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de Vries S, van Oost F, Smaling H, de Knegt N, Cluitmans P, Smits R, Meinders E. Real-time stress detection based on artificial intelligence for people with an intellectual disability. Assist Technol 2024; 36:232-240. [PMID: 37751530 DOI: 10.1080/10400435.2023.2261045] [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] [Accepted: 09/11/2023] [Indexed: 09/28/2023] Open
Abstract
People with severe intellectual disabilities (ID) could have difficulty expressing their stress which may complicate timely responses from caregivers. The present study proposes an automatic stress detection system that can work in real-time. The system uses wearable sensors that record physiological signals in combination with machine learning to detect physiological changes related to stress. Four experiments were conducted to assess if the system could detect stress in people with and without ID. Three experiments were conducted with people without ID (n = 14, n = 18, and n = 48), and one observational study was done with people with ID (n = 12). To analyze if the system could detect stress, the performance of random, general, and personalized models was evaluated. The mixed ANOVA found a significant effect for model type, F(2, 134) = 116.50, p < .001. Additionally, the post-hoc t-tests found that the personalized model for the group with ID performed better than the random model, t(11) = 9.05, p < .001. The findings suggest that the personalized model can detect stress in people with and without ID. A larger-scale study is required to validate the system for people with ID.
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Affiliation(s)
- Stefan de Vries
- Research and Development, Mentech Eindhoven, Eindhoven, The Netherlands
| | - Fransje van Oost
- Research and Development, Mentech Eindhoven, Eindhoven, The Netherlands
| | - Hanneke Smaling
- Department of Public Health and Primary Care, Leiden University Medical Center, Leiden, The Netherlands
- University Network for the Care sector Zuid-Holland, Leiden University Medical Center, Leiden, The Netherlands
| | - Nanda de Knegt
- Prinsenstichting, Care center for people with intellectual disabilities, Purmerend, The Netherlands
| | - Pierre Cluitmans
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Reon Smits
- Research and Development, Mentech Eindhoven, Eindhoven, The Netherlands
| | - Erwin Meinders
- Research and Development, Mentech Eindhoven, Eindhoven, The Netherlands
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2
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Başaran OT, Can YS, André E, Ersoy C. Relieving the burden of intensive labeling for stress monitoring in the wild by using semi-supervised learning. Front Psychol 2024; 14:1293513. [PMID: 38250116 PMCID: PMC10797089 DOI: 10.3389/fpsyg.2023.1293513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 12/08/2023] [Indexed: 01/23/2024] Open
Abstract
Stress, a natural process affecting individuals' wellbeing, has a profound impact on overall quality of life. Researchers from diverse fields employ various technologies and methodologies to investigate it and alleviate the negative effects of this phenomenon. Wearable devices, such as smart bands, capture physiological data, including heart rate variability, motions, and electrodermal activity, enabling stress level monitoring through machine learning models. However, labeling data for model accuracy assessment poses a significant challenge in stress-related research due to incomplete or inaccurate labels provided by individuals in their daily lives. To address this labeling predicament, our study proposes implementing Semi-Supervised Learning (SSL) models. Through comparisons with deep learning-based supervised models and clustering-based unsupervised models, we evaluate the performance of our SSL models. Our experiments show that our SSL models achieve 77% accuracy with a classifier trained on an augmented dataset prepared using the label propagation (LP) algorithm. Additionally, our deep autoencoder network achieves 76% accuracy. These results highlight the superiority of SSL models over unsupervised learning techniques and their comparable performance to supervised learning models, even with limited labeled data. By relieving the burden of labeling in daily life stress recognition, our study advances stress-related research, recognizing stress as a natural process rather than a disease. This facilitates the development of more efficient and accurate stress monitoring methods in the wild.
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Affiliation(s)
- Osman Tugay Başaran
- Computer and Communication Systems (CCS) Labs, Telecommunication Networks Group (TKN), Department of Electrical Engineering and Computer Science, Technische Universität Berlin, Berlin, Germany
| | - Yekta Said Can
- Faculty of Applied Computer Science, Institute of Computer Science, Universität Augsburg, Augsburg, Germany
| | - Elisabeth André
- Faculty of Applied Computer Science, Institute of Computer Science, Universität Augsburg, Augsburg, Germany
| | - Cem Ersoy
- NETLAB Research Laboratory, Department of Computer Engineering, Bogazici University, Istanbul, Turkey
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Wieland F, Nigg C. A Trainable Open-Source Machine Learning Accelerometer Activity Recognition Toolbox: Deep Learning Approach. JMIR AI 2023; 2:e42337. [PMID: 38875548 PMCID: PMC11041400 DOI: 10.2196/42337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 02/28/2023] [Accepted: 04/22/2023] [Indexed: 06/16/2024]
Abstract
BACKGROUND The accuracy of movement determination software in current activity trackers is insufficient for scientific applications, which are also not open-source. OBJECTIVE To address this issue, we developed an accurate, trainable, and open-source smartphone-based activity-tracking toolbox that consists of an Android app (HumanActivityRecorder) and 2 different deep learning algorithms that can be adapted to new behaviors. METHODS We employed a semisupervised deep learning approach to identify the different classes of activity based on accelerometry and gyroscope data, using both our own data and open competition data. RESULTS Our approach is robust against variation in sampling rate and sensor dimensional input and achieved an accuracy of around 87% in classifying 6 different behaviors on both our own recorded data and the MotionSense data. However, if the dimension-adaptive neural architecture model is tested on our own data, the accuracy drops to 26%, which demonstrates the superiority of our algorithm, which performs at 63% on the MotionSense data used to train the dimension-adaptive neural architecture model. CONCLUSIONS HumanActivityRecorder is a versatile, retrainable, open-source, and accurate toolbox that is continually tested on new data. This enables researchers to adapt to the behavior being measured and achieve repeatability in scientific studies.
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Affiliation(s)
- Fluri Wieland
- Department of Health Science, Institute of Sports Science, University of Bern, Bern, Switzerland
| | - Claudio Nigg
- Department of Health Science, Institute of Sports Science, University of Bern, Bern, Switzerland
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Barki H, Chung WY. Mental Stress Detection Using a Wearable In-Ear Plethysmography. BIOSENSORS 2023; 13:397. [PMID: 36979609 PMCID: PMC10046749 DOI: 10.3390/bios13030397] [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: 01/31/2023] [Revised: 03/15/2023] [Accepted: 03/16/2023] [Indexed: 06/18/2023]
Abstract
This study presents an ear-mounted photoplethysmography (PPG) system that is designed to detect mental stress. Mental stress is a prevalent condition that can negatively impact an individual's health and well-being. Early detection and treatment of mental stress are crucial for preventing related illnesses and maintaining overall wellness. The study used data from 14 participants that were collected in a controlled environment. The participants were subjected to stress-inducing tasks such as the Stroop color-word test and mathematical calculations. The raw PPG signal was then preprocessed and transformed into scalograms using continuous wavelet transform (CWT). A convolutional neural network classifier was then used to classify the transformed signals as stressed or non-stressed. The results of the study show that the PPG system achieved high levels of accuracy (92.04%) and F1-score (90.8%). Furthermore, by adding white Gaussian noise to the raw PPG signals, the results were improved even more, with an accuracy of 96.02% and an F1-score of 95.24%. The proposed ear-mounted device shows great promise as a reliable tool for the early detection and treatment of mental stress, potentially revolutionizing the field of mental health and well-being.
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Affiliation(s)
- Hika Barki
- Department of AI Convergence, Pukyong National University, Busan 48513, Republic of Korea;
| | - Wan-Young Chung
- Department of AI Convergence, Pukyong National University, Busan 48513, Republic of Korea;
- Department of Electronic Engineering, Pukyong National University, Busan 48513, Republic of Korea
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5
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Matsumoto K, Matsui T, Suwa H, Yasumoto K. Stress Estimation Using Biometric and Activity Indicators to Improve QoL of the Elderly. SENSORS (BASEL, SWITZERLAND) 2023; 23:535. [PMID: 36617129 PMCID: PMC9824521 DOI: 10.3390/s23010535] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 12/19/2022] [Accepted: 12/23/2022] [Indexed: 06/17/2023]
Abstract
It is essential to estimate the stress state of the elderly to improve their QoL. Stress states change every day and hour, depending on the activities performed and the duration/intensity. However, most existing studies estimate stress states using only biometric information or specific activities (e.g., sleep duration, exercise duration/amount, etc.) as explanatory variables and do not consider all daily living activities. It is necessary to link various daily living activities and biometric information in order to estimate the stress state more accurately. Specifically, we construct a stress estimation model using machine learning with the answers to a stress status questionnaire obtained every morning and evening as the ground truth and the biometric data during each of the performed activities and the new proposed indicator including biological and activity perspectives as the features. We used the following methods: Baseline Method 1, in which the RRI variance and Lorenz plot area for 4 h after waking and 24 h before the questionnaire were used as features; Baseline Method 2, in which sleep time was added as a feature to Baseline Method 1; the proposed method, in which the Lorenz plot area per activity and total time per activity were added. We compared the results with the proposed method, which added the new indicators as the features. The results of the evaluation experiments using the one-month data collected from five elderly households showed that the proposed method had an average estimation accuracy of 59%, 7% better than Baseline Method 1 (52%) and 4% better than Baseline Method 2 (55%).
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Affiliation(s)
- Kanta Matsumoto
- Nara Institute of Science and Technology, 8916-5 Takayama-cho, Ikoma-shi 630-0192, Japan
| | - Tomokazu Matsui
- Nara Institute of Science and Technology, 8916-5 Takayama-cho, Ikoma-shi 630-0192, Japan
| | - Hirohiko Suwa
- Nara Institute of Science and Technology, 8916-5 Takayama-cho, Ikoma-shi 630-0192, Japan
- RIKEN Center for Advanced Intelligence Project AIP, Tokyo 103-0027, Japan
| | - Keiichi Yasumoto
- Nara Institute of Science and Technology, 8916-5 Takayama-cho, Ikoma-shi 630-0192, Japan
- RIKEN Center for Advanced Intelligence Project AIP, Tokyo 103-0027, Japan
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Gyrard A, Tabeau K, Fiorini L, Kung A, Senges E, De Mul M, Giuliani F, Lefebvre D, Hoshino H, Fabbricotti I, Sancarlo D, D’Onofrio G, Cavallo F, Guiot D, Arzoz-Fernandez E, Okabe Y, Tsukamoto M. Knowledge Engineering Framework for IoT Robotics Applied to Smart Healthcare and Emotional Well-Being. Int J Soc Robot 2023; 15:445-472. [PMID: 34804257 PMCID: PMC8594653 DOI: 10.1007/s12369-021-00821-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/22/2021] [Indexed: 12/01/2022]
Abstract
Social companion robots are getting more attention to assist elderly people to stay independent at home and to decrease their social isolation. When developing solutions, one remaining challenge is to design the right applications that are usable by elderly people. For this purpose, co-creation methodologies involving multiple stakeholders and a multidisciplinary researcher team (e.g., elderly people, medical professionals, and computer scientists such as roboticists or IoT engineers) are designed within the ACCRA (Agile Co-Creation of Robots for Ageing) project. This paper will address this research question: How can Internet of Robotic Things (IoRT) technology and co-creation methodologies help to design emotional-based robotic applications? This is supported by the ACCRA project that develops advanced social robots to support active and healthy ageing, co-created by various stakeholders such as ageing people and physicians. We demonstra this with three robots, Buddy, ASTRO, and RoboHon, used for daily life, mobility, and conversation. The three robots understand and convey emotions in real-time using the Internet of Things and Artificial Intelligence technologies (e.g., knowledge-based reasoning).
