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Doppler radar remote sensing of torso kinematics can provide an indirect measure of cardiopulmonary function. Motion at the human body surface due to heart and lung activity has been successfully used to characterize such measures as respiratory rate and depth, obstructive sleep apnea, and even the identity of an individual subject. For a sedentary subject, Doppler radar can track the periodic motion of the portion of the body moving as a result of the respiratory cycle as distinct from other extraneous motions that may occur, to provide a spatial temporal displacement pattern that can be combined with a mathematical model to indirectly assess quantities such as tidal volume, and paradoxical breathing. Furthermore, it has been demonstrated that even healthy respiratory function results in distinct motion patterns between individuals that vary as a function of relative time and depth measures over the body surface during the inhalation/exhalation cycle. Potentially, the biomechanics that results in different measurements between individuals can be further exploited to recognize pathology related to lung ventilation heterogeneity and other respiratory diagnostics.
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Parametric Classification of Recoverable Radar-Assessed Respiratory Rate Data
A static algorithm-based method is described here to differentiate between recoverable sedentary respiratory rate data extraneous motion segments measured using Doppler radar. Extraneous motion such as locomotion and fidgeting can cause drastic changes in dc offset and SNR of the received signal. Such extraneous data may not be excluded and can lead to an erroneous assessment of the respiration rate. In some cases, however, moderate distinct extraneous motion does not completely occlude the measurement of respiratory torso motion, allowing for respiration rate recovery. This work focuses on the accurate classification of data which is suitable for respiration rate analysis in the presence of locomotion and small extraneous movements. The proposed algorithm has been demonstrated to be accurate for classifying data with recoverable respiratory rates for 2 subjects and 3 types of fidgets with 99.4% accuracy on average.
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- PAR ID:
- 10547789
- Publisher / Repository:
- IEEE
- Date Published:
- ISBN:
- 979-8-3503-4045-7
- Page Range / eLocation ID:
- 109 to 111
- Subject(s) / Keyword(s):
- Sedentary Non-sedentary locomotion fidgets
- Format(s):
- Medium: X
- Location:
- San Antonio, TX, USA
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/RWS56914.2024.10438609
