Student Health Monitoring
Student Health Monitoring - Related Videos
07:02
Investigating the Potential of Singly Curved Thin Piezoelectric Transducers for Energy Harvesting and Structural Health Monitoring
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2025
Here, we experimentally compare singly curved and straight piezoelectric transducers embedded in reinforced concrete structures for energy harvesting and structural health monitoring by evaluating their open-circuit voltage, power generation, power storage, and damage detection capability. Curved transducers have surpassed the straight ones, offering better performance for real-world applications.
13:44
Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques
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Cited by 6 •
2022
Here, we present a protocol for the behavioral analysis of a project-based learning methodology for health sciences students (20-56 years old). The protocol facilitates the comparison of the participants' performance in e-Learning versus blended-Learning (b-Learning) through a monitoring tool. The results are analyzed using Educational Data Mining and qualitative techniques.
06:58
An Application for Pairing with Wearable Devices to Monitor Personal Health Status
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Cited by 3 •
2022
The present protocol introduces a non-commercial self-developed application for collecting real-time on-site data, including psychological scales, GPS location, heart rate, and blood-oxygen saturation level, as well as the application's operating procedures. An empirical study conducted in Taiwan in 2020 was used as an application example.
15:00
Setup of Consumer Wearable Devices for Exposure and Health Monitoring in Population Studies
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Cited by 6 •
2023
Commercial smartwatches equipped with wearable sensors are increasingly being used in population studies. However, their utility is often constrained by their limited battery duration, memory capacity, and data quality. This report provides examples of cost-effective solutions to real-life technical challenges encountered during studies involving asthmatic children and elderly cardiac patients.
01:25
Microsoft Excel: Student's t-Test
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2025
Student's t-test in Microsoft Excel is a statistical method used to compare the means of two groups to determine if they are significantly different from each other. It's commonly used to evaluate hypotheses, such as testing whether a treatment has an effect compared to a control group. Excel provides built-in functions to perform t-tests, making it accessible for users needing to conduct basic statistical analysis. To conduct a t-test in Excel, use the T.TEST function or the "Data Analysis...
FAQs
The value comes from linking several stages rather than relying on a single reading. A wearable, mobile device, or connected clinical system captures signals such as heart rate, activity, sleep, or temperature; transmission makes those data available for processing; analysis then looks for trends or potential concerns. This engineering chain supports earlier recognition of changes and more informed follow-up.
Measurement accuracy is only one design requirement. A system also needs to be usable enough for students to engage with it, secure enough to protect health information, and accessible across student populations. These requirements can compete: a technically capable design may have limited practical value if it is difficult to use, raises privacy concerns, or creates unequal access.
Trend analysis adds context that an isolated measurement may not provide. By examining health-related signals over time, engineers can focus on changes that may indicate a potential concern rather than treating every reading as equally meaningful. This supports monitoring approaches aimed at detecting changes early, while preserving a role for health interventions rather than treating automated analysis as the intervention itself.
An engineering workflow begins by selecting relevant signals and a collection method, such as a wearable, mobile device, or connected clinical system. The system then captures measurements, transmits them, and processes the resulting data. Designers can examine the output for trends or potential concerns and connect findings with health interventions, program evaluation, or research objectives.
The data can support several levels of work: identifying changes early for student well-being and safety, guiding health interventions, evaluating campus wellness programs, and studying student health in research. The appropriate use depends on the system’s ability to produce useful information while maintaining security, privacy, usability, and equitable access for the student population.
Engineering contributes at multiple layers of the system. Biomedical, electrical, software, and systems engineering each provide relevant perspectives on health measurement, connected devices, data transmission, processing, and coordination among system components. This interdisciplinary approach helps align technical performance with campus use, student needs, and responsible handling of health-related information.