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Affiliation(s)
| | - Kasia Tabeau
- Erasmus School of Health Policy and Management, Erasmus University Rotterdam, Rotterdam, The Netherlands
| | - Laura Fiorini
- Department of Industrial Engineering, University of Florence, Florence, Italy
- The BioRobotics Institute, Scuola Superiore Sant’Anna, Pisa, Italy
| | | | - Eloise Senges
- Trialog, Paris, France
- Erasmus School of Health Policy and Management, Erasmus University Rotterdam, Rotterdam, The Netherlands
- Department of Industrial Engineering, University of Florence, Florence, Italy
- The BioRobotics Institute, Scuola Superiore Sant’Anna, Pisa, Italy
- BlueFrog Robotics, Paris, France
- Université Paris-Dauphine, Paris, France
- Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy
- Kyoto University, Kyoto, Japan
- Kobe University, Kobe, Japan
| | - Marleen De Mul
- Erasmus School of Health Policy and Management, Erasmus University Rotterdam, Rotterdam, The Netherlands
| | - Francesco Giuliani
- Trialog, Paris, France
- Erasmus School of Health Policy and Management, Erasmus University Rotterdam, Rotterdam, The Netherlands
- Department of Industrial Engineering, University of Florence, Florence, Italy
- The BioRobotics Institute, Scuola Superiore Sant’Anna, Pisa, Italy
- BlueFrog Robotics, Paris, France
- Université Paris-Dauphine, Paris, France
- Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy
- Kyoto University, Kyoto, Japan
- Kobe University, Kobe, Japan
| | | | - Hiroshi Hoshino
- Trialog, Paris, France
- Erasmus School of Health Policy and Management, Erasmus University Rotterdam, Rotterdam, The Netherlands
- Department of Industrial Engineering, University of Florence, Florence, Italy
- The BioRobotics Institute, Scuola Superiore Sant’Anna, Pisa, Italy
- BlueFrog Robotics, Paris, France
- Université Paris-Dauphine, Paris, France
- Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy
- Kyoto University, Kyoto, Japan
- Kobe University, Kobe, Japan
| | - Isabelle Fabbricotti
- Erasmus School of Health Policy and Management, Erasmus University Rotterdam, Rotterdam, The Netherlands
| | - Daniele Sancarlo
- Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy
| | - Grazia D’Onofrio
- Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy
| | - Filippo Cavallo
- Department of Industrial Engineering, University of Florence, Florence, Italy
- The BioRobotics Institute, Scuola Superiore Sant’Anna, Pisa, Italy
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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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8
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Computer assisted identification of stress, anxiety, depression (SAD) in students: A state-of-the-art review. Med Eng Phys 2022; 110:103900. [PMID: 36273998 DOI: 10.1016/j.medengphy.2022.103900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 08/09/2022] [Accepted: 10/02/2022] [Indexed: 11/05/2022]
Abstract
Stress, depression, and anxiety are a person's physiological states that emerge from various body features such as speech, body language, eye contact, facial expression, etc. Physiological emotion is a part of human life and is associated with psychological activities. Sad emotion is relatable to negative thoughts and recognized in three stages containing stress, anxiety, and depression. These stages of Physiological emotion show various common and distinguished symptoms. The present study explores stress, depression, and anxiety symptoms in student life. The study reviews the psychological features generated through various body parts to identify psychological activities. Environmental factors, including a daily routine, greatly trigger psychological activities. The psychological disorder may affect mental and physical health adversely. The correct recognition of such disorder is expensive and time-consuming as it requires accurate datasets of symptoms. In the present study, an attempt has been made to investigate the effectiveness of computerized automated techniques that include machine learning algorithms for identifying stress, anxiety, and depression mental disorder. The proposed paper reviews the machine learning-based algorithms applied over datasets containing questionnaires, audio, video, etc., to recognize sad details. During the review process, the proposed study found that artificial intelligence and machine learning techniques are well recommended and widely utilized in most of the existing literature for measuring psychological disorders. The various machine learning-based algorithms are applied over datasets containing questionnaires, audio, video, etc., to recognize sad details. There has been continuous monitoring for the body symptoms established in the various existing literature to identify psychological states. The present review reveals the study of excellence and competence of machine learning techniques in detecting psychological disorders' stress, depression, and anxiety parameters. This paper shows a systematic review of some existing computer vision-based models with their merits and demerits.
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9
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Gopalakrishnan A, Venkataraman R, Gururajan R, Zhou X, Genrich R. Mobile phone enabled mental health monitoring to enhance diagnosis for severity assessment of behaviours: a review. PeerJ Comput Sci 2022; 8:e1042. [PMID: 36092018 PMCID: PMC9455148 DOI: 10.7717/peerj-cs.1042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Accepted: 06/22/2022] [Indexed: 06/15/2023]
Abstract
Mental health issues are a serious consequence of the COVID-19 pandemic, influencing about 700 million people worldwide. These physiological issues need to be consistently observed on the people through non-invasive devices such as smartphones, and fitness bands in order to remove the burden of having the conciseness of continuously being monitored. On the other hand, technological improvements have enhanced the abilities and roles of conventional mobile phones from simple communication to observations and improved accessibility in terms of size and price may reflect growing familiarity with the smartphone among a vast number of consumers. As a result of continuous monitoring, together with various embedded sensors in mobile phones, raw data can be converted into useful information about the actions and behaviors of the consumers. Thus, the aim of this comprehensive work concentrates on the literature work done so far in the prediction of mental health issues via passive monitoring data from smartphones. This study also explores the way users interact with such self-monitoring technologies and what challenges they might face. We searched several electronic databases (PubMed, IEEE Xplore, ACM Digital Libraries, Soups, APA PsycInfo, and Mendeley Data) for published studies that are relevant to focus on the topic and English language proficiency from January 2015 to December 2020. We identified 943 articles, of which 115 articles were eligible for this scoping review based on the predetermined inclusion and exclusion criteria carried out manually. These studies provided various works regarding smartphones for health monitoring such as Physical activity (26.0 percent; 30/115), Mental health analysis (27.8 percent; 32/115), Student specific monitoring (15.6 percent; 18/115) are the three analyses carried out predominantly.
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Affiliation(s)
- Abinaya Gopalakrishnan
- Department of Networking and Communications, School of Computing, SRM Institute of Science and Technology, Kattankulathur, Chennai, India
- School of Business, University of Southern Queensland, Toowoomba, Australia
| | - Revathi Venkataraman
- Department of Networking and Communications, School of Computing, SRM Institute of Science and Technology, Kattankulathur, Chennai, India
| | - Raj Gururajan
- School of Business, University of Southern Queensland, Toowoomba, Australia
| | - Xujuan Zhou
- School of Business, University of Southern Queensland, Toowoomba, Australia
| | - Rohan Genrich
- School of Business, University of Southern Queensland, Toowoomba, Australia
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Sah RK, Cleveland MJ, Habibi A, Ghasemzadeh H. Stressalyzer: Convolutional Neural Network Framework for Personalized Stress Classification. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:4658-4663. [PMID: 36086580 DOI: 10.1109/embc48229.2022.9871842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Stress detection and monitoring is an active area of research with important implications for an individual's personal, professional, and social health. Current approaches for stress classification use traditional machine learning algorithms trained on features computed from multiple sensor modalities. These methods are data and computation-intensive, rely on hand-crafted features, and lack reproducibility. These limitations impede the practical use of stress detection and classification systems in the real world. To overcome these shortcomings, we propose Stressalyzer, a novel stress classification and personalization framework from single-modality sensor data without feature computation and selection. Stressalyzer uses only Electrodermal activity (EDA) sensor data while providing competitive results compared to the state-of-the-art techniques that use multiple sensor modalities and are computationally expensive due to the calculation of large number of features. Using the dataset collected in a laboratory setting from 15 subjects, our single-channel neural network-based model achieves a classification accuracy of 92.9% and an f1 score of 0.89 for binary stress classification. Our leave-one-subject-out analysis establishes the subjective nature of stress and shows that personalizing stress models using Stressalyzer significantly improves the model performance. Without model personalization, we found a performance decline in 40% of the subjects, suggesting the need for model personalization.
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11
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Sadeghi M, McDonald AD, Sasangohar F. Posttraumatic stress disorder hyperarousal event detection using smartwatch physiological and activity data. PLoS One 2022; 17:e0267749. [PMID: 35584096 PMCID: PMC9116643 DOI: 10.1371/journal.pone.0267749] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 04/16/2022] [Indexed: 12/26/2022] Open
Abstract
Posttraumatic Stress Disorder (PTSD) is a psychiatric condition affecting nearly a quarter of the United States war veterans who return from war zones. Treatment for PTSD typically consists of a combination of in-session therapy and medication. However; patients often experience their most severe PTSD symptoms outside of therapy sessions. Mobile health applications may address this gap, but their effectiveness is limited by the current gap in continuous monitoring and detection capabilities enabling timely intervention. The goal of this article is to develop a novel method to detect hyperarousal events using physiological and activity-based machine learning algorithms. Physiological data including heart rate and body acceleration as well as self-reported hyperarousal events were collected using a tool developed for commercial off-the-shelf wearable devices from 99 United States veterans diagnosed with PTSD over several days. The data were used to develop four machine learning algorithms: Random Forest, Support Vector Machine, Logistic Regression and XGBoost. The XGBoost model had the best performance in detecting onset of PTSD symptoms with over 83% accuracy and an AUC of 0.70. Post-hoc SHapley Additive exPlanations (SHAP) additive explanation analysis showed that algorithm predictions were correlated with average heart rate, minimum heart rate and average body acceleration. Findings show promise in detecting onset of PTSD symptoms which could be the basis for developing remote and continuous monitoring systems for PTSD. Such systems may address a vital gap in just-in-time interventions for PTSD self-management outside of scheduled clinical appointments.
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Affiliation(s)
- Mahnoosh Sadeghi
- Department of Industrial and / Systems Engineering, Texas A&M University, College Station, Texas, United States of America
| | - Anthony D. McDonald
- Department of Industrial and / Systems Engineering, Texas A&M University, College Station, Texas, United States of America
| | - Farzan Sasangohar
- Department of Industrial and / Systems Engineering, Texas A&M University, College Station, Texas, United States of America
- * E-mail:
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12
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Stan R. Personality Traits, Technology-Related Teaching Skills, and Coping Mechanisms as Antecedents of Teachers' Job-Related Affective Well-Being and Burnout in Compulsory and Higher Education Online Teaching Settings. Front Psychol 2022; 13:792642. [PMID: 35519656 PMCID: PMC9062184 DOI: 10.3389/fpsyg.2022.792642] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2021] [Accepted: 02/21/2022] [Indexed: 12/04/2022] Open
Abstract
Teachers’ job-related well-being has been affected by the sudden shift to emergency remote online teaching due to the COVID-19 pandemic which has totally reshaped the task performance. Therefore, this study attempts to enlighten the possible reasons for the deterioration in teachers’ job-related well-being and proposes an integrated application of three models of prediction for job-related affective well-being and burnout as teachers’ indicators for the well-being in online teaching settings. The first model includes personality traits (extroversion, neuroticism, and conscientiousness) measured with the revised neuroticism, extroversion, and openness personality inventory (NEO-PI-R). The second model integrates an indispensable skill for the online teaching which is technological pedagogical content knowledge (TPCK) as technology-related teaching skill conceptualized by the TPACK framework. The TPACK model is a technology integration that identifies three types of knowledge instructors need to combine for successful EdTech integration - technological, pedagogical, and content knowledge (i.e., TPACK). The third model, a multidimensional one, includes coping mechanisms (e.g., problem-focused coping, emotion-focused coping, social support coping, and avoidant coping) as mediators in the relationship between personality traits and TPCK on the one side, and job-related well-being indicators on the other side. Findings from regression analyses were used to test the first two models, and the findings from a mediation analysis were used to test the third model to show that teachers’ TPCK explains a significant amount of variance in the job-related affective well-being of the teachers. The analyses also demonstrate that avoidant coping particularly mediates the relation between burnout and job-related affective well-being during COVID-19 school closures. Results indicate the efficacy of the TPACK model in increasing the job-related well-being of the teachers. The analysis of the data led to recommend that teachers should improve their personal technology-related teaching skills and adopt coping strategies in consistent with their personality traits. Moreover, public schools, as organizations, could advance educational technology programs to enhance technology-related teaching skills with the aim of increasing the well-being of their employees in online teaching settings.
