculty of Exact Sciences and Engineering, University of Madeira, 9020-105 Funchal, Portugal
| | - Diogo Cabral
- Interactive Technologies Institute, Laboratory of Robotics and Engineering Systems (ITI/LARSyS), 9020-105 Funchal, Portugal
- Instituto Superior Técnico, University of Lisbon, 1049-001 Lisbon, Portugal
| | - Pedro Campos
- Interactive Technologies Institute, Laboratory of Robotics and Engineering Systems (ITI/LARSyS), 9020-105 Funchal, Portugal
- Faculty of Exact Sciences and Engineering, University of Madeira, 9020-105 Funchal, Portugal
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Namvari M, Lipoth J, Knight S, Jamali AA, Hedayati M, Spiteri RJ, Syed-Abdul S. Photoplethysmography Enabled Wearable Devices and Stress Detection: A Scoping Review. J Pers Med 2022; 12:1792. [PMID: 36579537 PMCID: PMC9695300 DOI: 10.3390/jpm12111792] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 10/16/2022] [Accepted: 10/27/2022] [Indexed: 11/06/2022] Open
Abstract
BACKGROUND Mental and physical health are both important for overall health. Mental health includes emotional, psychological, and social well-being; however, it is often difficult to monitor remotely. The objective of this scoping review is to investigate studies that focus on mental health and stress detection and monitoring using PPG-based wearable sensors. METHODS A literature review for this scoping review was conducted using the PRISMA (Preferred Reporting Items for the Systematic Reviews and Meta-analyses) framework. A total of 290 studies were found in five medical databases (PubMed, Medline, Embase, CINAHL, and Web of Science). Studies were deemed eligible if non-invasive PPG-based wearables were worn on the wrist or ear to measure vital signs of the heart (heart rate, pulse transit time, pulse waves, blood pressure, and blood volume pressure) and analyzed the data qualitatively. RESULTS Twenty-three studies met the inclusion criteria, with four real-life studies, eighteen clinical studies, and one joint clinical and real-life study. Out of the twenty-three studies, seventeen were published as journal-based articles, and six were conference papers with full texts. Because most of the articles were concerned with physiological and psychological stress, we decided to only include those that focused on stress. In twelve of the twenty articles, a PPG-based sensor alone was used to monitor stress, while in the remaining eight papers, a PPG sensor was used in combination with other sensors. CONCLUSION The growing demand for wearable devices for mental health monitoring is evident. However, there is still a significant amount of research required before wearable devices can be used easily and effectively for such monitoring. Although the results of this review indicate that mental health monitoring and stress detection using PPG is possible, there are still many limitations within the current literature, such as a lack of large and diverse studies and ground-truth methods, that need to be addressed before wearable devices can be globally useful to patients.
