Activity Segmentation Using Wearable Sensors for DVT/PE Risk Detection
| Autor: | Kapil R. Dandekar, Austin Gentry, Owen Montgomery, Brent Lee, William M. Mongan |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
0209 industrial biotechnology
medicine.medical_specialty medicine.diagnostic_test business.industry Deep vein Wearable computer 02 engineering and technology Electromyography 030204 cardiovascular system & hematology Accelerometer Sitting Article 03 medical and health sciences Statistical classification 020901 industrial engineering & automation 0302 clinical medicine Physical medicine and rehabilitation medicine.anatomical_structure Medicine Segmentation Ankle business |
| Zdroj: | Proc COMPSAC COMPSAC (2) |
| Popis: | 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%. |
| Databáze: | OpenAIRE |
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