Machine learning algorithms for posture identification of Obstructive Sleep Apnea patients
Channel: A-WEAR
Date: 2020-11-26

In this research, we used data of two separate experiments from thirteen healthy subjects in different sleeping postures using two commercially available Internet of Thing (IoT) based pressure mats. On this data, we employed machine learning based supervised learning algorithms for posture identification. This monitoring system may help sleep apnea patients and caregivers  be alerted of improper postures in a timely manner and help identify the sleeping style of each patient.

This project has received funding from the European Union’s Horizon 2020 (H2020) Marie Sklodowska-Curie Innovative Training Networks H2020-MSCA-ITN-2018 call, under the Grant Agreement no 813278.

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