Advancing Sports Cardiology: Integrating Artificial Intelligence with Wearable Devices for Cardiovascular Health Management

ACS Applied Materials & Interfaces, Год журнала: 2025, Номер unknown

Опубликована: Март 12, 2025

Sports cardiology focuses on athletes' cardiovascular health, yet sudden cardiac death remains a significant concern despite preventative measures. Prolonged physical activity leads to notable adaptations, known as the athlete's heart, which can resemble certain pathological conditions, complicating accurate diagnoses and potentially leading serious consequences such unnecessary exclusion from sports or missed treatment opportunities. Wearable devices, including smartwatches smart glasses, have become prevalent for monitoring health metrics, offering potential clinical applications cardiologists. These gadgets are capable of spotting exercise-induced arrhythmias, uncovering hidden heart problems, crucial information training recovery, minimize exercise-related incidents enhance care. However, concerns about data accuracy actionable value obtained persist. A major challenge lies in integration artificial intelligence with wearables, research gaps remain regarding their ability provide real-time, reliable, clinically relevant insights. Combining wearable devices improve how is managed used cardiology. Artificial intelligence, particularly machine learning, classify, predict, draw inferences collected by revolutionizing patient usage. Despite intelligence's proven effectiveness managing chronic limited its application cardiology, creates critical gap that needs be addressed. This review examines commercially available wearables exploring integrated into technology advance field.

Язык: Английский

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Accurate Blood Glucose Level Monitoring Using Microwave Imaging

Measurement Science and Technology, Год журнала: 2024, Номер 35(7), С. 076120 - 076120

Опубликована: Апрель 3, 2024

Abstract Painless and non-invasive detection techniques are needed to replace finger-prick blood collection for people with diabetes. A first-of-its-kind, noninvasive, continuous glucose level (BGL) method based on microwave imaging is introduced in this paper. This avoids the complex task of frequency choice design electromagnetic sensors. radar-based technology combined an improved very-deep super-resolution (VDSR-BL) presented obtain high-resolution (HR) images. After image reconstruction by VDSR-BL, peak signal-to-noise ratio structural similarity index HR images reach 35.4461 dB 0.9761, respectively. Then, ensemble learning strategy support vector regression random forest algorithms proposed identify BGL estimation. The developed system has been verified medium under tests different solutions. final results a root mean squared error 0.1394 mg ml −1 absolute relative difference 8.02%, which show good accuracy clinical acceptance. Meanwhile, we also conducted human trials. high correlation coefficient ( R ) 0.9254 was achieved between invasive BGL. Together, these that offers promising new approach noninvasive monitoring.

Язык: Английский

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Humidity sensitive memristor based on Ni–Al layered double hydroxides

Materials Today Advances, Год журнала: 2024, Номер 23, С. 100515 - 100515

Опубликована: Июль 5, 2024

Язык: Английский

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Integrating motion sensors based on deep neural networks into training and monitoring systems using a fuzzy comprehensive evaluation method

Computers & Electrical Engineering, Год журнала: 2024, Номер 122, С. 109890 - 109890

Опубликована: Дек. 5, 2024

Язык: Английский

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