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Affiliation(s)
- Rosana Stan
- Department of Psychology, University of Oradea, Oradea, Romania
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Vega J, Bell BT, Taylor C, Xie J, Ng H, Honary M, McNaney R. Detecting Mental Health Behaviors Using Mobile Interactions: Exploratory Study Focusing on Binge Eating. JMIR Ment Health 2022; 9:e32146. [PMID: 35086064 PMCID: PMC9086876 DOI: 10.2196/32146] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 01/16/2022] [Accepted: 01/17/2022] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Binge eating is a subjective loss of control while eating, which leads to the consumption of large amounts of food. It can cause significant emotional distress and is often accompanied by purging behaviors (eg, meal skipping, overexercising, or vomiting). OBJECTIVE The aim of this study was to explore the potential of mobile sensing to detect indicators of binge-eating episodes, with a view toward informing the design of future context-aware mobile interventions. METHODS This study was conducted in 2 stages. The first involved the development of the DeMMI (Detecting Mental health behaviors using Mobile Interactions) app. As part of this, we conducted a consultation session to explore whether the types of sensor data we were proposing to capture were useful and appropriate, as well as to gather feedback on some specific app features relating to self-reporting. The second stage involved conducting a 6-week period of data collection with 10 participants experiencing binge eating (logging both their mood and episodes of binge eating) and 10 comparison participants (logging only mood). An optional interview was conducted after the study, which discussed their experience using the app, and 8 participants (n=3, 38% binge eating and n=5, 63% comparisons) consented. RESULTS The findings showed unique differences in the types of sensor data that were triangulated with the individuals' episodes (with nearby Bluetooth devices, screen and app use features, mobility features, and mood scores showing relevance). Participants had a largely positive opinion about the app, its unobtrusive role, and its ease of use. Interacting with the app increased participants' awareness of and reflection on their mood and phone usage patterns. Moreover, they expressed no privacy concerns as these were alleviated by the study information sheet. CONCLUSIONS This study contributes a series of recommendations for future studies wishing to scale our approach and for the design of bespoke mobile interventions to support this population.
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Affiliation(s)
- Julio Vega
- Department of Medicine, University of Pittsburgh, Pittsburgh, PA, United States
| | | | | | - Jue Xie
- Department of Human Centred Computing, Monash University, Clayton, Australia
| | - Heidi Ng
- Department of Human Centred Computing, Monash University, Clayton, Australia
| | | | - Roisin McNaney
- Department of Human Centred Computing, Monash University, Clayton, Australia
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14
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Jaber D, Hajj H, Maalouf F, El-Hajj W. Medically-oriented design for explainable AI for stress prediction from physiological measurements. BMC Med Inform Decis Mak 2022; 22:38. [PMID: 35148762 PMCID: PMC8840288 DOI: 10.1186/s12911-022-01772-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Accepted: 12/08/2021] [Indexed: 11/29/2022] Open
Abstract
Background In the last decade, a lot of attention has been given to develop artificial intelligence (AI) solutions for mental health using machine learning. To build trust in AI applications, it is crucial for AI systems to provide for practitioners and patients the reasons behind the AI decisions. This is referred to as Explainable AI. While there has been significant progress in developing stress prediction models, little work has been done to develop explainable AI for mental health. Methods In this work, we address this gap by designing an explanatory AI report for stress prediction from wearable sensors. Because medical practitioners and patients are likely to be familiar with blood test reports, we modeled the look and feel of the explanatory AI on those of a standard blood test report. The report includes stress prediction and the physiological signals related to stressful episodes. In addition to the new design for explaining AI in mental health, the work includes the following contributions: Methods to automatically generate different components of the report, an approach for evaluating and validating the accuracies of the explanations, and a collection of ground truth of relationships between physiological measurements and stress prediction. Results Test results showed that the explanations were consistent with ground truth. The reference intervals for stress versus non-stress were quite distinctive with little variation. In addition to the quantitative evaluations, a qualitative survey, conducted by three expert psychiatrists confirmed the usefulness of the explanation report in understanding the different aspects of the AI system. Conclusion In this work, we have provided a new design for explainable AI used in stress prediction based on physiological measurements. Based on the report, users and medical practitioners can determine what biological features have the most impact on the prediction of stress in addition to any health-related abnormalities. The effectiveness of the explainable AI report was evaluated using a quantitative and a qualitative assessment. The stress prediction accuracy was shown to be comparable to state-of-the-art. The contributions of each physiological signal to the stress prediction was shown to correlate with ground truth. In addition to these quantitative evaluations, a qualitative survey with psychiatrists confirmed the confidence and effectiveness of the explanation report in the stress made by the AI system. Future work includes the addition of more explanatory features related to other emotional states of the patient, such as sadness, relaxation, anxiousness, or happiness.
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Affiliation(s)
- Dalia Jaber
- Electrical and Computer Engineering Department, American University of Beirut, Beirut, Lebanon.
| | - Hazem Hajj
- Pathfinding, Automation Technology and Analytics, Intel Corporation, Hillsboro, Oregon, USA
| | - Fadi Maalouf
- Department of Psychiatry, American University of Beirut, Beirut, Lebanon
| | - Wassim El-Hajj
- Computer Science Department, American University of Beirut, Beirut, Lebanon
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15
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Michalke S, Lohrenz L, Lattemann C, Robra-Bissantz S. Exploring engagement, well-being, and welfare on engagement platforms: Insight into the personal service sector from the DACH region. ELECTRONIC MARKETS 2022; 32:2429-2444. [PMID: 36186664 PMCID: PMC9510306 DOI: 10.1007/s12525-022-00589-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/07/2022] [Accepted: 08/23/2022] [Indexed: 05/12/2023]
Abstract
Engagement platforms (EPs) are an essential technology to enable co-creation and service innovation. Therefore, the design and governance of these platforms are receiving increasing attention in research. In this study, we aim to identify which activities and mechanisms foster engagement and which governance mechanisms are implemented to avoid harm on EPs. To this end, we conducted expert interviews with founders, CEOs, and managers of 14 personal and household-related service platform companies from the DACH region (Germany(D), Austria(A), Switzerland(CH)), to gain insights into their activities and mechanisms for creating and maintaining successful EPs. We found eight mechanisms, e.g., moderation of content, limitations of entry and certification, employed by personal EPs (PEPs) as self-regulatory mechanisms to avoid misconduct and negative experiences of actors. The identified governance mechanisms may guide the design and governing of PEPs by providing tangible examples to foster actor engagement while considering externalities on a societal and individual level.
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Affiliation(s)
- Simon Michalke
- Jacobs University Bremen gGmbh, Management and Economics, Campus Ring 1, Bremen, 28759 Germany
| | - Lisa Lohrenz
- TU Braunschweig, WI2, Mühlenpfordtstraße 23, Braunschweig, 38106 Germany
| | - Christoph Lattemann
- Jacobs University Bremen gGmbh, Management and Economics, Campus Ring 1, Bremen, 28759 Germany
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16
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Pfeifer LS, Heyers K, Ocklenburg S, Wolf OT. Stress research during the COVID-19 pandemic and beyond. Neurosci Biobehav Rev 2021; 131:581-596. [PMID: 34599918 PMCID: PMC8480136 DOI: 10.1016/j.neubiorev.2021.09.045] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Revised: 09/22/2021] [Accepted: 09/24/2021] [Indexed: 12/27/2022]
Abstract
The COVID-19 pandemic confronts stress researchers in psychology and neuroscience with unique challenges. Widely used experimental paradigms such as the Trier Social Stress Test feature physical social encounters to induce stress by means of social-evaluative threat. As lockdowns and contact restrictions currently prevent in-person meetings, established stress induction paradigms are often difficult to use. Despite these challenges, stress research is of pivotal importance as the pandemic will likely increase the prevalence of stress-related mental disorders. Therefore, we review recent research trends like virtual reality, pre-recordings and online adaptations regarding their usefulness for established stress induction paradigms. Such approaches are not only crucial for stress research during COVID-19 but will likely stimulate the field far beyond the pandemic. They may facilitate research in new contexts and in homebound or movement-restricted participant groups. Moreover, they allow for new experimental variations that may advance procedures as well as the conceptualization of stress itself. While posing challenges for stress researchers undeniably, the COVID-19 pandemic may evolve into a driving force for progress eventually.
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Affiliation(s)
- Lena Sophie Pfeifer
- Cognitive Psychology, Institute of Cognitive Neuroscience, Faculty of Psychology, Ruhr University Bochum, Bochum, Germany,Corresponding author at: Lena Sophie Pfeifer (Cognitive Psychology, Ruhr University Bochum, Universitätsstraße 150, Room: IB 6/77, 44780, Bochum, Germany)
| | - Katrin Heyers
- Biopsychology, Institute of Cognitive Neuroscience, Faculty of Psychology, Ruhr University Bochum, Bochum, Germany,General Psychology II and Biological Psychology, Institute of Psychology, School of Human Sciences, Osnabrück University, Osnabrück, Germany
| | - Sebastian Ocklenburg
- Biopsychology, Institute of Cognitive Neuroscience, Faculty of Psychology, Ruhr University Bochum, Bochum, Germany
| | - Oliver T. Wolf
- Cognitive Psychology, Institute of Cognitive Neuroscience, Faculty of Psychology, Ruhr University Bochum, Bochum, Germany
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17
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Nguyen B, Kolappan S, Bhat V, Krishnan S. Clustering and Feature Analysis of Smartphone Data for Depression Monitoring. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:113-116. [PMID: 34891251 DOI: 10.1109/embc46164.2021.9629737] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Modern advancements have allowed society to be at the most innovative stages of technology which involves the possibility of multimodal data collection. Dartmouth dataset is a rich dataset collected over 10 weeks from 60 participants. The dataset includes different types of data but this paper focuses on 10 different smartphone sensor data and a Patient Health Questionnaire (PHQ) 9 survey that monitors the severity of depression. This paper extracts key features from smartphone data to identify depression. A multi-view bi-clustering (MVBC) algorithm is applied to categorize homogeneous behaviour subgroups. MVBC takes multiple views of sensing data as input. The algorithm inputs three views: average, trend, and location views. MVBC categorizes the subjects to low, medium and high PHQ-9 scores. Real-world data collection may have fewer sensors, allowing for less features to be extracted. This creates a focus on prioritization of features. In this body of work, minimum redundancy maximum relevance (mRMR) is applied to the sensing features to prioritize the features that better distinguish the different groups. The resulting MVBC are compared to literature to support the categorized clusters. Decision Tree (DT) 10-fold cross validation shows that our method can classify individuals into the correct subgroups using a reduced number of features to achieve an overall accuracy of 94.7±1.62%. Achieving high accuracies with reduced features allows for focus on low power analysis and edge computing applications for long-term mental health monitoring using a smartphone.
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18
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Sheikh M, Qassem M, Kyriacou PA. Wearable, Environmental, and Smartphone-Based Passive Sensing for Mental Health Monitoring. Front Digit Health 2021; 3:662811. [PMID: 34713137 PMCID: PMC8521964 DOI: 10.3389/fdgth.2021.662811] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Accepted: 03/02/2021] [Indexed: 12/21/2022] Open
Abstract
Collecting and analyzing data from sensors embedded in the context of daily life has been widely employed for the monitoring of mental health. Variations in parameters such as movement, sleep duration, heart rate, electrocardiogram, skin temperature, etc., are often associated with psychiatric disorders. Namely, accelerometer data, microphone, and call logs can be utilized to identify voice features and social activities indicative of depressive symptoms, and physiological factors such as heart rate and skin conductance can be used to detect stress and anxiety disorders. Therefore, a wide range of devices comprising a variety of sensors have been developed to capture these physiological and behavioral data and translate them into phenotypes and states related to mental health. Such systems aim to identify behaviors that are the consequence of an underlying physiological alteration, and hence, the raw sensor data are captured and converted into features that are used to define behavioral markers, often through machine learning. However, due to the complexity of passive data, these relationships are not simple and need to be well-established. Furthermore, parameters such as intrapersonal and interpersonal differences need to be considered when interpreting the data. Altogether, combining practical mobile and wearable systems with the right data analysis algorithms can provide a useful tool for the monitoring and management of mental disorders. The current review aims to comprehensively present and critically discuss all available smartphone-based, wearable, and environmental sensors for detecting such parameters in relation to the treatment and/or management of the most common mental health conditions.
