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Article · Retos · October 29, 2024

Real-Time precision prehospital stroke diagnosis in weightlifting athletes using cutting-edge non-invasive sensors

Azhar Tursynova, Bolganay Kaldarova

Azhar Tursynova
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Abstract

This research paper presents an investigation into the efficacy of a novel diagnostic technology designed for the real-time monitoring of weightlifting athletes, focusing on precision, time efficiency, and user convenience compared to traditional diagnostic systems. The study introduces an advanced non-invasive sensor system, integrated into a cohesive Internet of Medical Things (IoMT) framework, which facilitates the immediate and accurate assessment of athletes' health parameters. To empirically test the benefits of this new technology, a pedagogical experiment was conducted involving two distinct groups: an experimental group that utilized the proposed technology for medical checkups, and a control group that continued with traditional diagnostic methods. Each group consisted of 30 athletes, and the outcomes were measured across three dimensions: the precision of diagnostic results, the time expended for medical checkups, and the user-reported convenience of the equipment. The findings indicate that the proposed technology not only significantly enhances the precision of health diagnostics but also reduces the time required for medical examinations, thereby increasing overall efficiency. Additionally, the higher convenience scores reported by the experimental group suggest improved user satisfaction and usability. These results demonstrate the potential of the proposed diagnostic system to transform athlete health monitoring by providing more accurate, efficient, and user-friendly medical assessments, suggesting a significant step forward in the application of advanced technologies in sports medicine.

Non-Invasive Vital Sign MonitoringArtificial Intelligence in HealthcareInternet of Things and AIweightliftersnon-invasive sensorsInternet of Medical Things

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