Juhani Airaksinen

Verified Contributor4 Publications in Hub

Research Bibliography

ID: end-to-end-sensor-fusion-and-classification-of-atrial-fibrillation-using-deep-neural-networks-and-smartphone-mechanocardiography2022

End-to-end sensor fusion and classification of atrial fibrillation using deep neural networks and smartphone mechanocardiography

This study shows that smartphones can detect atrial fibrillation (AFib) using vibrations from the chest with high accuracy, offering a practical and affordable heart monitoring solution.

ID: multiclass-classifier-based-cardiovascular-condition-detection-using-smartphone-mechanocardiography2018

Multiclass Classifier based Cardiovascular Condition Detection Using Smartphone Mechanocardiography

This study shows that smartphones can detect heart conditions like AFib and heart attacks using built-in sensors and machine learning, offering a promising tool for global heart health monitoring.

ID: comprehensive-analysis-of-cardiogenic-vibrations-for-automated-detection-of-atrial-fibrillation-using-smartphone-mechanocardiograms2018

Comprehensive Analysis of Cardiogenic Vibrations for Automated Detection of Atrial Fibrillation Using Smartphone Mechanocardiograms

This study shows that a smartphone can detect atrial fibrillation (AFib) with high accuracy using chest vibrations, making heart monitoring accessible and easy for everyone without extra devices.

ID: machine-learning-based-classification-of-myocardial-infarction-conditions-using-smartphone-derived-seismo--and-gyrocardiography2018

Machine Learning Based Classification of Myocardial Infarction Conditions Using Smartphone-Derived Seismo- and Gyrocardiography

Researchers used smartphone sensors to track heart changes in heart attack patients before and after treatment, achieving promising accuracy with machine learning methods.