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Affiliation(s)
- Mahsa Sheikh
- Research Centre for Biomedical Engineering, School of Mathematics, Computer Science & Engineering, City, University of London, London, United Kingdom
| | - M Qassem
- Research Centre for Biomedical Engineering, School of Mathematics, Computer Science & Engineering, City, University of London, London, United Kingdom
| | - Panicos A Kyriacou
- Research Centre for Biomedical Engineering, School of Mathematics, Computer Science & Engineering, City, University of London, London, United Kingdom
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19
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Optimal artificial neural network-based data mining technique for stress prediction in working employees. Soft comput 2021. [DOI: 10.1007/s00500-021-06058-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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20
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Abo-Tabik M, Benn Y, Costen N. Are Machine Learning Methods the Future for Smoking Cessation Apps? SENSORS 2021; 21:s21134254. [PMID: 34206167 PMCID: PMC8271573 DOI: 10.3390/s21134254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 06/07/2021] [Accepted: 06/16/2021] [Indexed: 11/16/2022]
Abstract
Smoking cessation apps provide efficient, low-cost and accessible support to smokers who are trying to quit smoking. This article focuses on how up-to-date machine learning algorithms, combined with the improvement of mobile phone technology, can enhance our understanding of smoking behaviour and support the development of advanced smoking cessation apps. In particular, we focus on the pros and cons of existing approaches that have been used in the design of smoking cessation apps to date, highlighting the need to improve the performance of these apps by minimizing reliance on self-reporting of environmental conditions (e.g., location), craving status and/or smoking events as a method of data collection. Lastly, we propose that making use of more advanced machine learning methods while enabling the processing of information about the user’s circumstances in real time is likely to result in dramatic improvement in our understanding of smoking behaviour, while also increasing the effectiveness and ease-of-use of smoking cessation apps, by enabling the provision of timely, targeted and personalised intervention.
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Affiliation(s)
- Maryam Abo-Tabik
- Department of Computing and Mathematics, Manchester Metropolitan University, Manchester M1 5GD, UK;
| | - Yael Benn
- Department of Psychology, Manchester Metropolitan University, Manchester M15 6GX, UK
- Correspondence: (Y.B.); (N.C.)
| | - Nicholas Costen
- Department of Computing and Mathematics, Manchester Metropolitan University, Manchester M1 5GD, UK;
- Correspondence: (Y.B.); (N.C.)
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21
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Maxhuni A, Hernandez-Leal P, Morales EF, Sucar LE, Osmani V, Mayora O. Unobtrusive Stress Assessment Using Smartphones. IEEE TRANSACTIONS ON MOBILE COMPUTING 2021; 20:2313-2325. [DOI: 10.1109/tmc.2020.2974834] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/25/2023]
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22
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Lekkas D, Jacobson NC. Using artificial intelligence and longitudinal location data to differentiate persons who develop posttraumatic stress disorder following childhood trauma. Sci Rep 2021; 11:10303. [PMID: 33986445 PMCID: PMC8119967 DOI: 10.1038/s41598-021-89768-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Accepted: 04/30/2021] [Indexed: 11/09/2022] Open
Abstract
Post-traumatic stress disorder (PTSD) is characterized by complex, heterogeneous symptomology, thus detection outside traditional clinical contexts is difficult. Fortunately, advances in mobile technology, passive sensing, and analytics offer promising avenues for research and development. The present study examined the ability to utilize Global Positioning System (GPS) data, derived passively from a smartphone across seven days, to detect PTSD diagnostic status among a cohort (N = 185) of high-risk, previously traumatized women. Using daily time spent away and maximum distance traveled from home as a basis for model feature engineering, the results suggested that diagnostic group status can be predicted out-of-fold with high performance (AUC = 0.816, balanced sensitivity = 0.743, balanced specificity = 0.8, balanced accuracy = 0.771). Results further implicate the potential utility of GPS information as a digital biomarker of the PTSD behavioral repertoire. Future PTSD research will benefit from application of GPS data within larger, more diverse populations.
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Affiliation(s)
- Damien Lekkas
- Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, 46 Centerra Parkway, Suite 300, Lebanon, NH, 03766, USA. .,Quantitative Biomedical Sciences Program, Dartmouth College, Lebanon, NH, 03766, USA.
| | - Nicholas C Jacobson
- Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, 46 Centerra Parkway, Suite 300, Lebanon, NH, 03766, USA.,Department of Biomedical Data Science, Geisel School of Medicine, Dartmouth College, Lebanon, NH, 03766, USA.,Department of Psychiatry, Geisel School of Medicine, Dartmouth College, Lebanon, NH, 03766, USA.,Quantitative Biomedical Sciences Program, Dartmouth College, Lebanon, NH, 03766, USA
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23
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Wang C, Qi H. Visualising the knowledge structure and evolution of wearable device research. J Med Eng Technol 2021; 45:207-222. [PMID: 33769166 DOI: 10.1080/03091902.2021.1891314] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
In recent years, the literature associated with wearable devices has grown rapidly, but few studies have used bibliometrics and a visualisation approach to conduct deep mining and reveal a panorama of the wearable devices field. To explore the foundational knowledge and research hotspots of the wearable devices field, this study conducted a series of bibliometric analyses on the related literature, including papers' production trends in the field and the distribution of countries, a keyword co-occurrence analysis, theme evolution analysis and research hotspots and trends for the future. By conducting a literature content analysis and structure analysis, we found the following: (a) The subject evolution path includes sensor research, sensitivity research and multi-functional device research. (b) Wearable device research focuses on information collection, sensor materials, manufacturing technology and application, artificial intelligence technology application, energy supply and medical applications. The future development trend will be further studied in combination with big data analysis, telemedicine and personalised precision medical application.
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Affiliation(s)
- Chen Wang
- Department of Health informatics and Management, School of Health Humanities, Peking University, Beijing, China
| | - Huiying Qi
- Department of Health informatics and Management, School of Health Humanities, Peking University, Beijing, China
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24
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Ueafuea K, Boonnag C, Sudhawiyangkul T, Leelaarporn P, Gulistan A, Chen W, Mukhopadhyay SC, Wilaiprasitporn T, Piyayotai S. Potential Applications of Mobile and Wearable Devices for Psychological Support During the COVID-19 Pandemic: A Review. IEEE SENSORS JOURNAL 2021; 21:7162-7178. [PMID: 37974630 PMCID: PMC8768987 DOI: 10.1109/jsen.2020.3046259] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Revised: 12/12/2020] [Accepted: 12/17/2020] [Indexed: 11/14/2023]
Abstract
The coronavirus disease 19 (COVID-19) pandemic that has been raging in 2020 does affect not only the physical state but also the mental health of the general population, particularly, that of the healthcare workers. Given the unprecedented large-scale impacts of the COVID-19 pandemic, digital technology has gained momentum as invaluable social interaction and health tracking tools in this time of great turmoil, in part due to the imposed state-wide mobilization limitations to mitigate the risk of infection that might arise from in-person socialization or hospitalization. Over the last five years, there has been a notable increase in the demand and usage of mobile and wearable devices as well as their adoption in studies of mental fitness. The purposes of this scoping review are to summarize evidence on the sweeping impact of COVID-19 on mental health as well as to evaluate the merits of the devices for remote psychological support. We conclude that the COVID-19 pandemic has inflicted a significant toll on the mental health of the population, leading to an upsurge in reports of pathological stress, depression, anxiety, and insomnia. It is also clear that mobile and wearable devices (e.g., smartwatches and fitness trackers) are well placed for identifying and targeting individuals with these psychological burdens in need of intervention. However, we found that most of the previous studies used research-grade wearable devices that are difficult to afford for the normal consumer due to their high cost. Thus, the possibility of replacing the research-grade wearable devices with the current smartwatch is also discussed.
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Affiliation(s)
- Kawisara Ueafuea
- Bio-Inspired Robotics and Neural Engineering (BRAIN) Lab, School of Information Science and Technology (IST)Vidyasirimedhi Institute of Science & Technology (VISTEC)Rayong21210Thailand
| | | | - Thapanun Sudhawiyangkul
- Bio-Inspired Robotics and Neural Engineering (BRAIN) Lab, School of Information Science and Technology (IST)Vidyasirimedhi Institute of Science & Technology (VISTEC)Rayong21210Thailand
| | - Pitshaporn Leelaarporn
- Bio-Inspired Robotics and Neural Engineering (BRAIN) Lab, School of Information Science and Technology (IST)Vidyasirimedhi Institute of Science & Technology (VISTEC)Rayong21210Thailand
| | - Ameen Gulistan
- Bio-Inspired Robotics and Neural Engineering (BRAIN) Lab, School of Information Science and Technology (IST)Vidyasirimedhi Institute of Science & Technology (VISTEC)Rayong21210Thailand
| | - Wei Chen
- Center for Intelligent Medical Electronics, School of Information Science and TechnologyFudan UniversityShanghai200433China
- Human Phenome Institute, Fudan UniversityShanghai200433China
| | | | - Theerawit Wilaiprasitporn
- Bio-Inspired Robotics and Neural Engineering (BRAIN) Lab, School of Information Science and Technology (IST)Vidyasirimedhi Institute of Science & Technology (VISTEC)Rayong21210Thailand
| | - Supanida Piyayotai
- Learning Institute, King Mongkut’s University of Technology ThonburiBangkok10140Thailand
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25
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Things that help out: designing smart wearables as partners in stress management. AI & SOCIETY 2021. [DOI: 10.1007/s00146-020-01003-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
AbstractWe propose an approach to designing smart wearables that act as partners to help people cope with stress in daily life. Our approach contributes to the developing field of smart wearables by addressing how technological capabilities can be designed to establish partnerships that consider the person, the situation, and the appropriate type of support. As such, this study also contributes to healthcare by opening up novel technology-supported routes to stress treatment and care. We present the results of a phenomenological study conducted with three war veterans who suffer from chronic posttraumatic stress disorder. We describe how their experiences of dealing with their stress informed our design approach, and discuss the implications of these results on smart wearables and stress management in general. We conclude by reflecting on the limitations of this study and directions for future work.
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26
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Hernandez N, Castro L, Medina-Quero J, Favela J, Michan L, Mortenson WB. Scoping Review of Healthcare Literature on Mobile, Wearable, and Textile Sensing Technology for Continuous Monitoring. JOURNAL OF HEALTHCARE INFORMATICS RESEARCH 2021; 5:270-299. [PMID: 33554008 PMCID: PMC7849621 DOI: 10.1007/s41666-020-00087-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Revised: 07/30/2020] [Accepted: 12/02/2020] [Indexed: 12/01/2022]
Abstract
Remote monitoring of health can reduce frequent hospitalisations, diminishing the burden on the healthcare system and cost to the community. Patient monitoring helps identify symptoms associated with diseases or disease-driven disorders, which makes it an essential element of medical diagnoses, clinical interventions, and rehabilitation treatments for severe medical conditions. This monitoring can be expensive and time-consuming and provide an incomplete picture of the state of the patient. In the last decade, there has been a significant increase in the adoption of mobile and wearable devices, along with the introduction of smart textile solutions that offer the possibility of continuous monitoring. These alternatives fuel a technology shift in healthcare, one that involves the continuous tracking and monitoring of individuals. This scoping review examines how mobile, wearable, and textile sensing technology have been permeating healthcare by offering alternate solutions to challenging issues, such as personalised prescriptions or home-based secondary prevention. To do so, we have selected 222 healthcare literature articles published from 2007 to 2019 and reviewed them following the PRISMA process under the schema of a scoping review framework. Overall, our findings show a recent increase in research on mobile sensing technology to address patient monitoring, reflected by 128 articles published in journals and 19 articles in conference proceedings between 2014 and 2019, which represents 57.65% and 8.55% respectively of all included articles.
