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Bovens D, van Baarle E, Ziesemer K, Molewijk B. The ethical dimension of personal health monitoring in the armed forces: a scoping review. BMC Med Ethics 2024; 25:88. [PMID: 39127660 DOI: 10.1186/s12910-024-01086-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2024] [Accepted: 08/01/2024] [Indexed: 08/12/2024] Open
Abstract
BACKGROUND Personal Health Monitoring (PHM) has the potential to enhance soldier health outcomes. To promote morally responsible development, implementation, and use of PHM in the armed forces, it is important to be aware of the inherent ethical dimension of PHM. In order to improve the understanding of the ethical dimension, a scoping review of the existing academic literature on the ethical dimension of PHM was conducted. METHODS Four bibliographical databases (Ovid/Medline, Embase.com, Clarivate Analytics/Web of Science Core Collection, and Elsevier/SCOPUS) were searched for relevant literature from their inception to June 1, 2023. Studies were included if they sufficiently addressed the ethical dimension of PHM and were related to or claimed relevance for the military. After selection and extraction, the data was analysed using a qualitative thematic approach. RESULTS A total of 9,071 references were screened. After eligibility screening, 19 articles were included for this review. The review identifies and describes three categories reflecting the ethical dimension of PHM in the military: (1) utilitarian considerations, (2) value-based considerations, and (3) regulatory responsibilities. The four main values that have been identified as being of concern are those of privacy, security, trust, and autonomy. CONCLUSIONS This review demonstrates that PHM in the armed forces is primarily approached from a utilitarian perspective, with a focus on its benefits, without explicit critical deliberation on PHM's potential moral downsides. Also, the review highlights a significant research gap with a specific lack of empirical studies focussing specifically on the ethical dimension of PHM. Awareness of the inherent ethical dimension of PHM in the military, including value conflicts and how to balance them, can help to contribute to a morally responsible development, implementation, and use of PHM in the armed forces.
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Affiliation(s)
- Dave Bovens
- Department of Ethics, Law and Humanities, Amsterdam UMC, Location VU University Medical Center, De Boelelaan 1089a, 1081 HV, Amsterdam, The Netherlands.
- Defence Healthcare Organisation, Ministry of Defence, Utrecht, The Netherlands.
| | - Eva van Baarle
- Department of Ethics, Law and Humanities, Amsterdam UMC, Location VU University Medical Center, De Boelelaan 1089a, 1081 HV, Amsterdam, The Netherlands
- Faculty of Military Sciences, Netherlands Defence Academy, Breda, The Netherlands
| | - Kirsten Ziesemer
- Medical Library, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Bert Molewijk
- Department of Ethics, Law and Humanities, Amsterdam UMC, Location VU University Medical Center, De Boelelaan 1089a, 1081 HV, Amsterdam, The Netherlands
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El-Helaly M. Artificial Intelligence and Occupational Health and Safety, Benefits and Drawbacks. LA MEDICINA DEL LAVORO 2024; 115:e2024014. [PMID: 38686574 PMCID: PMC11181216 DOI: 10.23749/mdl.v115i2.15835] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Accepted: 04/05/2024] [Indexed: 05/02/2024]
Abstract
This paper discusses the impact of artificial intelligence (AI) on occupational health and safety. Although the integration of AI into the field of occupational health and safety is still in its early stages, it has numerous applications in the workplace. Some of these applications offer numerous benefits for the health and safety of workers, such as continuous monitoring of workers' health and safety and the workplace environment through wearable devices and sensors. However, AI might have negative impacts in the workplace, such as ethical worries and data privacy concerns. To maximize the benefits and minimize the drawbacks of AI in the workplace, certain measures should be applied, such as training for both employers and employees and setting policies and guidelines regulating the integration of AI in the workplace.