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Affiliation(s)
- Mina Namvari
- Department of Computer Science, University of Saskatchewan, Saskatoon, SK S7N 5C9, Canada
- SUNUM Nanotechnology Research Centre, Sabanci University, Istanbul 34956, Turkey
| | - Jessica Lipoth
- Department of Computer Science, University of Saskatchewan, Saskatoon, SK S7N 5C9, Canada
| | - Sheida Knight
- Department of Computer Science, University of Saskatchewan, Saskatoon, SK S7N 5C9, Canada
| | - Ali Akbar Jamali
- Department of Computer Science, University of Saskatchewan, Saskatoon, SK S7N 5C9, Canada
| | | | - Raymond J. Spiteri
- Department of Computer Science, University of Saskatchewan, Saskatoon, SK S7N 5C9, Canada
| | - Shabbir Syed-Abdul
- Graduate Institute of Biomedical Informatics, College of Medical Sciences and Technology, Taipei Medical University, Taipei 106, Taiwan
- International Center for Health Information Technology, College of Medical Science and Technology, Taipei Medical University, Taipei 106, Taiwan
- School of Gerontology and Long-Term Care, College of Nursing, Taipei Medical University, Taipei 110, Taiwan
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Deep-Learning-Based Stress Recognition with Spatial-Temporal Facial Information. SENSORS 2021; 21:s21227498. [PMID: 34833572 PMCID: PMC8625615 DOI: 10.3390/s21227498] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Revised: 10/29/2021] [Accepted: 11/09/2021] [Indexed: 01/31/2023]
Abstract
In recent times, as interest in stress control has increased, many studies on stress recognition have been conducted. Several studies have been based on physiological signals, but the disadvantage of this strategy is that it requires physiological-signal-acquisition devices. Another strategy employs facial-image-based stress-recognition methods, which do not require devices, but predominantly use handcrafted features. However, such features have low discriminating power. We propose a deep-learning-based stress-recognition method using facial images to address these challenges. Given that deep-learning methods require extensive data, we constructed a large-capacity image database for stress recognition. Furthermore, we used temporal attention, which assigns a high weight to frames that are highly related to stress, as well as spatial attention, which assigns a high weight to regions that are highly related to stress. By adding a network that inputs the facial landmark information closely related to stress, we supplemented the network that receives only facial images as the input. Experimental results on our newly constructed database indicated that the proposed method outperforms contemporary deep-learning-based recognition methods.
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Kästle JL, Anvari B, Krol J, Wurdemann HA. Correlation between Situational Awareness and EEG signals. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2020.12.026] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
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9
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Perpetuini D, Chiarelli AM, Cardone D, Filippini C, Rinella S, Massimino S, Bianco F, Bucciarelli V, Vinciguerra V, Fallica P, Perciavalle V, Gallina S, Conoci S, Merla A. Prediction of state anxiety by machine learning applied to photoplethysmography data. PeerJ 2021; 9:e10448. [PMID: 33520434 PMCID: PMC7812926 DOI: 10.7717/peerj.10448] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Accepted: 11/08/2020] [Indexed: 11/26/2022] Open
Abstract
Background As the human behavior is influenced by both cognition and emotion, affective computing plays a central role in human-machine interaction. Algorithms for emotions recognition are usually based on behavioral analysis or on physiological measurements (e.g., heart rate, blood pressure). Among these physiological signals, pulse wave propagation in the circulatory tree can be assessed through photoplethysmography (PPG), a non-invasive optical technique. Since pulse wave characteristics are influenced by the cardiovascular status, which is affected by the autonomic nervous activity and hence by the psychophysiological state, PPG might encode information about emotional conditions. The capability of a multivariate data-driven approach to estimate state anxiety (SA) of healthy participants from PPG features acquired on the brachial and radial artery was investigated. Methods The machine learning method was based on General Linear Model and supervised learning. PPG was measured employing a custom-made system and SA of the participants was assessed through the State-Trait Anxiety Inventory (STAI-Y) test. Results A leave-one-out cross-validation framework showed a good correlation between STAI-Y score and the SA predicted by the machine learning algorithm (r = 0.81; p = 1.87∙10−9). The preliminary results suggested that PPG can be a promising tool for emotions recognition, convenient for human-machine interaction applications.