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Affiliation(s)
- N Hernandez
- School of Computing, Campus Jordanstown, Ulster University, Newtownabbey, BT37-0QB UK
| | - L Castro
- Department of Computing and Design, Sonora Institute of Technology (ITSON), Ciudad Obregón, 85000 Mexico
| | - J Medina-Quero
- Department of Computer Science, Campus Las Lagunillas, University of Jaen, Jaén, 23071 Spain
| | - J Favela
- Department of Computer Science, Ensenada Centre for Scientific Research and Higher Education, Ensenada, 22860 Mexico
| | - L Michan
- Department of Comparative Biology, National Autonomous University of Mexico, Mexico City, 04510 Mexico
| | - W Ben Mortenson
- International Collaboration on Repair Discoveries and GF Strong Rehabilitation Research Program, University of British Columbia, Vancouver, V6T-1Z4 Canada
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27
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Sevil M, Rashid M, Hajizadeh I, Askari MR, Hobbs N, Brandt R, Park M, Quinn L, Cinar A. Discrimination of simultaneous psychological and physical stressors using wristband biosignals. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 199:105898. [PMID: 33360529 PMCID: PMC7878428 DOI: 10.1016/j.cmpb.2020.105898] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Accepted: 11/30/2020] [Indexed: 05/07/2023]
Abstract
BACKGROUND AND OBJECTIVE In this work, we address the problem of detecting and discriminating acute psychological stress (APS) in the presence of concurrent physical activity (PA) using wristband biosignals. We focused on signals available from wearable devices that can be worn in daily life because the ultimate objective of this work is to provide APS and PA information in real-time management of chronic conditions such as diabetes by automated personalized insulin delivery. Monitoring APS noninvasively throughout free-living conditions remains challenging because the responses to APS and PA of many physiological variables measured by wearable devices are similar. METHODS Various classification algorithms are compared to simultaneously detect and discriminate the PA (sedentary state, treadmill running, and stationary bike) and the type of APS (non-stress state, mental stress, and emotional anxiety). The impact of APS inducements is verified with commonly used self-reported questionnaires (The State-Trait Anxiety Inventory (STAI)). To aid the classification algorithms, novel features are generated from the physiological variables reported by a wristband device during 117 hours of experiments involving simultaneous APS inducement and PA. We also translate the APS assessment into a quantitative metric for use in predicting the adverse outcomes. RESULTS An accurate classification of the concurrent PA and APS states is achieved with an overall classification accuracy of 99% for PA and 92% for APS. The average accuracy of APS detection during sedentary state, treadmill running, and stationary bike is 97.3, 94.1, and 84.5%, respectively. CONCLUSIONS The simultaneous assessment of APS and PA throughout free-living conditions from a convenient wristband device is useful for monitoring the factors contributing to an elevated risk of acute events in people with chronic diseases like cardiovascular complications and diabetes.
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Affiliation(s)
- Mert Sevil
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, 60616, USA
| | - Mudassir Rashid
- Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL, 60616, USA
| | - Iman Hajizadeh
- Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL, 60616, USA
| | - Mohammad Reza Askari
- Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL, 60616, USA
| | - Nicole Hobbs
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, 60616, USA
| | - Rachel Brandt
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, 60616, USA
| | - Minsun Park
- College of Nursing, University of Illinois at Chicago, IL, 60616, USA
| | - Laurie Quinn
- College of Nursing, University of Illinois at Chicago, IL, 60616, USA
| | - Ali Cinar
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, 60616, USA; Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL, 60616, USA.
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Kim S, Lee K. Screening for Depression in Mobile Devices Using Patient Health Questionnaire-9 (PHQ-9) Data: A Diagnostic Meta-Analysis via Machine Learning Methods. Neuropsychiatr Dis Treat 2021; 17:3415-3430. [PMID: 34848962 PMCID: PMC8612669 DOI: 10.2147/ndt.s339412] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Accepted: 11/02/2021] [Indexed: 12/25/2022] Open
Abstract
PURPOSE Depression is a symptom commonly encountered in primary care; however, it is often not detected by doctors. Recently, disease diagnosis and treatment approaches have been attempted using smart devices. In this study, instrumental effectiveness was confirmed with the diagnostic meta-analysis of studies that demonstrated the diagnostic effectiveness of PHQ-9 for depression using mobile devices. PATIENTS AND METHODS We found all published and unpublished studies through EMBASE, MEDLINE, MEDLINE In-Process, and PsychINFO up to March 26, 2021. We performed a meta-analysis by including 1099 subjects in four studies. We performed a diagnostic meta-analysis according to the PHQ-9 cut-off score and machine learning algorithm techniques. Quality assessment was conducted using the QUADAS-2 tool. Data on the sensitivity and specificity of the studies included in the meta-analysis were extracted in a standardized format. Bivariate and summary receiver operating characteristic (SROC) curve were constructed using the metandi, midas, metabias, and metareg functions of the Stata algorithm meta-analysis words. RESULTS Using four studies out of the 5476 papers searched, a diagnostic meta-analysis of the PHQ-9 scores of 1099 people diagnosed with depression was performed. The pooled sensitivity and specificity were 0.797 (95% CI = 0.642-0.895) and 0.85 (95% CI = 0.780-0.900), respectively. The diagnostic odds ratio was 22.16 (95% CI = 7.273-67.499). Overall, a good balance was maintained, and no heterogeneity or publication bias was presented. CONCLUSION Through various machine learning algorithm techniques, it was possible to confirm that PHQ-9 depression screening in mobiles is an effective diagnostic tool when integrated into a diagnostic meta-analysis.
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Affiliation(s)
- Sunhae Kim
- Department of Psychiatry, Hanyang University Medical Center, Seoul, Korea
| | - Kounseok Lee
- Department of Psychiatry, Hanyang University Medical Center, Seoul, Korea
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Mishra V, Sen S, Chen G, Hao T, Rogers J, Chen CH, Kotz D. Evaluating the Reproducibility of Physiological Stress Detection Models. PROCEEDINGS OF THE ACM ON INTERACTIVE, MOBILE, WEARABLE AND UBIQUITOUS TECHNOLOGIES 2020; 4:147. [PMID: 36189150 PMCID: PMC9523764 DOI: 10.1145/3432220] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Recent advances in wearable sensor technologies have led to a variety of approaches for detecting physiological stress. Even with over a decade of research in the domain, there still exist many significant challenges, including a near-total lack of reproducibility across studies. Researchers often use some physiological sensors (custom-made or off-the-shelf), conduct a study to collect data, and build machine-learning models to detect stress. There is little effort to test the applicability of the model with similar physiological data collected from different devices, or the efficacy of the model on data collected from different studies, populations, or demographics. This paper takes the first step towards testing reproducibility and validity of methods and machine-learning models for stress detection. To this end, we analyzed data from 90 participants, from four independent controlled studies, using two different types of sensors, with different study protocols and research goals. We started by evaluating the performance of models built using data from one study and tested on data from other studies. Next, we evaluated new methods to improve the performance of stress-detection models and found that our methods led to a consistent increase in performance across all studies, irrespective of the device type, sensor type, or the type of stressor. Finally, we developed and evaluated a clustering approach to determine the stressed/not-stressed classification when applying models on data from different studies, and found that our approach performed better than selecting a threshold based on training data. This paper's thorough exploration of reproducibility in a controlled environment provides a critical foundation for deeper study of such methods, and is a prerequisite for tackling reproducibility in free-living conditions.
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Kołakowska A, Szwoch W, Szwoch M. A Review of Emotion Recognition Methods Based on Data Acquired via Smartphone Sensors. SENSORS (BASEL, SWITZERLAND) 2020; 20:E6367. [PMID: 33171646 PMCID: PMC7664622 DOI: 10.3390/s20216367] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 10/29/2020] [Accepted: 11/05/2020] [Indexed: 01/18/2023]
Abstract
In recent years, emotion recognition algorithms have achieved high efficiency, allowing the development of various affective and affect-aware applications. This advancement has taken place mainly in the environment of personal computers offering the appropriate hardware and sufficient power to process complex data from video, audio, and other channels. However, the increase in computing and communication capabilities of smartphones, the variety of their built-in sensors, as well as the availability of cloud computing services have made them an environment in which the task of recognising emotions can be performed at least as effectively. This is possible and particularly important due to the fact that smartphones and other mobile devices have become the main computer devices used by most people. This article provides a systematic overview of publications from the last 10 years related to emotion recognition methods using smartphone sensors. The characteristics of the most important sensors in this respect are presented, and the methods applied to extract informative features on the basis of data read from these input channels. Then, various machine learning approaches implemented to recognise emotional states are described.
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Affiliation(s)
- Agata Kołakowska
- Faculty of Electronics, Telecommunications and Informatics, Gdańsk University of Technology, 80-233 Gdansk, Poland; (W.S.); (M.S.)
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Galván-Tejada CE, Villagrana-Bañuelos KE, Zanella-Calzada LA, Moreno-Báez A, Luna-García H, Celaya-Padilla JM, Galván-Tejada JI, Gamboa-Rosales H. Univariate Analysis of Short-Chain Fatty Acids Related to Sudden Infant Death Syndrome. Diagnostics (Basel) 2020; 10:E896. [PMID: 33147746 PMCID: PMC7693700 DOI: 10.3390/diagnostics10110896] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Revised: 10/12/2020] [Accepted: 10/19/2020] [Indexed: 11/29/2022] Open
Abstract
Sudden infant death syndrome (SIDS) is defined as the death of a child under one year of age, during sleep, without apparent cause, after exhaustive investigation, so it is a diagnosis of exclusion. SIDS is the principal cause of death in industrialized countries. Inborn errors of metabolism (IEM) have been related to SIDS. These errors are a group of conditions characterized by the accumulation of toxic substances usually produced by an enzyme defect and there are thousands of them and included are the disorders of the β-oxidation cycle, similarly to what can affect the metabolism of different types of fatty acid chain (within these, short chain fatty acids (SCFAs)). In this work, an analysis of postmortem SCFAs profiles of children who died due to SIDS is proposed. Initially, a set of features containing SCFAs information, obtained from the NIH Common Fund's National Metabolomics Data Repository (NMDR) is submitted to an univariate analysis, developing a model based on the relationship between each feature and the binary output (death due to SIDS or not), obtaining 11 univariate models. Then, each model is validated, calculating their receiver operating characteristic curve (ROC curve) and area under the ROC curve (AUC) value. For those features whose models presented an AUC value higher than 0.650, a new multivariate model is constructed, in order to validate its behavior in comparison to the univariate models. In addition, a comparison between this multivariate model and a model developed based on the whole set of features is finally performed. From the results, it can be observed that each SCFA which comprises of the SFCAs profile, has a relationship with SIDS and could help in risk identification.
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Affiliation(s)
- Carlos E. Galván-Tejada
- Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, Zacatecas 98000, Mexico; (C.E.G.-T.); (K.E.V.-B.); (A.M.-B.); (H.L.-G.); (J.M.C.-P.); (J.I.G.-T.)
| | - Karen E. Villagrana-Bañuelos
- Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, Zacatecas 98000, Mexico; (C.E.G.-T.); (K.E.V.-B.); (A.M.-B.); (H.L.-G.); (J.M.C.-P.); (J.I.G.-T.)
| | | | - Arturo Moreno-Báez
- Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, Zacatecas 98000, Mexico; (C.E.G.-T.); (K.E.V.-B.); (A.M.-B.); (H.L.-G.); (J.M.C.-P.); (J.I.G.-T.)
| | - Huizilopoztli Luna-García
- Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, Zacatecas 98000, Mexico; (C.E.G.-T.); (K.E.V.-B.); (A.M.-B.); (H.L.-G.); (J.M.C.-P.); (J.I.G.-T.)
| | - Jose M. Celaya-Padilla
- Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, Zacatecas 98000, Mexico; (C.E.G.-T.); (K.E.V.-B.); (A.M.-B.); (H.L.-G.); (J.M.C.-P.); (J.I.G.-T.)
| | - Jorge I. Galván-Tejada
- Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, Zacatecas 98000, Mexico; (C.E.G.-T.); (K.E.V.-B.); (A.M.-B.); (H.L.-G.); (J.M.C.-P.); (J.I.G.-T.)
| | - Hamurabi Gamboa-Rosales
- Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, Zacatecas 98000, Mexico; (C.E.G.-T.); (K.E.V.-B.); (A.M.-B.); (H.L.-G.); (J.M.C.-P.); (J.I.G.-T.)