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Affiliation(s)
- Mohamed El-Helaly
- Occupational and Environmental Medicine, Faculty of Medicine, Mansoura University, Mansoura City, Egypt
- Faculty of Medicine, New Mansoura University, New Mansoura City, Egypt
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Bustos D, Cardoso R, Carvalho DD, Guedes J, Vaz M, Torres Costa J, Santos Baptista J, Fernandes RJ. Exploring the Applicability of Physiological Monitoring to Manage Physical Fatigue in Firefighters. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23115127. [PMID: 37299854 DOI: 10.3390/s23115127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 05/24/2023] [Accepted: 05/25/2023] [Indexed: 06/12/2023]
Abstract
Physical fatigue reduces productivity and quality of work while increasing the risk of injuries and accidents among safety-sensitive professionals. To prevent its adverse effects, researchers are developing automated assessment methods that, despite being highly accurate, require a comprehensive understanding of underlying mechanisms and variables' contributions to determine their real-life applicability. This work aims to evaluate the performance variations of a previously developed four-level physical fatigue model when alternating its inputs to have a comprehensive view of the impact of each physiological variable on the model's functioning. Data from heart rate, breathing rate, core temperature and personal characteristics from 24 firefighters during an incremental running protocol were used to develop the physical fatigue model based on an XGBoosted tree classifier. The model was trained 11 times with different input combinations resulting from alternating four groups of features. Performance measures from each case showed that heart rate is the most relevant signal for estimating physical fatigue. Breathing rate and core temperature enhanced the model when combined with heart rate but showed poor performance individually. Overall, this study highlights the advantage of using more than one physiological measure for improving physical fatigue modelling. The findings can contribute to variables and sensor selection in occupational applications and as the foundation for further field research.
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Affiliation(s)
- Denisse Bustos
- Associated Laboratory for Energy, Transports and Aeronautics-LAETA (PROA), Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal
| | - Ricardo Cardoso
- Centre of Research, Education, Innovation and Intervention in Sport-CIFI2D, Faculty of Sport, University of Porto, 4200-450 Porto, Portugal
- Porto Biomechanics Laboratory, Faculty of Sport, University of Porto, 4200-450 Porto, Portugal
| | - Diogo D Carvalho
- Centre of Research, Education, Innovation and Intervention in Sport-CIFI2D, Faculty of Sport, University of Porto, 4200-450 Porto, Portugal
- Porto Biomechanics Laboratory, Faculty of Sport, University of Porto, 4200-450 Porto, Portugal
| | - Joana Guedes
- Associated Laboratory for Energy, Transports and Aeronautics-LAETA (PROA), Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal
| | - Mário Vaz
- Associated Laboratory for Energy, Transports and Aeronautics-LAETA (PROA), Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal
- Porto Biomechanics Laboratory, Faculty of Sport, University of Porto, 4200-450 Porto, Portugal
| | - José Torres Costa
- Associated Laboratory for Energy, Transports and Aeronautics-LAETA (PROA), Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal
| | - João Santos Baptista
- Associated Laboratory for Energy, Transports and Aeronautics-LAETA (PROA), Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal
- Porto Biomechanics Laboratory, Faculty of Sport, University of Porto, 4200-450 Porto, Portugal
| | - Ricardo J Fernandes
- Centre of Research, Education, Innovation and Intervention in Sport-CIFI2D, Faculty of Sport, University of Porto, 4200-450 Porto, Portugal
- Porto Biomechanics Laboratory, Faculty of Sport, University of Porto, 4200-450 Porto, Portugal
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Brambilla C, Lavit Nicora M, Storm F, Reni G, Malosio M, Scano A. Biomechanical Assessments of the Upper Limb for Determining Fatigue, Strain and Effort from the Laboratory to the Industrial Working Place: A Systematic Review. Bioengineering (Basel) 2023; 10:bioengineering10040445. [PMID: 37106632 PMCID: PMC10135542 DOI: 10.3390/bioengineering10040445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 03/24/2023] [Accepted: 04/03/2023] [Indexed: 04/08/2023] Open
Abstract
