Activity Segmentation Using Wearable Sensors for DVT/PE Risk Detection

Overview

Journal Proc COMPSAC

Date 2021 Feb 17

PMID 33594351

Citations 2

Authors

Austin Gentry

William M Mongan

Brent Lee

Owen Montgomery

Kapil R Dandekar

Affiliations

Soon will be listed here.

Abstract

Using a wearable electromyography (EMG) and an accelerometer sensor, classification of subject activity state (., walking, sitting, standing, or ankle circles) enables detection of prolonged "negative" activity states in which the calf muscles do not facilitate blood flow return via the deep veins of the leg. By employing machine learning classification on a multi-sensor wearable device, we are able to classify human subject state between "positive" and "negative" activities, and among each activity state, with greater than 95% accuracy. Some negative activity states cannot be accurately discriminated due to their similar presentation from an accelerometer (., standing . sitting); however, it is desirable to separate these states to better inform the risk of developing a Deep Vein Thrombosis (DVT). Augmentation with a wearable EMG sensor improves separability of these activities by 30%.

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