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Jakobsen P, Garcia-Ceja E, Riegler M, Stabell LA, Nordgreen T, Torresen J, Fasmer OB, Oedegaard KJ. Applying machine learning in motor activity time series of depressed bipolar and unipolar patients compared to healthy controls. PLoS One 2020; 15:e0231995. [PMID: 32833958 PMCID: PMC7446864 DOI: 10.1371/journal.pone.0231995] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Accepted: 08/09/2020] [Indexed: 11/18/2022] Open
Abstract
Current practice of assessing mood episodes in affective disorders largely depends on subjective observations combined with semi-structured clinical rating scales. Motor activity is an objective observation of the inner physiological state expressed in behavior patterns. Alterations of motor activity are essential features of bipolar and unipolar depression. The aim was to investigate if objective measures of motor activity can aid existing diagnostic practice, by applying machine-learning techniques to analyze activity patterns in depressed patients and healthy controls. Random Forrest, Deep Neural Network and Convolutional Neural Network algorithms were used to analyze 14 days of actigraph recorded motor activity from 23 depressed patients and 32 healthy controls. Statistical features analyzed in the dataset were mean activity, standard deviation of mean activity and proportion of zero activity. Various techniques to handle data imbalance were applied, and to ensure generalizability and avoid overfitting a Leave-One-User-Out validation strategy was utilized. All outcomes reports as measures of accuracy for binary tests. A Deep Neural Network combined with SMOTE class balancing technique performed a cut above the rest with a true positive rate of 0.82 (sensitivity) and a true negative rate of 0.84 (specificity). Accuracy was 0.84 and the Matthews Correlation Coefficient 0.65. Misclassifications appear related to data overlapping among the classes, so an appropriate future approach will be to compare mood states intra-individualistically. In summary, machine-learning techniques present promising abilities in discriminating between depressed patients and healthy controls in motor activity time series.
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Affiliation(s)
- Petter Jakobsen
- NORMENT, Division of Psychiatry, Haukeland University Hospital, Bergen, Norway
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
- * E-mail:
| | | | - Michael Riegler
- Simula Metropolitan Center for Digitalisation, Oslo, Norway
- Department of Informatics, University of Oslo, Oslo, Norway
| | - Lena Antonsen Stabell
- NORMENT, Division of Psychiatry, Haukeland University Hospital, Bergen, Norway
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Tine Nordgreen
- Division of Psychiatry, Haukeland University Hospital, Bergen, Norway
- Department of Clinical Psychology, Faculty of Psychology, University of Bergen, Bergen, Norway
| | - Jim Torresen
- Department of Informatics, University of Oslo, Oslo, Norway
| | - Ole Bernt Fasmer
- NORMENT, Division of Psychiatry, Haukeland University Hospital, Bergen, Norway
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Ketil Joachim Oedegaard
- NORMENT, Division of Psychiatry, Haukeland University Hospital, Bergen, Norway
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
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Burr C, Taddeo M, Floridi L. The Ethics of Digital Well-Being: A Thematic Review. SCIENCE AND ENGINEERING ETHICS 2020; 26:2313-2343. [PMID: 31933119 PMCID: PMC7417400 DOI: 10.1007/s11948-020-00175-8] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2019] [Accepted: 01/03/2020] [Indexed: 05/24/2023]
Abstract
This article presents the first thematic review of the literature on the ethical issues concerning digital well-being. The term 'digital well-being' is used to refer to the impact of digital technologies on what it means to live a life that is good for a human being. The review explores the existing literature on the ethics of digital well-being, with the goal of mapping the current debate and identifying open questions for future research. The review identifies major issues related to several key social domains: healthcare, education, governance and social development, and media and entertainment. It also highlights three broader themes: positive computing, personalised human-computer interaction, and autonomy and self-determination. The review argues that three themes will be central to ongoing discussions and research by showing how they can be used to identify open questions related to the ethics of digital well-being.
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Affiliation(s)
- Christopher Burr
- Oxford Internet Institute, University of Oxford, 1 St Giles, Oxford, OX1 3JS, UK.
| | - Mariarosaria Taddeo
- Oxford Internet Institute, University of Oxford, 1 St Giles, Oxford, OX1 3JS, UK
- The Alan Turing Institute, 96 Euston Road, London, NW1 2DB, UK
| | - Luciano Floridi
- Oxford Internet Institute, University of Oxford, 1 St Giles, Oxford, OX1 3JS, UK
- The Alan Turing Institute, 96 Euston Road, London, NW1 2DB, UK
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34
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Active Volume Control in Smart Phones Based on User Activity and Ambient Noise. SENSORS 2020; 20:s20154117. [PMID: 32722095 PMCID: PMC7435858 DOI: 10.3390/s20154117] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Revised: 06/18/2020] [Accepted: 07/21/2020] [Indexed: 11/20/2022]
Abstract
To communicate efficiently with a prospective user, auditory interfaces are employed in mobile communication devices. Diverse sounds in different volumes are used to alert the user in various devices such as mobile phones, modern laptops and domestic appliances. These alert noises behave erroneously in dynamic noise environments, leading to major annoyances to the user. In noisy environments, as sounds can be played quietly, this leads to the improper masked rendering of the necessary information. To overcome these issues, a multi-model sensing technique is developed as a smartphone application to achieve automatic volume control in a smart phone. Based on the ambient environment, the volume is automatically controlled such that it is maintained at an appropriate level for the user. By identifying the average noise level of the ambient environment from dynamic microphone and together with the activity recognition data obtained from the inertial sensors, the automatic volume control is achieved. Experiments are conducted with five different mobile devices at various noise-level environments and different user activity states. Results demonstrate the effectiveness of the proposed application for active volume control in dynamic environments.
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Talha IM, Salehin I, Debnath SC, Saifuzzaman M, Moon NN, Nur FN. Human Behaviour Impact to Use of Smartphones with the Python Implementation Using Naive Bayesian. 2020 11TH INTERNATIONAL CONFERENCE ON COMPUTING, COMMUNICATION AND NETWORKING TECHNOLOGIES (ICCCNT) 2020. [DOI: 10.1109/icccnt49239.2020.9225620] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
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Meina M, Ratajczak E, Sadowska M, Rykaczewski K, Dreszer J, Bałaj B, Biedugnis S, Węgrzyński W, Krasuski A. Heart Rate Variability and Accelerometry as Classification Tools for Monitoring Perceived Stress Levels-A Pilot Study on Firefighters. SENSORS (BASEL, SWITZERLAND) 2020; 20:E2834. [PMID: 32429383 PMCID: PMC7285091 DOI: 10.3390/s20102834] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/08/2020] [Revised: 05/03/2020] [Accepted: 05/09/2020] [Indexed: 12/18/2022]
Abstract
Chronic stress is the main cause of health problems in high-risk jobs. Wearable sensors can become an ecologically valid method of stress level assessment in real-life applications. We sought to determine a non-invasive technique for objective stress monitoring. Data were collected from firefighters during 24-h shifts using sensor belts equipped with a dry-lead electrocardiograph (ECG) and a three-axial accelerometer. Levels of stress experienced during fire incidents were evaluated via a brief self-assessment questionnaire. Types of physical activity were distinguished basing on accelerometer readings, and heart rate variability (HRV) time series were segmented accordingly into corresponding fragments. Those segments were classified as stress/no-stress conditions. Receiver Operating Characteristic (ROC) analysis showed true positive classification as stress condition for 15% of incidents (while maintaining almost zero False Positive Rate), which parallels the amount of truly stressful incidents reported in the questionnaires. These results show a firm correspondence between the perceived stress level and physiological data. Psychophysiological measurements are reliable indicators of stress even in ecological settings and appear promising for chronic stress monitoring in high-risk jobs, such as firefighting.
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Affiliation(s)
- Michał Meina
- Faculty of Physics, Astronomy and Informatics, Department of Applied Informatics, Nicolaus Copernicus University in Toruń, Grudziądzka 5, 87-100 Torun, Poland;
| | - Ewa Ratajczak
- Faculty of Physics, Astronomy and Informatics, Department of Applied Informatics, Nicolaus Copernicus University in Toruń, Grudziądzka 5, 87-100 Torun, Poland;
- Faculty of Philosophy and Social Sciences, Institute of Psychology, Nicolaus Copernicus University in Toruń, Podmurna 74, 87-100 Torun, Poland; (M.S.); (J.D.); (B.B.)
- Centre for Modern Interdisciplinary Technologies, Nicolaus Copernicus University in Toruń, Wileńska 4, 87-100 Torun, Poland;
| | - Maria Sadowska
- Faculty of Philosophy and Social Sciences, Institute of Psychology, Nicolaus Copernicus University in Toruń, Podmurna 74, 87-100 Torun, Poland; (M.S.); (J.D.); (B.B.)
| | - Krzysztof Rykaczewski
- Centre for Modern Interdisciplinary Technologies, Nicolaus Copernicus University in Toruń, Wileńska 4, 87-100 Torun, Poland;
| | - Joanna Dreszer
- Faculty of Philosophy and Social Sciences, Institute of Psychology, Nicolaus Copernicus University in Toruń, Podmurna 74, 87-100 Torun, Poland; (M.S.); (J.D.); (B.B.)
| | - Bibianna Bałaj
- Faculty of Philosophy and Social Sciences, Institute of Psychology, Nicolaus Copernicus University in Toruń, Podmurna 74, 87-100 Torun, Poland; (M.S.); (J.D.); (B.B.)
| | - Stanisław Biedugnis
- Institute of Safety Engineering, The Main School of Fire Service, Słowackiego 52/54, 01-629 Warsaw, Poland;
| | - Wojciech Węgrzyński
- Fire Research Department, Building Research Institute (ITB), 00-611 Warsaw, Poland;
| | - Adam Krasuski
- Institute of Safety Engineering, The Main School of Fire Service, Słowackiego 52/54, 01-629 Warsaw, Poland;
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Faurholt-Jepsen M, Þórarinsdóttir H, Vinberg M, Ullum H, Frost M, Bardram J, Kessing LV. Automatically generated smartphone data and subjective stress in healthy individuals - a pilot study. Nord J Psychiatry 2020; 74:293-300. [PMID: 31880486 DOI: 10.1080/08039488.2019.1705904] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Background: Most people will also experience symptoms of stress at some point. Smartphone use has increased during the last decade and may be a new way of monitoring stress. Thus, it is of interest to investigate whether automatically generated smartphone data reflecting smartphone use is associated with subjective stress in healthy individuals.Aims: to investigate whether automatically generated smartphone data (e.g. the number of outgoing sms/day) was associated with (1) smartphone-based subjectively reported perceived stress, (2) perceived stress (Cohen's Perceived Stress Scale (PSS)) (3) functioning (Functioning Assessment Short Test (FAST)) and (4) non-clinical depressive symptoms (Hamilton Depression Rating Scale 17-items (HDRS)).Methods: A cohort of 40 healthy blood donors used an app for daily self-assessment of stress for 16 weeks. At baseline participants filled out the PSS and were clinically evaluated using the FAST and the HDRS. The PSS assessment was repeated at the end of the study. Associations were estimated with linear mixed effect regression and linear regression models.Results: There were no statistically significant associations between automatically generated smartphone data and perceived stress, functioning or severity of depressive symptoms, respectively (e.g. the number of outgoing text messages/day and self-assessed stress (B = 0.30, 95% CI: -0.40; 0.99, p = .40).Conclusions: Participants presented with low levels of stress during the study. Automatically generated smartphone data was not able to catch potential subjective stress among healthy individuals in the present study. Due to the small sample and low levels of stress the results should be interpreted with caution.