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Affiliation(s)
- David Perpetuini
- Department of Neuroscience, Imaging and Clinical Sciences, Institute for Advanced Biomedical Technologies, University of Chieti-Pescara, Chieti, Italy
| | - Antonio Maria Chiarelli
- Department of Neuroscience, Imaging and Clinical Sciences, Institute for Advanced Biomedical Technologies, University of Chieti-Pescara, Chieti, Italy
| | - Daniela Cardone
- Department of Neuroscience, Imaging and Clinical Sciences, Institute for Advanced Biomedical Technologies, University of Chieti-Pescara, Chieti, Italy
| | - Chiara Filippini
- Department of Neuroscience, Imaging and Clinical Sciences, Institute for Advanced Biomedical Technologies, University of Chieti-Pescara, Chieti, Italy
| | - Sergio Rinella
- Physiology Section, Department of Biomedical and Biotechnological Sciences, University of Catania, Catania, Italy
| | - Simona Massimino
- Physiology Section, Department of Biomedical and Biotechnological Sciences, University of Catania, Catania, Italy
| | - Francesco Bianco
- Institute of Cardiology, University of Chieti-Pescara, Chieti, Italy
| | | | | | | | - Vincenzo Perciavalle
- Physiology Section, Department of Biomedical and Biotechnological Sciences, University of Catania, Catania, Italy.,Department of Sciences of Life, Kore University of Enna, Enna, Italy
| | - Sabina Gallina
- Department of Neuroscience, Imaging and Clinical Sciences, Institute for Advanced Biomedical Technologies, University of Chieti-Pescara, Chieti, Italy.,Institute of Cardiology, University of Chieti-Pescara, Chieti, Italy
| | - Sabrina Conoci
- STMicroelectronics, ADG R&D, Catania, Italy.,Department of Chemical, Biological, Pharmaceutical and Environmental Science, University of Messina, Messina, Italy
| | - Arcangelo Merla
- Department of Neuroscience, Imaging and Clinical Sciences, Institute for Advanced Biomedical Technologies, University of Chieti-Pescara, Chieti, Italy
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Basjaruddin NC, Syahbarudin F, Sutjiredjeki E. Measurement Device for Stress Level and Vital Sign Based on Sensor Fusion. Healthc Inform Res 2021; 27:11-18. [PMID: 33611872 PMCID: PMC7921569 DOI: 10.4258/hir.2021.27.1.11] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Accepted: 12/22/2020] [Indexed: 11/23/2022] Open
Abstract
Objectives Medical health monitoring generally refers to two important aspects of health, namely, physical and mental health. Physical health can be measured through the basic parameters of normal values of vital signs, while mental health can be known from the prevalence of mental and emotional disorders, such as stress. Currently, the medical devices that are generally used to measure these two aspects of health are still separate, so they are less effective than they might be otherwise. To overcome this problem, we designed and realized a device that can measure stress levels through vital signs of the body, namely, heart rate, oxygen saturation, body temperature, and galvanic skin response (GSR). Methods The sensor fusion method is used to process data from multiple sensors, so the output that shows the stress level and health status of vital signs can be more accurate and precise. Results Based on the results of testing, this device is able to show the health status of vital signs and stress levels within ±20 seconds, with the accuracies of body temperature measurements, oxygen saturation, and GSR of 97.227%, 99.4%, and 98.6%, respectively. Conclusions A device for the measurement of stress levels and vital signs based on sensor fusion has been successfully designed and realized in accordance with the expected functions and specifications.
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Affiliation(s)
| | - Febian Syahbarudin
- Department of Electrical Engineering, Politeknik Negeri Bandung, Bandung, Indonesia
| | - Ediana Sutjiredjeki
- Department of Electrical Engineering, Politeknik Negeri Bandung, Bandung, Indonesia
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Abstract
Recommendation systems have overcome the overload of irrelevant information by considering users’ preferences and emotional states in the fields of tourism, health, e-commerce, and entertainment. This article reviews the principal recommendation approach documents found in scientific databases (Elsevier’s Scopus and Clarivate Web of Science) through a scientometric analysis in ScientoPy. Research publications related to the recommenders of emotion-based tourism cover the last two decades. The review highlights the collection, processing, and feature extraction of data from sensors and wearables to detect emotions. The study proposes the thematic categories of recommendation systems, emotion recognition, wearable technology, and machine learning. This paper also presents the evolution, trend analysis, theoretical background, and algorithmic approaches used to implement recommenders. Finally, the discussion section provides guidelines for designing emotion-sensitive tourist recommenders.
