Accuracy of a Smartwatch to Assess Heart Rate Monitoring and Atrial Fibrillation in Stroke Patients

Meza, Claudia; Juega, Jesús; Francisco, Jaume; Santos‐Ortega, Alba; Durán, Laura Buzón; Rodríguez, María Susana; Alvarez-Sabin, José; Seró, Laia; Ustrell, Xavier; Bashir, Saima; Serena, Joaquı́n; Silva, Yolanda; Molina, Carlos A.; Pagola, Jorge

doi:10.3390/s23104632

Cited by 6 publications

(2 citation statements)

References 24 publications

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“…Smartwatches use the PPG signal to calculate heart rate by analyzing the period between heartbeats. Smartwatches display real-time heart rate data [33]. Algorithms analyze the raw PPG signal and derive HRV data in smartwatches.…”

Section: Smartwatches and Stress Managementmentioning

confidence: 99%

The Future of Stress Management: Integration of Smartwatches and HRV Technology

Jerath,

Syam,

Ahmed

2023

Sensors

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In the modern world, stress has become a pervasive concern that affects individuals’ physical and mental well-being. To address this issue, many wearable devices have emerged as potential tools for stress detection and management by measuring heart rate, heart rate variability (HRV), and various metrics related to it. This literature review aims to provide a comprehensive analysis of existing research on HRV tracking and biofeedback using smartwatches pairing with reliable 3rd party mobile apps like Elite HRV, Welltory, and HRV4Training specifically designed for stress detection and management. We apply various algorithms and methodologies employed for HRV analysis and stress detection including time-domain, frequency-domain, and non-linear analysis techniques. Prominent smartwatches, such as Apple Watch, Garmin, Fitbit, Polar, and Samsung Galaxy Watch, are evaluated based on their HRV measurement accuracy, data quality, sensor technology, and integration with stress management features. We describe the efficacy of smartwatches in providing real-time stress feedback, personalized stress management interventions, and promoting overall well-being. To assist researchers, doctors, and developers with using smartwatch technology to address stress and promote holistic well-being, we discuss the data’s advantages and limitations, future developments, and the significance of user-centered design and personalized interventions.

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Section: Smartwatches and Stress Managementmentioning

confidence: 99%

The Future of Stress Management: Integration of Smartwatches and HRV Technology

Jerath,

Syam,

Ahmed

2023

Sensors

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“…In article [ 12 ], the gyroscope and accelerometer are utilized with the Arduino microcontroller, strapped to the torso, and transmitted sensor signals for heart rate measurements. Smartwatches are the most common heart rate monitoring tools [ 13 , 14 , 15 , 16 ]. They use photoplethysmography (PPG), a non-invasive optical technique to measure blood volume changes in the microvascular bed of tissue.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Machine Learning Algorithms for Heartbeat Detection Based on Accelerometric Signals Produced by a Smart Bed

Hoang,

Matrella,

Ciampolini

2024

Sensors

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This work aims to compare the performance of Machine Learning (ML) and Deep Learning (DL) algorithms in detecting users’ heartbeats on a smart bed. Targeting non-intrusive, continuous heart monitoring during sleep time, the smart bed is equipped with a 3D solid-state accelerometer. Acceleration signals are processed through an STM 32-bit microcontroller board and transmitted to a PC for recording. A photoplethysmographic sensor is simultaneously checked for ground truth reference. A dataset has been built, by acquiring measures in a real-world set-up: 10 participants were involved, resulting in 120 min of acceleration traces which were utilized to train and evaluate various Artificial Intelligence (AI) algorithms. The experimental analysis utilizes K-fold cross-validation to ensure robust model testing across different subsets of the dataset. Various ML and DL algorithms are compared, each being trained and tested using the collected data. The Random Forest algorithm exhibited the highest accuracy among all compared models. While it requires longer training time compared to some ML models such as Naïve Bayes, Linear Discrimination Analysis, and K-Nearest Neighbour Classification, it keeps substantially faster than Support Vector Machine and Deep Learning models. The Random Forest model demonstrated robust performance metrics, including recall, precision, F1-scores, macro average, weighted average, and overall accuracy well above 90%. The study highlights the better performance of the Random Forest algorithm for the specific use case, achieving superior accuracy and performance metrics in detecting user heartbeats in comparison to other ML and DL models tested. The drawback of longer training times is not too relevant in the long-term monitoring target scenario, so the Random Forest model stands out as a viable solution for real-time ballistocardiographic heartbeat detection, showcasing potential for healthcare and wellness monitoring applications.

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Noninvasive Hemodynamic Monitors, What Is New and Old

Siemer,

Friedman

et al. 2024

Advances in Anesthesia

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Accuracy of a Smartwatch to Assess Heart Rate Monitoring and Atrial Fibrillation in Stroke Patients

Cited by 6 publications

References 24 publications

The Future of Stress Management: Integration of Smartwatches and HRV Technology

The Future of Stress Management: Integration of Smartwatches and HRV Technology

Comparison of Machine Learning Algorithms for Heartbeat Detection Based on Accelerometric Signals Produced by a Smart Bed

Noninvasive Hemodynamic Monitors, What Is New and Old

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