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Affiliation(s)
- Maria Faurholt-Jepsen
- The Copenhagen Affective Disorder research Centre (CADIC), Psychiatric Centre Copenhagen, Copenhagen, Denmark
| | - Helga Þórarinsdóttir
- The Copenhagen Affective Disorder research Centre (CADIC), Psychiatric Centre Copenhagen, Copenhagen, Denmark
| | - Maj Vinberg
- The Copenhagen Affective Disorder research Centre (CADIC), Psychiatric Centre Copenhagen, Copenhagen, Denmark
| | - Henrik Ullum
- Department of Clinical Immunology, Copenhagen University Hospital, Copenhagen, Denmark
| | - Mads Frost
- Monsenso ApS, Langelinie, Copenhagen, Denmark
| | - Jakob Bardram
- Copenhagen Center for Health Technology, Technical University of Denmark, Lyngby, Denmark
| | - Lars Vedel Kessing
- The Copenhagen Affective Disorder research Centre (CADIC), Psychiatric Centre Copenhagen, Copenhagen, Denmark
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Montesinos V, Dell'Agnola F, Arza A, Aminifar A, Atienza D. Multi-Modal Acute Stress Recognition Using Off-the-Shelf Wearable Devices. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2019:2196-2201. [PMID: 31946337 DOI: 10.1109/embc.2019.8857130] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Monitoring stress and, in general, emotions has attracted a lot of attention over the past few decades. Stress monitoring has many applications, including high-risk missions and surgical procedures as well as mental/emotional health monitoring. In this paper, we evaluate the possibility of stress and emotion monitoring using off-the-shelf wearable sensors. To this aim, we propose a multi-modal machine-learning technique for acute stress episodes detection, by fusing the information careered in several biosignals and wearable sensors. Furthermore, we investigate the contribution of each wearable sensor in stress detection and demonstrate the possibility of acute stress recognition using wearable devices. In particular, we acquire the physiological signals using the Shimmer3 ECG Unit and the Empatica E4 wristband. Our experimental evaluation shows that it is possible to detect acute stress episodes with an accuracy of 84.13%, for an unseen test set, using multi-modal machinelearning and sensor-fusion techniques.
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Mishra V, Pope G, Lord S, Lewia S, Lowens B, Caine K, Sen S, Halter R, Kotz D. Continuous Detection of Physiological Stress with Commodity Hardware. ACM TRANSACTIONS ON COMPUTING FOR HEALTHCARE 2020; 1:8. [PMID: 32832933 PMCID: PMC7442214 DOI: 10.1145/3361562] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/01/2018] [Accepted: 09/01/2019] [Indexed: 12/31/2022]
Abstract
Timely detection of an individual's stress level has the potential to improve stress management, thereby reducing the risk of adverse health consequences that may arise due to mismanagement of stress. Recent advances in wearable sensing have resulted in multiple approaches to detect and monitor stress with varying levels of accuracy. The most accurate methods, however, rely on clinical-grade sensors to measure physiological signals; they are often bulky, custom made, and expensive, hence limiting their adoption by researchers and the general public. In this article, we explore the viability of commercially available off-the-shelf sensors for stress monitoring. The idea is to be able to use cheap, nonclinical sensors to capture physiological signals and make inferences about the wearer's stress level based on that data. We describe a system involving a popular off-the-shelf heart rate monitor, the Polar H7; we evaluated our system with 26 participants in both a controlled lab setting with three well-validated stress-inducing stimuli and in free-living field conditions. Our analysis shows that using the off-the-shelf sensor alone, we were able to detect stressful events with an F1-score of up to 0.87 in the lab and 0.66 in the field, on par with clinical-grade sensors.
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Arapovic-Johansson B, Wåhlin C, Hagberg J, Kwak L, Axén I, Björklund C, Jensen I. Experience of Stress Assessed by Text Messages and Its Association with Objective Workload-A Longitudinal Study. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:E680. [PMID: 31973041 PMCID: PMC7037329 DOI: 10.3390/ijerph17030680] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/11/2019] [Revised: 01/15/2020] [Accepted: 01/19/2020] [Indexed: 12/23/2022]
Abstract
Exploring stress trajectories in detail and over a long time may give valuable information in terms of both understanding and practice. We followed a group of primary health care employees in a randomized controlled trial. The objective was to describe their experience of stress, explore the intra-individual variability and examine the association between the experience of stress and the objective workload. Weekly text messages with a single item stress question were distributed in two time series: 12 weeks at the beginning of the trial and 26 weeks after the 6-month follow up. Aggregated objective data about workload were collected from their administration office and related to stress levels. There was a seasonal variation, with higher stress during the fall than in spring and summer. The analysis comparing high and low stress subgroups showed that the stress trajectory of a high-stress subgroup was different from that of a low-stress subgroup. Individuals with high exhaustion scores had higher odds of belonging to a subgroup of individuals with high intra-individual variability in stress experience. The objective workload was measured in two ways and was strongly associated with the stress experience. We found that the lower the productivity, the higher the feeling of stress.
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Affiliation(s)
- Bozana Arapovic-Johansson
- Unit of Intervention and Implementation Research for Worker Health, Institute of Environmental Medicine, Karolinska Institute, 171 77 Solna, Sweden; (C.W.); (J.H.); (L.K.); (I.A.); (C.B.); (I.J.)
| | - Charlotte Wåhlin
- Unit of Intervention and Implementation Research for Worker Health, Institute of Environmental Medicine, Karolinska Institute, 171 77 Solna, Sweden; (C.W.); (J.H.); (L.K.); (I.A.); (C.B.); (I.J.)
- Occupational and Environmental Medicine Center, and Department of Health, Medicine and Caring sciences, Unit of Clinical Medicine, Linköping University, 581 83 Linköping, Sweden
| | - Jan Hagberg
- Unit of Intervention and Implementation Research for Worker Health, Institute of Environmental Medicine, Karolinska Institute, 171 77 Solna, Sweden; (C.W.); (J.H.); (L.K.); (I.A.); (C.B.); (I.J.)
| | - Lydia Kwak
- Unit of Intervention and Implementation Research for Worker Health, Institute of Environmental Medicine, Karolinska Institute, 171 77 Solna, Sweden; (C.W.); (J.H.); (L.K.); (I.A.); (C.B.); (I.J.)
| | - Iben Axén
- Unit of Intervention and Implementation Research for Worker Health, Institute of Environmental Medicine, Karolinska Institute, 171 77 Solna, Sweden; (C.W.); (J.H.); (L.K.); (I.A.); (C.B.); (I.J.)
| | - Christina Björklund
- Unit of Intervention and Implementation Research for Worker Health, Institute of Environmental Medicine, Karolinska Institute, 171 77 Solna, Sweden; (C.W.); (J.H.); (L.K.); (I.A.); (C.B.); (I.J.)
| | - Irene Jensen
- Unit of Intervention and Implementation Research for Worker Health, Institute of Environmental Medicine, Karolinska Institute, 171 77 Solna, Sweden; (C.W.); (J.H.); (L.K.); (I.A.); (C.B.); (I.J.)
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Nappi I, de Campos Ribeiro G. Internet of Things technology applications in the workplace environment: a critical review. JOURNAL OF CORPORATE REAL ESTATE 2020. [DOI: 10.1108/jcre-06-2019-0028] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Purpose
The purpose of this study is to examine the use of IoT technology (RFID technology, sensor networks, wearable devices and other smart items) in office settings and its respective impact on the optimization of employees’ productivity and workspace effectiveness.
Design/methodology/approach
The paper reviews 41 relevant publications reporting IoT use in office settings to identify how this technology has been applied in office settings and what topics are mostly addressed in the literature; how IoT technology improves employees’ productivity; and what the benefits and risks associated with IoT use in the workplace environment are.
Findings
Two main areas of application of IoT technology in the workplace environment were identified. The first one concerns the influence of the physical characteristics of workplaces on aspects related to workspace effectiveness. The second one is employee-centered and concerns the use of IoT data to identify employees’ social behavior, physiological data and emotional estates associated with productivity. IoT technology provides real-time data with speedy information retrieval. However, its deployment in office settings is not exempt from risks. Employee workplace surveillance, re-individualization of the IoT data and employee refusal of IoT technology in office settings are the main risks associated with this technology.
Originality/value
This literature review categorizes IoT application in office settings according to two perspectives and highlights employees' attitudes, user-experience of IoT technology and the risks associated with this technology. These results will help researchers and workplace managers interested in the deployment of this technology in the workplace environment.
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Masud MT, Mamun MA, Thapa K, Lee DH, Griffiths MD, Yang SH. Unobtrusive monitoring of behavior and movement patterns to detect clinical depression severity level via smartphone. J Biomed Inform 2020; 103:103371. [PMID: 31935462 DOI: 10.1016/j.jbi.2019.103371] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2019] [Revised: 12/09/2019] [Accepted: 12/29/2019] [Indexed: 12/21/2022]
Abstract
The number of individuals with mental disorders is increasing and they are commonly found among individuals who avoid social interaction and like to live alone. Amongst such mental health disorders is depression which is both common and serious. The present paper introduces a method to assess the depression level of an individual using a smartphone by monitoring their daily activities. The time domain characteristics from a smartphone acceleration sensor were used alongside a vector machine algorithm to classify physical activities. Additionally, the geographical location information was clustered using a smartphone GPS sensor to simplify movement patterns. A total of 12 features were extracted from individuals' physical activity and movement patterns and were analyzed alongside their weekly depression scores using the nine-item Patient Health Questionnaire. Using a wrapper feature selection method, a subset of features was selected and applied to a linear regression model to estimate the depression score. The support vector machine algorithm was then used to classify the depression severity level among individuals (absence, moderate, severe) and had an accuracy of 87.2% in severe depression cases which outperformed other classification models including the k-nearest neighbor and artificial neural network. This method of identifying depression is a cost-effective solution for long-term use and can monitor individuals for depression without invading their personal space or creating other day-to-day disturbances.
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Affiliation(s)
- Mohammed T Masud
- Department of Electronic Engineering, Kwangwoon University, Seoul 139-701, Republic of Korea; Undergraduate Research Organization, Gerua Rd, Savar, Dhaka, Bangladesh.
| | - Mohammed A Mamun
- Undergraduate Research Organization, Gerua Rd, Savar, Dhaka, Bangladesh; Department of Public Health & Informatics, Jahangirnagar University, Savar, Dhaka, Bangladesh.
| | - K Thapa
- Department of Electronic Engineering, Kwangwoon University, Seoul 139-701, Republic of Korea.
| | - D H Lee
- Department of Electronic Engineering, Kwangwoon University, Seoul 139-701, Republic of Korea.
| | - Mark D Griffiths
- International Gaming Research Unit, Psychology Department, Nottingham Trent University, United Kingdom.
| | - S-H Yang
- Department of Electronic Engineering, Kwangwoon University, Seoul 139-701, Republic of Korea.