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Jia NZ, Mejorado D, Poullados S, Bae H, Traverso G, Dias R, Hanumara N. Design of a Wearable System to Capture Physiological Data to Monitor Surgeons' Stress During Surgery. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:4539-4542. [PMID: 33019003 DOI: 10.1109/embc44109.2020.9176180] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
The mental and physiological stress experienced by surgeons during operations has been identified as an important human factor that impacts surgical performance and patient safety. It is crucial to objectively measure and quantify surgeons' stress via physiological signals in order to enhance the understanding of how stress contributes to surgical outcomes. Current clinical and consumer devices for monitoring bio signals are not well adapted for use in the operating room; therefore, we designed an unobtrusive system, that measures select signals that correlate with stress and stores the data for integration into a data processing pipeline. Herein, we present a proof-of-concept device that captures data from ECG, EMG, EDA, and IMU sensors and initial testing results.
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Thammasan N, Stuldreher IV, Schreuders E, Giletta M, Brouwer AM. A Usability Study of Physiological Measurement in School Using Wearable Sensors. SENSORS (BASEL, SWITZERLAND) 2020; 20:E5380. [PMID: 32962191 PMCID: PMC7570846 DOI: 10.3390/s20185380] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Revised: 09/15/2020] [Accepted: 09/17/2020] [Indexed: 12/13/2022]
Abstract
Measuring psychophysiological signals of adolescents using unobtrusive wearable sensors may contribute to understanding the development of emotional disorders. This study investigated the feasibility of measuring high quality physiological data and examined the validity of signal processing in a school setting. Among 86 adolescents, a total of more than 410 h of electrodermal activity (EDA) data were recorded using a wrist-worn sensor with gelled electrodes and over 370 h of heart rate data were recorded using a chest-strap sensor. The results support the feasibility of monitoring physiological signals at school. We describe specific challenges and provide recommendations for signal analysis, including dealing with invalid signals due to loose sensors, and quantization noise that can be caused by limitations in analog-to-digital conversion in wearable devices and be mistaken as physiological responses. Importantly, our results show that using toolboxes for automatic signal preprocessing, decomposition, and artifact detection with default parameters while neglecting differences between devices and measurement contexts yield misleading results. Time courses of students' physiological signals throughout the course of a class were found to be clearer after applying our proposed preprocessing steps.
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Affiliation(s)
- Nattapong Thammasan
- Human Media Interaction, Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, 7522 NB Enschede, The Netherlands;
| | - Ivo V. Stuldreher
- Human Media Interaction, Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, 7522 NB Enschede, The Netherlands;
- Perceptual and Cognitive Systems, The Netherlands Organisation for Applied Scientific Research (TNO), 3769 DE Soesterberg, The Netherlands;
| | - Elisabeth Schreuders
- Department Developmental Psychology, Institute of Psychology, Tilburg University, 5000 LE Tilburg, The Netherlands; (E.S.); (M.G.)
| | - Matteo Giletta
- Department Developmental Psychology, Institute of Psychology, Tilburg University, 5000 LE Tilburg, The Netherlands; (E.S.); (M.G.)
- Department of Developmental, Personality and Social Psychology, Faculty of Psychology and Educational Sciences, Ghent University, 9000 Ghent, Belgium
| | - Anne-Marie Brouwer
- Perceptual and Cognitive Systems, The Netherlands Organisation for Applied Scientific Research (TNO), 3769 DE Soesterberg, The Netherlands;
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StressFoot: Uncovering the Potential of the Foot for Acute Stress Sensing in Sitting Posture. SENSORS 2020; 20:s20102882. [PMID: 32438713 PMCID: PMC7285061 DOI: 10.3390/s20102882] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Revised: 05/10/2020] [Accepted: 05/16/2020] [Indexed: 11/17/2022]
Abstract
Stress is a naturally occurring psychological response and identifiable by several body signs. We propose a novel way to discriminate acute stress and relaxation, using movement and posture characteristics of the foot. Based on data collected from 23 participants performing tasks that induced stress and relaxation, we developed several machine learning models to construct the validity of our method. We tested our models in another study with 11 additional participants. The results demonstrated replicability with an overall accuracy of 87%. To also demonstrate external validity, we conducted a field study with 10 participants, performing their usual everyday office tasks over a working day. The results showed substantial robustness. We describe ten significant features in detail to enable an easy replication of our models.
