Machine Learning in Wearable Healthcare Devices
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Cited by 3 publications
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Implementing Augmented Reality and Virtual Reality for authentic healthcare education
Augmented reality (AR) and virtual reality (VR) technologies have demonstrated immense potential to transform fields like education and healthcare through immersive and interactive virtual environments (Bower et al., 2014; Dhar et al., 2023; Moro et al., 2021)). However, high costs of proprietary headsets and content platforms have inhibited widespread adoption of these technologies in resource-constrained contexts, especially in developing countries (Karre et al., 2019). Augmented reality (AR) and virtual reality (VR) have the potential to transform how we approach education and healthcare, enhancing access and outcomes especially in developing countries. AR/VR furthers United Nations (UN) Sustainable Development Goals (SDGs) 3 and 4 through inclusive, equitable education and healthcare (United Nations, 2016). VR can simulate immersive learning environments, providing hands-on medical training to healthcare workers in regions with limited resources. By using VR for anatomy and surgery education, healthcare professionals can gain experience without risk to patients. This improves local healthcare capacity and retention of health workers in remote areas. Similarly, AR and VR can enable experiential learning for students without access to labs or materials (Sinou et al., 2023). This facilitates authentic learning for financially or geographically constrained students (van der Meer et al., 2023). AR/VR health interventions can also improve patient diagnosis and care (Sureja et al., 2023). AR glasses for doctors could display patient vitals or past records during examinations to improve diagnostic capabilities. Remote consultations can connect rural healthcare workers with urban specialists via AR assistive tools during complex treatments. AR/VR distraction therapy has also proven effective during painful procedures for children and the elderly (Vaillant-Ciszewicz et al., 2022). Such solutions enhance community health literacy and comfort with medical services, a key challenge in developing contexts. This presentation proposes a practical methodology for opportunities to expand access to AR/VR healthcare and education tools in low-resource settings through three pathways - utilising low-cost VR headsets, employing inclusive user interface design, and using participatory methodologies during content development. The Educational Design Research (EDR) methodology will guide the project through four main phases (McKenney and Reeves, 2020; Kartoğlu et al., 2020): Analysis and Exploration Phase Conduct a literature review on AR/VR adoption in healthcare education. Engage stakeholders (educators, students, industry partners) through focus groups and interviews. Analyze existing curricula, learning objectives, and assessment practices in healthcare education programs across Australasia. Design and Development Phase Develop design principles and guidelines for creating effective AR/VR experiences in healthcare education. Collaborate with interdisciplinary teams to design and prototype AR/VR experiences aligned with learning objectives and assessment practices. Conduct iterative cycles of prototyping, testing, and refinement with stakeholder feedback. Implementation and Evaluation Phase Implement the developed AR/VR experiences in selected healthcare education programs across Australasia. Evaluate the effectiveness through mixed methods, including quantitative measures of learning outcomes, engagement, and skill development, as well as qualitative analysis of user experiences. Conduct formative evaluations for improvement and refinement. Reflection and Dissemination Phase Analyze and synthesize findings from the implementation and evaluation phases. Refine the design principles and guidelines based on research findings. Develop a comprehensive framework and guidelines for effective AR/VR implementation in healthcare education across Australasia. Disseminate research findings, framework, and guidelines through publications, conferences, workshops, and online resources. The project will apply the principles of EDR, such as interdisciplinary collaboration, contextual adaptation, and iterative refinement, to develop a robust and contextualized solution for AR/VR adoption in healthcare education programs across Australasia. References Bower, M., Howe, C., McCredie, N., Robinson, A., & Grover, D. (2014). Augmented Reality in education – cases, places and potentials. Educational Media International, 51(1), 1–15. https://doi.org/10.1080/09523987.2014.889400 Dhar, E., Upadhyay, U., Huang, Y., Uddin, M., Manias, G., Kyriazis, D., Wajid, U., AlShawaf, H., & Syed Abdul, S. (2023). A scoping review to assess the effects of virtual reality in medical education and clinical care. DIGITAL HEALTH, 9, 20552076231158022. https://doi.org/10.1177/20552076231158022 Kartoğlu, Ü., Siagian, R. C., & Reeves, T. C. (2020). Creating a "Good Clinical Practices Inspection" Authentic Online Learning Environment through Educational Design Research. TechTrends : for leaders in education & training, 