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Vitali D, Olugbade T, Eccleston C, Keogh E, Bianchi-Berthouze N, de C Williams AC. Sensing behavior change in chronic pain: a scoping review of sensor technology for use in daily life. Pain 2024; 165:1348-1360. [PMID: 38258888 DOI: 10.1097/j.pain.0000000000003134] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 09/26/2023] [Indexed: 01/24/2024]
Abstract
ABSTRACT Technology offers possibilities for quantification of behaviors and physiological changes of relevance to chronic pain, using wearable sensors and devices suitable for data collection in daily life contexts. We conducted a scoping review of wearable and passive sensor technologies that sample data of psychological interest in chronic pain, including in social situations. Sixty articles met our criteria from the 2783 citations retrieved from searching. Three-quarters of recruited people were with chronic pain, mostly musculoskeletal, and the remainder with acute or episodic pain; those with chronic pain had a mean age of 43 (few studies sampled adolescents or children) and 60% were women. Thirty-seven studies were performed in laboratory or clinical settings and the remainder in daily life settings. Most used only 1 type of technology, with 76 sensor types overall. The commonest was accelerometry (mainly used in daily life contexts), followed by motion capture (mainly in laboratory settings), with a smaller number collecting autonomic activity, vocal signals, or brain activity. Subjective self-report provided "ground truth" for pain, mood, and other variables, but often at a different timescale from the automatically collected data, and many studies reported weak relationships between technological data and relevant psychological constructs, for instance, between fear of movement and muscle activity. There was relatively little discussion of practical issues: frequency of sampling, missing data for human or technological reasons, and the users' experience, particularly when users did not receive data in any form. We conclude the review with some suggestions for content and process of future studies in this field.
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Affiliation(s)
- Diego Vitali
- Research Department of Clinical, Educational & Health Psychology, University College London, London, United Kingdom
| | - Temitayo Olugbade
- School of Engineering and Informatics, University of Sussex, Brighton, United Kingdom
- Interaction Centre, University College London, London, United Kingdom
| | - Christoper Eccleston
- Centre for Pain Research, The University of Bath, Bath, United Kingdom
- Department of Experimental, Clinical and Health Psychology, Ghent University, Ghent, Belgium
- Department of Psychology, The University of Helsinki, Helsinki, Finland
| | - Edmund Keogh
- Centre for Pain Research, The University of Bath, Bath, United Kingdom
| | | | - Amanda C de C Williams
- Research Department of Clinical, Educational & Health Psychology, University College London, London, United Kingdom
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Alam J, Khan MF, Khan MA, Singh R, Mundazeer M, Kumar P. A Systematic Approach Focused on Machine Learning Models for Exploring the Landscape of Physiological Measurement and Estimation Using Photoplethysmography (PPG). J Cardiovasc Transl Res 2024; 17:669-684. [PMID: 38010481 DOI: 10.1007/s12265-023-10462-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 11/08/2023] [Indexed: 11/29/2023]
Abstract
A non-invasive optical technique known as photoplethysmography (PPG) can be used to provide various physiological measurements and estimations. PPG can be used to assess cardiovascular disease (CVD). Hypertension is a primary risk factor for CVD and a major health problem worldwide. PPG is popular because of its important applications in the evaluation of cardiac activity, variations in venous blood volume, blood oxygen saturation, blood pressure and heart rate variability, etc. In this study, we provide a comprehensive analysis of the extraction of various physiological parameters using PPG waveforms. In addition, we focused on the role of machine learning (ML) models used for the estimation of blood pressure and hypertension classification based on PPG waveforms to make future research and innovation recommendations. This study will be helpful for researchers, scientists, and medical practitioners working on PPG waveforms for monitoring, screening, and diagnosis, as a comparative study or reference.