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15
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Affanni A. Wireless Sensors System for Stress Detection by Means of ECG and EDA Acquisition. SENSORS 2020; 20:s20072026. [PMID: 32260321 PMCID: PMC7181292 DOI: 10.3390/s20072026] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/15/2020] [Revised: 03/12/2020] [Accepted: 03/31/2020] [Indexed: 01/28/2023]
Abstract
This paper describes the design of a two channels electrodermal activity (EDA) sensor and two channels electrocardiogram (ECG) sensor. The EDA sensors acquire data on the hands and transmit them to the ECG sensor with wireless WiFi communication for increased wearability. The sensors system acquires two EDA channels to improve the removal of motion artifacts that take place if EDA is measured on individuals who need to move their hands in their activities. The ECG channels are acquired on the chest and the ECG sensor is responsible for aligning the two ECG traces with the received packets from EDA sensors; the ECG sensor sends via WiFi the aligned packets to a laptop for real time plot and data storage. The metrological characterization showed high-level performances in terms of linearity and jitter; the delays introduced by the wireless transmission from EDA to ECG sensor have been proved to be negligible for the present application.
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Affiliation(s)
- Antonio Affanni
- Polytechnic Department of Engineering and Architecture (DPIA), University of Udine, Via delle Scienze 206, 33100 Udine, Italy
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Zaman S, Wesley A, Silva DRDC, Buddharaju P, Akbar F, Gao G, Mark G, Gutierrez-Osuna R, Pavlidis I. Stress and productivity patterns of interrupted, synergistic, and antagonistic office activities. Sci Data 2019; 6:264. [PMID: 31704939 PMCID: PMC6841929 DOI: 10.1038/s41597-019-0249-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2019] [Accepted: 10/09/2019] [Indexed: 11/09/2022] Open
Abstract
We describe a controlled experiment, aiming to study productivity and stress effects of email interruptions and activity interactions in the modern office. The measurement set includes multimodal data for n = 63 knowledge workers who volunteered for this experiment and were randomly assigned into four groups: (G1/G2) Batch email interruptions with/without exogenous stress. (G3/G4) Continual email interruptions with/without exogenous stress. To provide context, the experiment's email treatments were surrounded by typical office tasks. The captured variables include physiological indicators of stress, measures of report writing quality and keystroke dynamics, as well as psychometric scores and biographic information detailing participants' profiles. Investigations powered by this dataset are expected to lead to personalized recommendations for handling email interruptions and a deeper understanding of synergistic and antagonistic office activities. Given the centrality of email in the modern office, and the importance of office work to people's lives and the economy, the present data have a valuable role to play.
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Affiliation(s)
- Shaila Zaman
- Computational Physiology Laboratory, University of Houston, Houston, USA
| | - Amanveer Wesley
- Computational Physiology Laboratory, University of Houston, Houston, USA
| | | | - Pradeep Buddharaju
- Computational Physiology Laboratory, University of Houston, Houston, USA
| | - Fatema Akbar
- Department of Informatics, University of California, Irvine, USA
| | - Ge Gao
- College of Information Studies, University of Maryland, College Park, USA
| | - Gloria Mark
- Department of Informatics, University of California, Irvine, USA
| | - Ricardo Gutierrez-Osuna
- Perception, Sensing, and Instrumentation Laboratory, Texas A & M University, College Station, USA
| | - Ioannis Pavlidis
- Computational Physiology Laboratory, University of Houston, Houston, USA.
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