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Trifan A, Oliveira M, Oliveira JL. Passive Sensing of Health Outcomes Through Smartphones: Systematic Review of Current Solutions and Possible Limitations. JMIR Mhealth Uhealth 2019; 7:e12649. [PMID: 31444874 PMCID: PMC6729117 DOI: 10.2196/12649] [Citation(s) in RCA: 62] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2018] [Revised: 05/24/2019] [Accepted: 05/28/2019] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND Technological advancements, together with the decrease in both price and size of a large variety of sensors, has expanded the role and capabilities of regular mobile phones, turning them into powerful yet ubiquitous monitoring systems. At present, smartphones have the potential to continuously collect information about the users, monitor their activities and behaviors in real time, and provide them with feedback and recommendations. OBJECTIVE This systematic review aimed to identify recent scientific studies that explored the passive use of smartphones for generating health- and well-being-related outcomes. In addition, it explores users' engagement and possible challenges in using such self-monitoring systems. METHODS A systematic review was conducted, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, to identify recent publications that explore the use of smartphones as ubiquitous health monitoring systems. We ran reproducible search queries on PubMed, IEEE Xplore, ACM Digital Library, and Scopus online databases and aimed to find answers to the following questions: (1) What is the study focus of the selected papers? (2) What smartphone sensing technologies and data are used to gather health-related input? (3) How are the developed systems validated? and (4) What are the limitations and challenges when using such sensing systems? RESULTS Our bibliographic research returned 7404 unique publications. Of these, 118 met the predefined inclusion criteria, which considered publication dates from 2014 onward, English language, and relevance for the topic of this review. The selected papers highlight that smartphones are already being used in multiple health-related scenarios. Of those, physical activity (29.6%; 35/118) and mental health (27.9; 33/118) are 2 of the most studied applications. Accelerometers (57.7%; 67/118) and global positioning systems (GPS; 40.6%; 48/118) are 2 of the most used sensors in smartphones for collecting data from which the health status or well-being of its users can be inferred. CONCLUSIONS One relevant outcome of this systematic review is that although smartphones present many advantages for the passive monitoring of users' health and well-being, there is a lack of correlation between smartphone-generated outcomes and clinical knowledge. Moreover, user engagement and motivation are not always modeled as prerequisites, which directly affects user adherence and full validation of such systems.
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Affiliation(s)
- Alina Trifan
- Department of Electronics, Telecommunications and Informatics, University of Aveiro, Aveiro, Portugal
- Institute of Electronics and Informatics Engineering of Aveiro, University of Aveiro, Aveiro, Portugal
| | - Maryse Oliveira
- Department of Electronics, Telecommunications and Informatics, University of Aveiro, Aveiro, Portugal
- Institute of Electronics and Informatics Engineering of Aveiro, University of Aveiro, Aveiro, Portugal
| | - José Luís Oliveira
- Department of Electronics, Telecommunications and Informatics, University of Aveiro, Aveiro, Portugal
- Institute of Electronics and Informatics Engineering of Aveiro, University of Aveiro, Aveiro, Portugal
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Abstract
Nowadays, with rapid advancement of both the upcoming 5G architecture construction and emerging Internet of Things (IoT) scenarios, Device-to-Device (D2D) communication provides a novel paradigm for mobile networking. By facilitating continuous and high data rate services between physically proximate devices without interconnection with access points (AP) or service network (SN), spectral efficiency of the 5G network can be drastically increased. However, due to its inherent open wireless communicating features, security issues and privacy risks in D2D communication remain unsolved in spite of its benefits and prosperous future. Hence, proper D2D authentication mechanisms among the D2D entities are of great significance. Moreover, the increasing proliferation of smartphones enables seamlessly biometric sensor data collecting and processing, which highly correspond to the user’s unique behavioral characteristics. For the above consideration, we present a secure certificateless D2D authenticating mechanism intended for extreme scenarios in this paper. In the assumption, the key updating mechanism only requires a small modification in the SN side, while the decryption information of user equipment (UEs) remains constant as soon as the UEs are validated. Note that a symmetric key mechanism is adopted for the further data transmission. Additionally, the user activities data from smartphone sensors are analyzed for continuous authentication, which is periodically conducted after the initial validation. Note that in the assumed scenario, most of the UEs are out of the effective range of cellular networks. In this case, the UEs are capable of conducting data exchange without cellular connection. Security analysis demonstrates that the proposed scheme can provide adequate security properties as well as resistance to various attacks. Furthermore, performance analysis proves that the proposed scheme is efficient compared with state-of-the-art D2D authentication schemes.
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Puli A, Kushki A. Toward Automatic Anxiety Detection in Autism: A Real-Time Algorithm for Detecting Physiological Arousal in the Presence of Motion. IEEE Trans Biomed Eng 2019; 67:646-657. [PMID: 31144623 DOI: 10.1109/tbme.2019.2919273] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
OBJECTIVE Anxiety is a significant clinical concern in autism spectrum disorder (ASD) due to its negative impact on physical and psychological health. Treatment of anxiety in ASD remains a challenge due to difficulties with self-awareness and communication of anxiety symptoms. To reduce these barriers to treatment, physiological markers of autonomic arousal, collected through wearable sensors, have been proposed as real-time, objective, and language-free measures of anxiety. A critical limitation of the existing anxiety detection systems is that physiological arousal is not specific to anxiety and can occur with other user states such as physical activity. This can result in false positives, which can hinder the operation of these systems in real-world situations. The objective of this paper was to address this challenge by proposing an approach for real-time detection and mitigation of physical activity effects. METHODS A novel multiple model Kalman-like filter is proposed to integrate heart rate and accelerometry signals. The filter tracks user heart rate under different motion assumptions and chooses the appropriate model for anxiety detection based on user motion conditions. RESULTS Evaluation of the algorithm using data from a sample of children with ASD shows a significant reduction in false positives compared to the state-of-the-art, and an overall arousal detection accuracy of 93%. CONCLUSION The proposed method is able to reduce false detections due to user motion and effectively detect arousal states during movement periods. SIGNIFICANCE The results add to the growing evidence supporting the feasibility of wearable technologies for anxiety detection and management in naturalistic settings.
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Continuous Stress Detection Using Wearable Sensors in Real Life: Algorithmic Programming Contest Case Study. SENSORS 2019; 19:s19081849. [PMID: 31003456 PMCID: PMC6515276 DOI: 10.3390/s19081849] [Citation(s) in RCA: 74] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/02/2019] [Revised: 04/16/2019] [Accepted: 04/16/2019] [Indexed: 11/17/2022]
Abstract
The negative effects of mental stress on human health has been known for decades. High-level stress must be detected at early stages to prevent these negative effects. After the emergence of wearable devices that could be part of our lives, researchers have started detecting extreme stress of individuals with them during daily routines. Initial experiments were performed in laboratory environments and recently a number of works took a step outside the laboratory environment to the real-life. We developed an automatic stress detection system using physiological signals obtained from unobtrusive smart wearable devices which can be carried during the daily life routines of individuals. This system has modality-specific artifact removal and feature extraction methods for real-life conditions. We further tested our system in a real-life setting with collected physiological data from 21 participants of an algorithmic programming contest for nine days. This event had lectures, contests as well as free time. By using heart activity, skin conductance and accelerometer signals, we successfully discriminated contest stress, relatively higher cognitive load (lecture) and relaxed time activities by using different machine learning methods.
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Can YS, Arnrich B, Ersoy C. Stress detection in daily life scenarios using smart phones and wearable sensors: A survey. J Biomed Inform 2019; 92:103139. [PMID: 30825538 DOI: 10.1016/j.jbi.2019.103139] [Citation(s) in RCA: 95] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2018] [Revised: 02/06/2019] [Accepted: 02/18/2019] [Indexed: 10/27/2022]
Abstract
Stress has become a significant cause for many diseases in the modern society. Recently, smartphones, smartwatches and smart wrist-bands have become an integral part of our lives and have reached a widespread usage. This raised the question of whether we can detect and prevent stress with smartphones and wearable sensors. In this survey, we will examine the recent works on stress detection in daily life which are using smartphones and wearable devices. Although there are a number of works related to stress detection in controlled laboratory conditions, the number of studies examining stress detection in daily life is limited. We will divide and investigate the works according to used physiological modality and their targeted environment such as office, campus, car and unrestricted daily life conditions. We will also discuss promising techniques, alleviation methods and research challenges.
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Affiliation(s)
- Yekta Said Can
- Bogazici University, Computer Engineering Department, Turkey.
| | - Bert Arnrich
- Hasso Plattner Institute, University of Potsdam, Germany
| | - Cem Ersoy
- Bogazici University, Computer Engineering Department, Turkey
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S AA, P S, V S, S SK, A S, Akl TJ, P PS, Sivaprakasam M. Electrodermal Activity Based Pre-surgery Stress Detection Using a Wrist Wearable. IEEE J Biomed Health Inform 2019; 24:92-100. [PMID: 30668508 DOI: 10.1109/jbhi.2019.2893222] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Surgery is a particularly potent stressor and the detrimental effects of stress on people undergoing any surgery is indisputable. When left unchecked, the pre-surgery stress adversely impacts people's physical and psychological well-being, and may even evolve into severe pathological states. Therefore, it is essential to identify levels of preoperative stress in surgical patients. This paper focuses on developing an automatic pre-surgery stress detection scheme based on electrodermal activity (EDA). The measurement set up involves a wrist wearable that monitors EDA of a subject continuously in the most non-invasive and unobtrusive manner. Data were collected from 41 subjects [17 females and 24 males, age: 54.8 ± 16.8 years (mean ± SD)], who subsequently underwent different surgical procedures at the Sri Ramakrishna Hospital, Coimbatore, India. A supervised machine learning algorithm that detects motion artifacts in the recorded EDA data was developed. It yielded an accuracy of 97.83% on a new user dataset. The clean EDA data were further analyzed to determine low, moderate, and high levels of stress. A novel localized supervised learning scheme based on the adaptive partitioning of the dataset was adopted for stress detection. Consequently, the interindividual variability in the EDA due to person-specific factors such as the sweat gland density and skin thickness, which may lead to erroneous classification, could be eliminated. The scheme yielded a classification accuracy of 85.06% on a new user dataset and proved to be more effective than the general supervised classification model.
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Feature Extraction in Motor Activity Signal: Towards a Depression Episodes Detection in Unipolar and Bipolar Patients. Diagnostics (Basel) 2019; 9:diagnostics9010008. [PMID: 30634621 PMCID: PMC6468429 DOI: 10.3390/diagnostics9010008] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2018] [Revised: 12/27/2018] [Accepted: 01/07/2019] [Indexed: 01/04/2023] Open
Abstract
Depression is a mental disorder characterized by recurrent sadness and loss of interest in the enjoyment of the positive aspects of life, in addition to fatigue, causing inability to perform daily activities, which leads to a loss of quality of life. To monitor depression (unipolar and bipolar patients), traditional methods rely on reports from patients; nevertheless, bias is commonly present in them. To overcome this problem, Ecological Momentary Assessment (EMA) reports have been widely used, which include data of the behavior, feelings and other types of activities recorded almost in real time through the use of portable devices and smartphones containing motion sensors. In this work a methodology was proposed to detect depressive subjects from control subjects based in the data of their motor activity, recorded by a wearable device, obtained from the "Depresjon" database. From the motor activity signals, the extraction of statistical features was carried out to subsequently feed a random forest classifier. Results show a sensitivity value of 0.867, referring that those subjects with presence of depression have a degree of 86.7% of being correctly classified, while the specificity shows a value of 0.919, referring that those subjects with absence of depression have a degree of 91.9% of being classified with a correct response, using the motor activity signal provided from the wearable device. Based on these results, it is concluded that the motor activity allows distinguishing between the two classes, providing a preliminary and automated tool to specialists for the diagnosis of depression.
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Stress Detection Using Low Cost Heart Rate Sensors. JOURNAL OF HEALTHCARE ENGINEERING 2018; 2016. [PMID: 27372071 PMCID: PMC5058562 DOI: 10.1155/2016/5136705] [Citation(s) in RCA: 49] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/11/2016] [Accepted: 05/05/2016] [Indexed: 12/01/2022]
Abstract
The automated detection of stress is a central problem for ambient assisted living solutions. The paper presents the concepts and results of two studies targeted at stress detection with a low cost heart rate sensor, a chest belt. In the device validation study (n = 5), we compared heart rate data and other features from the belt to those measured by a gold standard device to assess the reliability of the sensor. With simple synchronization and data cleaning algorithm, we were able to select highly (>97%) correlated, low average error (2.2%) data segments of considerable length from the chest data for further processing. The protocol for the clinical study (n = 46) included a relax phase followed by a phase with provoked mental stress, 10 minutes each. We developed a simple method for the detection of the stress using only three time-domain features of the heart rate signal. The method produced accuracy of 74.6%, sensitivity of 75.0%, and specificity of 74.2%, which is impressive compared to the performance of two state-of-the-art methods run on the same data. Since the proposed method uses only time-domain features, it can be efficiently implemented on mobile devices.
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