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Affiliation(s)
- Javed Alam
- Quantlase Lab LLC, Masdar City, Abu Dhabi, United Arab Emirates.
| | | | - Meraj Alam Khan
- Quantlase Lab LLC, Masdar City, Abu Dhabi, United Arab Emirates
- DigiBiomics Inc, Mississauga, Ontario, Canada
| | - Rinky Singh
- Quantlase Lab LLC, Masdar City, Abu Dhabi, United Arab Emirates
| | | | - Pramod Kumar
- Quantlase Lab LLC, Masdar City, Abu Dhabi, United Arab Emirates
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Bolpagni M, Pardini S, Dianti M, Gabrielli S. Personalized Stress Detection Using Biosignals from Wearables: A Scoping Review. SENSORS (BASEL, SWITZERLAND) 2024; 24:3221. [PMID: 38794074 PMCID: PMC11126007 DOI: 10.3390/s24103221] [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: 04/23/2024] [Revised: 05/14/2024] [Accepted: 05/16/2024] [Indexed: 05/26/2024]
Abstract
Stress is a natural yet potentially harmful aspect of human life, necessitating effective management, particularly during overwhelming experiences. This paper presents a scoping review of personalized stress detection models using wearable technology. Employing the PRISMA-ScR framework for rigorous methodological structuring, we systematically analyzed literature from key databases including Scopus, IEEE Xplore, and PubMed. Our focus was on biosignals, AI methodologies, datasets, wearable devices, and real-world implementation challenges. The review presents an overview of stress and its biological mechanisms, details the methodology for the literature search, and synthesizes the findings. It shows that biosignals, especially EDA and PPG, are frequently utilized for stress detection and demonstrate potential reliability in multimodal settings. Evidence for a trend towards deep learning models was found, although the limited comparison with traditional methods calls for further research. Concerns arise regarding the representativeness of datasets and practical challenges in deploying wearable technologies, which include issues related to data quality and privacy. Future research should aim to develop comprehensive datasets and explore AI techniques that are not only accurate but also computationally efficient and user-centric, thereby closing the gap between theoretical models and practical applications to improve the effectiveness of stress detection systems in real scenarios.
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Affiliation(s)
- Marco Bolpagni
- Human Inspired Technology Research Centre, University of Padua, 35121 Padua, Italy
- Digital Health Research, Centre for Digital Health and Wellbeing, Fondazione Bruno Kessler, 38123 Trento, Italy; (S.P.); (M.D.); (S.G.)
| | - Susanna Pardini
- Digital Health Research, Centre for Digital Health and Wellbeing, Fondazione Bruno Kessler, 38123 Trento, Italy; (S.P.); (M.D.); (S.G.)
| | - Marco Dianti
- Digital Health Research, Centre for Digital Health and Wellbeing, Fondazione Bruno Kessler, 38123 Trento, Italy; (S.P.); (M.D.); (S.G.)
| | - Silvia Gabrielli
- Digital Health Research, Centre for Digital Health and Wellbeing, Fondazione Bruno Kessler, 38123 Trento, Italy; (S.P.); (M.D.); (S.G.)