1-12. https://doi.org/10.1007/s11528-020-00509-0 Karre, S. A., Mathur, N., & Reddy Y. R. (2019). Usability evaluation of VR products in industry. https://doi.org/10.1145/3297280.3297462 McKenney, S., & Reeves, T. C. (2020). Educational design research: Portraying, conducting, and enhancing productive scholarship. Medical Education, 55(1), 82–92. https://doi.org/10.1111/medu.14280 Moro, C., Birt, J., Stromberga, Z., Phelps, C., Clark, J., Glasziou, P., & Scott, A. M. (2021). Virtual and Augmented Reality Enhancements to Medical and Science Student Physiology and Anatomy Test Performance: A Systematic Review and Meta-Analysis. Anatomical sciences education, 14(3), 368-376. https://doi.org/10.1002/ase.2049 Sinou, N., Sinou, N., & Filippou, D. (2023). Virtual Reality and Augmented Reality in Anatomy Education During COVID-19 Pandemic. CUREUS JOURNAL OF MEDICAL SCIENCE, 15(2). https://doi.org/10.7759/cureus.35170 Sureja, N., Mehta, K., Shah, V., & Patel, G. (2023). Machine Learning in Wearable Healthcare Devices. In Machine Learning for Advanced Functional Materials (pp. 281-303). Springer Nature. https://doi.org/10.1007/978-981-99-0393-1_13 United Nations. (2016). Transforming our world: The 2030 agenda for sustainable development. UN Publishing. https://www.un.org/sustainabledevelopment/ Vaillant-Ciszewicz, A. J., Quin, C., Michel, E., Sacco, G., & Guerin, O. (2022). Customised virtual reality (VR) on mood disorders in nursing homes and long term care unit: A case study on a resident with moderate cognitive impairment [Article]. Annales Medico-Psychologiques. https://doi.org/10.1016/j.amp.2022.10.018 van der Meer, N., van der Werf, V., Brinkman, W. P., & Specht, M. (2023). Virtual reality and collaborative learning: a systematic literature review. Frontiers in Virtual Reality, 4, Article 1159905. https://doi.org/10.3389/frvir.2023.1159905
Implementing Augmented Reality and Virtual Reality for authentic healthcare education
Augmented reality (AR) and virtual reality (VR) technologies have demonstrated immense potential to transform fields like education and healthcare through immersive and interactive virtual environments (Bower et al., 2014; Dhar et al., 2023; Moro et al., 2021)). However, high costs of proprietary headsets and content platforms have inhibited widespread adoption of these technologies in resource-constrained contexts, especially in developing countries (Karre et al., 2019). Augmented reality (AR) and virtual reality (VR) have the potential to transform how we approach education and healthcare, enhancing access and outcomes especially in developing countries. AR/VR furthers United Nations (UN) Sustainable Development Goals (SDGs) 3 and 4 through inclusive, equitable education and healthcare (United Nations, 2016). VR can simulate immersive learning environments, providing hands-on medical training to healthcare workers in regions with limited resources. By using VR for anatomy and surgery education, healthcare professionals can gain experience without risk to patients. This improves local healthcare capacity and retention of health workers in remote areas. Similarly, AR and VR can enable experiential learning for students without access to labs or materials (Sinou et al., 2023). This facilitates authentic learning for financially or geographically constrained students (van der Meer et al., 2023). AR/VR health interventions can also improve patient diagnosis and care (Sureja et al., 2023). AR glasses for doctors could display patient vitals or past records during examinations to improve diagnostic capabilities. Remote consultations can connect rural healthcare workers with urban specialists via AR assistive tools during complex treatments. AR/VR distraction therapy has also proven effective during painful procedures for children and the elderly (Vaillant-Ciszewicz et al., 2022). Such solutions enhance community health literacy and comfort with medical services, a key challenge in developing contexts. This presentation proposes a practical methodology for opportunities to expand access to AR/VR healthcare and education tools in low-resource settings through three pathways - utilising low-cost VR headsets, employing inclusive user interface design, and using participatory methodologies during content development. The Educational Design Research (EDR) methodology will guide the project through four main phases (McKenney and Reeves, 2020; Kartoğlu et al., 2020): Analysis and Exploration Phase Conduct a literature review on AR/VR adoption in healthcare education. Engage stakeholders (educators, students, industry partners) through focus groups and interviews. Analyze existing curricula, learning objectives, and assessment practices in healthcare education programs across Australasia. Design and Development Phase Develop design principles and guidelines for creating effective AR/VR experiences in healthcare education. Collaborate with interdisciplinary teams to design and prototype AR/VR experiences aligned with learning objectives and assessment practices. Conduct iterative cycles of prototyping, testing, and refinement with stakeholder feedback. Implementation and Evaluation Phase Implement the developed AR/VR experiences in selected healthcare education programs across Australasia. Evaluate the effectiveness through mixed methods, including quantitative