Accelerometer

End-to-end sensor fusion and classification of atrial fibrillation

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

This paper presents a deep learning framework for detecting atrial fibrillation (AFib) by analyzing the heart’s mechanical functioning using smartphone mechanocardiography. The model achieves high accuracy in classifying sinus rhythm, AFib, and Noise categories.

May 2022 · Saeed Mehrang, Mojtaba Jafari Tadi, Timo Knuutila, Jussi Jaakkola, Samuli Jaakkola, Tuomas Kiviniemi, Tuija Vasankari, Juhani Airaksinen, Tero Koivisto, Mikko Pänkäälä
Activity Recognition Framework

An Activity Recognition Framework Deploying the Random Forest Classifier and A Single Optical Heart Rate Monitoring and Triaxial Accelerometer Wrist-Band

This paper investigates a range of daily life activities and uses a random forest classifier to detect them based on wrist motions and optical heart rate. The highest accuracy was achieved with a forest of 64 trees and 13-s signal segments.

February 2018 · Saeed Mehrang, Julia Pietilä, Ilkka Korhonen