Recent human-centered developments in the industrial field (Industry 5.0) lead companies and stakeholders to ensure the wellbeing of their workers with assessments of upper limb performance in the workplace, with the aim of reducing work-related diseases and improving awareness of the physical status of workers, by assessing motor performance, fatigue, strain and effort. Such approaches are usually developed in laboratories and only at times they are translated to on-field applications; few studies summarized common practices for the assessments. Therefore, our aim is to review the current state-of-the-art approaches used for the assessment of fatigue, strain and effort in working scenarios and to analyze in detail the differences between studies that take place in the laboratory and in the workplace, in order to give insights on future trends and directions. A systematic review of the studies aimed at evaluating the motor performance, fatigue, strain and effort of the upper limb targeting working scenarios is presented. A total of 1375 articles were found in scientific databases and 288 were analyzed. About half of the scientific articles are focused on laboratory pilot studies investigating effort and fatigue in laboratories, while the other half are set in working places. Our results showed that assessing upper limb biomechanics is quite common in the field, but it is mostly performed with instrumental assessments in laboratory studies, while questionnaires and scales are preferred in working places. Future directions may be oriented towards multi-domain approaches able to exploit the potential of combined analyses, exploitation of instrumental approaches in workplace, targeting a wider range of people and implementing more structured trials to translate pilot studies to real practice.
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Affiliation(s)
- Cristina Brambilla
- Istituto di Sistemi e Tecnologie Industriali Intelligenti per il Manifatturiero Avanzato (STIIMA), Consiglio Nazionale delle Ricerche (CNR), Via Previati 1/E, 23900 Lecco, Italy
| | - Matteo Lavit Nicora
- Istituto di Sistemi e Tecnologie Industriali Intelligenti per il Manifatturiero Avanzato (STIIMA), Consiglio Nazionale delle Ricerche (CNR), Via Previati 1/E, 23900 Lecco, Italy
- Industrial Engineering Department, University of Bologna, 40126 Bologna, Italy
| | - Fabio Storm
- Bioengineering Laboratory, Scientific Institute, IRCCS “Eugenio Medea”, 23842 Bosisio Parini, Italy
| | - Gianluigi Reni
- Informatics Department, Autonomous Province of Bolzano, 39100 Bolzano, Italy
| | - Matteo Malosio
- Istituto di Sistemi e Tecnologie Industriali Intelligenti per il Manifatturiero Avanzato (STIIMA), Consiglio Nazionale delle Ricerche (CNR), Via Previati 1/E, 23900 Lecco, Italy
| | - Alessandro Scano
- Istituto di Sistemi e Tecnologie Industriali Intelligenti per il Manifatturiero Avanzato (STIIMA), Consiglio Nazionale delle Ricerche (CNR), Via Previati 1/E, 23900 Lecco, Italy
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Moshawrab M, Adda M, Bouzouane A, Ibrahim H, Raad A. Reviewing Federated Machine Learning and Its Use in Diseases Prediction. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23042112. [PMID: 36850717 PMCID: PMC9958993 DOI: 10.3390/s23042112] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 02/04/2023] [Accepted: 02/09/2023] [Indexed: 05/31/2023]
Abstract
Machine learning (ML) has succeeded in improving our daily routines by enabling automation and improved decision making in a variety of industries such as healthcare, finance, and transportation, resulting in increased efficiency and production. However, the development and widespread use of this technology has been significantly hampered by concerns about data privacy, confidentiality, and sensitivity, particularly in healthcare and finance. The "data hunger" of ML describes how additional data can increase performance and accuracy, which is why this question arises. Federated learning (FL) has emerged as a technology that helps solve the privacy problem by eliminating the need to send data to a primary server and collect it where it is processed and the model is trained. To maintain privacy and improve model performance, FL shares parameters rather than data during training, in contrast to the typical ML practice of sending user data during model development. Although FL is still in its infancy, there are already applications in various industries such as healthcare, finance, transportation, and others. In addition, 32% of companies have implemented or plan to implement federated learning in the next 12-24 months, according to the latest figures from KPMG, which forecasts an increase in investment in this area from USD 107 million in 2020 to USD 538 million in 2025. In this context, this article reviews federated learning, describes it technically, differentiates it from other technologies, and discusses current FL aggregation algorithms. It also discusses the use of FL in the diagnosis of cardiovascular disease, diabetes, and cancer. Finally, the problems hindering progress in this area and future strategies to overcome these limitations are discussed in detail.