measures of learning outcomes, engagement, and skill development, as well as qualitative analysis of user experiences. Conduct formative evaluations for improvement and refinement. Reflection and Dissemination Phase Analyze and synthesize findings from the implementation and evaluation phases. Refine the design principles and guidelines based on research findings. Develop a comprehensive framework and guidelines for effective AR/VR implementation in healthcare education across Australasia. Disseminate research findings, framework, and guidelines through publications, conferences, workshops, and online resources. The project will apply the principles of EDR, such as interdisciplinary collaboration, contextual adaptation, and iterative refinement, to develop a robust and contextualized solution for AR/VR adoption in healthcare education programs across Australasia. References Bower, M., Howe, C., McCredie, N., Robinson, A., & Grover, D. (2014). Augmented Reality in education – cases, places and potentials. Educational Media International, 51(1), 1–15. https://doi.org/10.1080/09523987.2014.889400 Dhar, E., Upadhyay, U., Huang, Y., Uddin, M., Manias, G., Kyriazis, D., Wajid, U., AlShawaf, H., & Syed Abdul, S. (2023). A scoping review to assess the effects of virtual reality in medical education and clinical care. DIGITAL HEALTH, 9, 20552076231158022. https://doi.org/10.1177/20552076231158022 Kartoğlu, Ü., Siagian, R. C., & Reeves, T. C. (2020). Creating a "Good Clinical Practices Inspection" Authentic Online Learning Environment through Educational Design Research. TechTrends : for leaders in education & training, 1-12. https://doi.org/10.1007/s11528-020-00509-0 Karre, S. A., Mathur, N., & Reddy Y. R. (2019). Usability evaluation of VR products in industry. https://doi.org/10.1145/3297280.3297462 McKenney, S., & Reeves, T. C. (2020). Educational design research: Portraying, conducting, and enhancing productive scholarship. Medical Education, 55(1), 82–92. https://doi.org/10.1111/medu.14280 Moro, C., Birt, J., Stromberga, Z., Phelps, C., Clark, J., Glasziou, P., & Scott, A. M. (2021). Virtual and Augmented Reality Enhancements to Medical and Science Student Physiology and Anatomy Test Performance: A Systematic Review and Meta-Analysis. Anatomical sciences education, 14(3), 368-376. https://doi.org/10.1002/ase.2049 Sinou, N., Sinou, N., & Filippou, D. (2023). Virtual Reality and Augmented Reality in Anatomy Education During COVID-19 Pandemic. CUREUS JOURNAL OF MEDICAL SCIENCE, 15(2). https://doi.org/10.7759/cureus.35170 Sureja, N., Mehta, K., Shah, V., & Patel, G. (2023). Machine Learning in Wearable Healthcare Devices. In Machine Learning for Advanced Functional Materials (pp. 281-303). Springer Nature. https://doi.org/10.1007/978-981-99-0393-1_13 United Nations. (2016). Transforming our world: The 2030 agenda for sustainable development. UN Publishing. https://www.un.org/sustainabledevelopment/ Vaillant-Ciszewicz, A. J., Quin, C., Michel, E., Sacco, G., & Guerin, O. (2022). Customised virtual reality (VR) on mood disorders in nursing homes and long term care unit: A case study on a resident with moderate cognitive impairment [Article]. Annales Medico-Psychologiques. https://doi.org/10.1016/j.amp.2022.10.018 van der Meer, N., van der Werf, V., Brinkman, W. P., & Specht, M. (2023). Virtual reality and collaborative learning: a systematic literature review. Frontiers in Virtual Reality, 4, Article 1159905. https://doi.org/10.3389/frvir.2023.1159905
Real-World Applications of Data Analytics, Big Data, and Machine Learning
No abstract
An intelligent wearable embedded architecture for stress detection and psychological behavior monitoring using heart rate variability
This study quantifies individual stress levels through real-time analysis of wearable sensor data. An embedded setup utilizes artificial neural networks to analyze R-R intervals and Heart Rate Variability (HRV). Emotion recognition of happiness, sadness, surprise, fear, and anger is explored using seven normalized HRV features. Statistical analysis and classification with a neural network model are performed on approximately 20,700 segments,with participants within the age ranged from 23 to 40, mixed gender, and normal health status, along with other pertinent demographics included. Findings show stress observation’s potential for mental well-being and early detection of stress-related disorders. Three classification algorithms (LVQ, BPN, CART) are evaluated, comparing ECG signal correlation features with traditional ones. BPN achieves the highest emotional recognition accuracy, surpassing LVQ by 5.9% – 8.5% and CART by 2% – 6.5%. Maximum accuracy is 82.35% for LVQ and 97.77% for BPN, but does not exceed 95%. Using only 72 feature sets yields the highest accuracy, surpassing S1 by 17.9% – 20.5% and combined S1/S2 by 11% – 12.7%. ECG signal correlation features outperform traditional features, potentially increasing emotion recognition accuracy by 25%. This study contributes to stress quantification and emotion recognition, promoting mental well-being and early stress disorder detection. The proposed embedded setup and analysis framework offer real-time monitoring and assessment of stress levels, enhancing health and wellness.
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