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Title: Activity Segmentation Using Wearable Sensors for DVT/PE Risk Detection

Using a wearable electromyography (EMG) and an accelerometer sensor, classification of subject activity state (i.e., 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 (i.e., standing vs. 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%.
Authors:
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Award ID's:
1816387
Publication Date:
NSF-PAR ID:
10118762
Journal Name:
2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC)
Page Range or eLocation-ID:
477 to 483
Sponsoring Org:
National Science Foundation