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Affiliation(s)
- Mohammad Moshawrab
- Département de Mathématiques, Informatique et Génie, Université du Québec à Rimouski, 300 Allée des Ursulines, Rimouski, QC G5L 3A1, Canada
| | - Mehdi Adda
- Département de Mathématiques, Informatique et Génie, Université du Québec à Rimouski, 300 Allée des Ursulines, Rimouski, QC G5L 3A1, Canada
| | - Abdenour Bouzouane
- Département d’Informatique et de Mathématique, Université du Québec à Chicoutimi, 555 Boulevard de l’Université, Chicoutimi, QC G7H 2B1, Canada
| | - Hussein Ibrahim
- Institut Technologique de Maintenance Industrielle, 175 Rue de la Vérendrye, Sept-Îles, QC G4R 5B7, Canada
| | - Ali Raad
- Faculty of Arts & Sciences, Islamic University of Lebanon, Wardaniyeh P.O. Box 30014, Lebanon
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Moshawrab M, Adda M, Bouzouane A, Ibrahim H, Raad A. Smart Wearables for the Detection of Cardiovascular Diseases: A Systematic Literature Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23020828. [PMID: 36679626 PMCID: PMC9865666 DOI: 10.3390/s23020828] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Revised: 12/27/2022] [Accepted: 01/09/2023] [Indexed: 06/02/2023]
Abstract
Background: The advancement of information and communication technologies and the growing power of artificial intelligence are successfully transforming a number of concepts that are important to our daily lives. Many sectors, including education, healthcare, industry, and others, are benefiting greatly from the use of such resources. The healthcare sector, for example, was an early adopter of smart wearables, which primarily serve as diagnostic tools. In this context, smart wearables have demonstrated their effectiveness in detecting and predicting cardiovascular diseases (CVDs), the leading cause of death worldwide. Objective: In this study, a systematic literature review of smart wearable applications for cardiovascular disease detection and prediction is presented. After conducting the required search, the documents that met the criteria were analyzed to extract key criteria such as the publication year, vital signs recorded, diseases studied, hardware used, smart models used, datasets used, and performance metrics. Methods: This study followed the PRISMA guidelines by searching IEEE, PubMed, and Scopus for publications published between 2010 and 2022. Once records were located, they were reviewed to determine which ones should be included in the analysis. Finally, the analysis was completed, and the relevant data were included in the review along with the relevant articles. Results: As a result of the comprehensive search procedures, 87 papers were deemed relevant for further review. In addition, the results are discussed to evaluate the development and use of smart wearable devices for cardiovascular disease management, and the results demonstrate the high efficiency of such wearable devices. Conclusions: The results clearly show that interest in this topic has increased. Although the results show that smart wearables are quite accurate in detecting, predicting, and even treating cardiovascular disease, further research is needed to improve their use.