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Volpes G, Valenti S, Genova G, Barà C, Parisi A, Faes L, Busacca A, Pernice R. Wearable Ring-Shaped Biomedical Device for Physiological Monitoring through Finger-Based Acquisition of Electrocardiographic, Photoplethysmographic, and Galvanic Skin Response Signals: Design and Preliminary Measurements. BIOSENSORS 2024; 14:205. [PMID: 38667198 PMCID: PMC11048376 DOI: 10.3390/bios14040205] [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: 02/25/2024] [Revised: 04/12/2024] [Accepted: 04/18/2024] [Indexed: 04/28/2024]
Abstract
Wearable health devices (WHDs) are rapidly gaining ground in the biomedical field due to their ability to monitor the individual physiological state in everyday life scenarios, while providing a comfortable wear experience. This study introduces a novel wearable biomedical device capable of synchronously acquiring electrocardiographic (ECG), photoplethysmographic (PPG), galvanic skin response (GSR) and motion signals. The device has been specifically designed to be worn on a finger, enabling the acquisition of all biosignals directly on the fingertips, offering the significant advantage of being very comfortable and easy to be employed by the users. The simultaneous acquisition of different biosignals allows the extraction of important physiological indices, such as heart rate (HR) and its variability (HRV), pulse arrival time (PAT), GSR level, blood oxygenation level (SpO2), and respiratory rate, as well as motion detection, enabling the assessment of physiological states, together with the detection of potential physical and mental stress conditions. Preliminary measurements have been conducted on healthy subjects using a measurement protocol consisting of resting states (i.e., SUPINE and SIT) alternated with physiological stress conditions (i.e., STAND and WALK). Statistical analyses have been carried out among the distributions of the physiological indices extracted in time, frequency, and information domains, evaluated under different physiological conditions. The results of our analyses demonstrate the capability of the device to detect changes between rest and stress conditions, thereby encouraging its use for assessing individuals' physiological state. Furthermore, the possibility of performing synchronous acquisitions of PPG and ECG signals has allowed us to compare HRV and pulse rate variability (PRV) indices, so as to corroborate the reliability of PRV analysis under stationary physical conditions. Finally, the study confirms the already known limitations of wearable devices during physical activities, suggesting the use of algorithms for motion artifact correction.
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Affiliation(s)
| | | | | | | | | | | | | | - Riccardo Pernice
- Department of Engineering, University of Palermo, Viale delle Scienze, Building 9, 90128 Palermo, Italy; (G.V.); (S.V.); (G.G.); (C.B.); (A.P.); (L.F.); (A.B.)
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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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Almadhor A, Sampedro GA, Abisado M, Abbas S, Kim YJ, Khan MA, Baili J, Cha JH. Wrist-Based Electrodermal Activity Monitoring for Stress Detection Using Federated Learning. SENSORS (BASEL, SWITZERLAND) 2023; 23:3984. [PMID: 37112323 PMCID: PMC10146352 DOI: 10.3390/s23083984] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 04/11/2023] [Accepted: 04/12/2023] [Indexed: 06/19/2023]
Abstract
With the most recent developments in wearable technology, the possibility of continually monitoring stress using various physiological factors has attracted much attention. By reducing the detrimental effects of chronic stress, early diagnosis of stress can enhance healthcare. Machine Learning (ML) models are trained for healthcare systems to track health status using adequate user data. Insufficient data is accessible, however, due to privacy concerns, making it challenging to use Artificial Intelligence (AI) models in the medical industry. This research aims to preserve the privacy of patient data while classifying wearable-based electrodermal activities. We propose a Federated Learning (FL) based approach using a Deep Neural Network (DNN) model. For experimentation, we use the Wearable Stress and Affect Detection (WESAD) dataset, which includes five data states: transient, baseline, stress, amusement, and meditation. We transform this raw dataset into a suitable form for the proposed methodology using the Synthetic Minority Oversampling Technique (SMOTE) and min-max normalization pre-processing methods. In the FL-based technique, the DNN algorithm is trained on the dataset individually after receiving model updates from two clients. To decrease the over-fitting effect, every client analyses the results three times. Accuracies, Precision, Recall, F1-scores, and Area Under the Receiver Operating Curve (AUROC) values are evaluated for each client. The experimental result shows the effectiveness of the federated learning-based technique on a DNN, reaching 86.82% accuracy while also providing privacy to the patient's data. Using the FL-based DNN model over a WESAD dataset improves the detection accuracy compared to the previous studies while also providing the privacy of patient data.
