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Accepted (AM)Open Access (License Unspecified), Open
Conference proceeding
Activity Segmentation Using Wearable Sensors for DVT/PE Risk Detection
2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC), v 2, pp 477-483
Jul 2019
PMID: 33594351
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
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%.
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Details
- Title
- Activity Segmentation Using Wearable Sensors for DVT/PE Risk Detection
- Creators
- Austin Gentry - Drexel UniversityWilliam Mongan - Drexel UniversityBrent Lee - Drexel UniversityOwen Montgomery - Drexel UniversityKapil Dandekar - Drexel University
- Publication Details
- 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC), v 2, pp 477-483
- Publisher
- IEEE
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Electrical and Computer Engineering; Epidemiology and Biostatistics; College of Nursing and Health Professions
- Web of Science ID
- WOS:000538781300079
- Scopus ID
- 2-s2.0-85072652514
- Other Identifier
- 991019168100304721
UN Sustainable Development Goals (SDGs)
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Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Computer Science, Interdisciplinary Applications
- Computer Science, Software Engineering