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Affiliation(s)
- Mohammad Moshawrab
- Département de Mathématiques, Informatique et Génie, Université du Québec à Rimouski, 300 Allée des Ursulines, Rimouski, QC G5L 3A1, Canada
| | - Mehdi Adda
- Département de Mathématiques, Informatique et Génie, Université du Québec à Rimouski, 300 Allée des Ursulines, Rimouski, QC G5L 3A1, Canada
| | - Abdenour Bouzouane
- Département d’Informatique et de Mathématique, Université du Québec à Chicoutimi, 555 Boulevard de l’Université, Chicoutimi, QC G7H 2B1, Canada
| | - Hussein Ibrahim
- Institut Technologique de Maintenance Industrielle, 175 Rue de la Vérendrye, Sept-Îles, QC G4R 5B7, Canada
| | - Ali Raad
- Faculty of Arts & Sciences, Islamic University of Lebanon, Wardaniyeh P.O. Box 30014, Lebanon
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Bustos D, Cardoso F, Rios M, Vaz M, Guedes J, Torres Costa J, Santos Baptista J, Fernandes RJ. Machine Learning Approach to Model Physical Fatigue during Incremental Exercise among Firefighters. SENSORS (BASEL, SWITZERLAND) 2022; 23:194. [PMID: 36616791 PMCID: PMC9823590 DOI: 10.3390/s23010194] [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: 11/05/2022] [Revised: 12/13/2022] [Accepted: 12/22/2022] [Indexed: 06/17/2023]
Abstract
Physical fatigue is a serious threat to the health and safety of firefighters. Its effects include decreased cognitive abilities and a heightened risk of accidents. Subjective scales and, recently, on-body sensors have been used to monitor physical fatigue among firefighters and safety-sensitive professionals. Considering the capabilities (e.g., noninvasiveness and continuous monitoring) and limitations (e.g., assessed fatiguing tasks and models validation procedures) of current approaches, this study aimed to develop a physical fatigue prediction model combining cardiorespiratory and thermoregulatory measures and machine learning algorithms within a firefighters' sample. Sensory data from heart rate, breathing rate and core temperature were recorded from 24 participants during an incremental running protocol. Various supervised machine learning algorithms were examined using 21 features extracted from the physiological variables and participants' characteristics to estimate four physical fatigue conditions: low, moderate, heavy and severe. Results showed that the XGBoosted Trees algorithm achieved the best outcomes with an average accuracy of 82% and accuracies of 93% and 86% for recognising the low and severe levels. Furthermore, this study evaluated different methods to assess the models' performance, concluding that the group cross-validation method presents the most practical results. Overall, this study highlights the advantages of using multiple physiological measures for enhancing physical fatigue modelling. It proposes a promising health and safety management tool and lays the foundation for future studies in field conditions.
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Affiliation(s)
- Denisse Bustos
- Associated Laboratory for Energy, Transports and Aeronautics, Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal
| | - Filipa Cardoso
- Centre of Research, Education, Innovation and Intervention in Sport, CIFI2D, Faculty of Sport, University of Porto, 4200-450 Porto, Portugal
- Porto Biomechanics Laboratory, Faculty of Sport, University of Porto, 4200-450 Porto, Portugal
| | - Manoel Rios
- Centre of Research, Education, Innovation and Intervention in Sport, CIFI2D, Faculty of Sport, University of Porto, 4200-450 Porto, Portugal
- Porto Biomechanics Laboratory, Faculty of Sport, University of Porto, 4200-450 Porto, Portugal
| | - Mário Vaz
- Associated Laboratory for Energy, Transports and Aeronautics, Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal
- Porto Biomechanics Laboratory, Faculty of Sport, University of Porto, 4200-450 Porto, Portugal
| | - Joana Guedes
- Associated Laboratory for Energy, Transports and Aeronautics, Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal
| | - José Torres Costa
- Associated Laboratory for Energy, Transports and Aeronautics, Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal
| | - João Santos Baptista
- Associated Laboratory for Energy, Transports and Aeronautics, Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal
- Porto Biomechanics Laboratory, Faculty of Sport, University of Porto, 4200-450 Porto, Portugal
| | - Ricardo J. Fernandes
- Centre of Research, Education, Innovation and Intervention in Sport, CIFI2D, Faculty of Sport, University of Porto, 4200-450 Porto, Portugal
- Porto Biomechanics Laboratory, Faculty of Sport, University of Porto, 4200-450 Porto, Portugal
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