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Affiliation(s)
- Ahmad Almadhor
- Department of Computer Engineering and Networks, College of Computer and Information Sciences, Jouf University, Sakaka 72388, Saudi Arabia;
| | - Gabriel Avelino Sampedro
- Faculty of Information and Communication Studies, University of the Philippines Open University, Los Baños 4031, Philippines;
- Center for Computational Imaging and Visual Innovations, De La Salle University, 2401 Taft Ave., Malate, Manila 1004, Philippines
| | - Mideth Abisado
- College of Computing and Information Technologies, National University, Manila 1008, Philippines;
| | - Sidra Abbas
- Department of Computer Science, COMSATS University, Islamabad 45550, Pakistan
| | - Ye-Jin Kim
- Department of Computer Science, Hanyang University, Seoul 04763, Republic of Korea; (Y.-J.K.); (J.-H.C.)
| | | | - Jamel Baili
- College of Computer Science, King Khalid University, Abha 61413, Saudi Arabia
- Higher Institute of Applied Science and Technology of Sousse (ISSATS), Cité Taffala (Ibn Khaldoun) 4003 Sousse, University of Sousse, Sousse 4000, Tunisia
| | - Jae-Hyuk Cha
- Department of Computer Science, Hanyang University, Seoul 04763, Republic of Korea; (Y.-J.K.); (J.-H.C.)
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Mitro N, Argyri K, Pavlopoulos L, Kosyvas D, Karagiannidis L, Kostovasili M, Misichroni F, Ouzounoglou E, Amditis A. AI-Enabled Smart Wristband Providing Real-Time Vital Signs and Stress Monitoring. SENSORS (BASEL, SWITZERLAND) 2023; 23:2821. [PMID: 36905025 PMCID: PMC10007366 DOI: 10.3390/s23052821] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 02/20/2023] [Accepted: 03/01/2023] [Indexed: 06/18/2023]
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 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%.
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Affiliation(s)
- Nikos Mitro
- School of Electrical & Computer Engineering, National Technical University of Athens (NTUA), 10682 Athens, Greece
| | - Katerina Argyri
- School of Electrical & Computer Engineering, National Technical University of Athens (NTUA), 10682 Athens, Greece
| | - Lampros Pavlopoulos
- Institute of Communication and Computer Systems (ICCS), 10682 Athens, Greece
| | - Dimitrios Kosyvas
- Institute of Communication and Computer Systems (ICCS), 10682 Athens, Greece
| | - Lazaros Karagiannidis
- School of Electrical & Computer Engineering, National Technical University of Athens (NTUA), 10682 Athens, Greece
| | - Margarita Kostovasili
- School of Electrical & Computer Engineering, National Technical University of Athens (NTUA), 10682 Athens, Greece
| | - Fay Misichroni
- Institute of Communication and Computer Systems (ICCS), 10682 Athens, Greece
| | - Eleftherios Ouzounoglou
- School of Electrical & Computer Engineering, National Technical University of Athens (NTUA), 10682 Athens, Greece
| | - Angelos Amditis
- School of Electrical & Computer Engineering, National Technical University of Athens (NTUA), 10682 Athens, Greece
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Abromavičius V, Serackis A, Katkevičius A, Kazlauskas M, Sledevič T. Prediction of exam scores using a multi-sensor approach for wearable exam stress dataset with uniform preprocessing. Technol Health Care 2023; 31:2499-2511. [PMID: 37955074 DOI: 10.3233/thc-235015] [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] [Indexed: 11/14/2023]
Abstract
BACKGROUND Physiological signals, such as skin conductance, heart rate, and temperature, provide valuable insight into the physiological responses of students to stress during examination sessions. OBJECTIVE The primary objective of this research is to explore the effectiveness of physiological signals in predicting grades and to assess the impact of different models and feature selection techniques on predictive performance. METHODS We extracted a comprehensive feature vector comprising 301 distinct features from seven signals and implemented a uniform preprocessing technique for all signals. In addition, we analyzed different algorithmic selection features to design relevant features for robust and accurate predictions. RESULTS The study reveals promising results, with the highest scores achieved using 100 and 150 features. The corresponding values for accuracy, AUROC, and F1-Score are 0.9, 0.89, and 0.87, respectively, indicating the potential of physiological signals for accurate grade prediction. CONCLUSION The findings of this study suggest practical applications in the field of education, where the use of physiological signals can help students cope with exam stress and improve their academic performance. The importance of feature selection and the use of appropriate models highlight the importance of engineering relevant features for precise and reliable predictions.
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