Sleep Posture Recognition With a Dual-Frequency Cardiopulmonary Doppler Radar
| Autor: | Victor Lubecke, Shekh M. M. Islam, Olga Boric-Lubecke, John E. Kiriazi |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Radar cross-section
Supine position General Computer Science Computer science radar remote sensing Doppler radar 02 engineering and technology 01 natural sciences law.invention radar cross-section sleep posture law 0202 electrical engineering electronic engineering information engineering medicine General Materials Science Computer vision Radar Box plot business.industry 010401 analytical chemistry General Engineering Sleep apnea 020206 networking & telecommunications Torso medicine.disease radar signal processing 0104 chemical sciences medicine.anatomical_structure Sleep (system call) Artificial intelligence lcsh:Electrical engineering. Electronics. Nuclear engineering business lcsh:TK1-9971 |
| Zdroj: | IEEE Access, Vol 9, Pp 36181-36194 (2021) |
| ISSN: | 2169-3536 |
| Popis: | While Doppler radar can be used to measure cardiopulmonary vital signs during sleep, meaningful diagnostic assessments are often subject to knowledge of a subject’s changing sleep posture. The torso Effective Radar Cross Section (ERCS) and displacement magnitude were studied for 20 human subjects in three imitated sleep posture categories using a dual-frequency Doppler radar system in an exploratory examination of the feasibility of using radar to recognize body orientation. Box plot statistical analyses were performed for comparative assessment of ratio variations in ERCS and respiration depth for three different imitated sleep postures. The observed statistical trends and correlations were applied to a physical model to develop posture decision algorithms with initial supine posture data used as a reference. A single-frequency algorithm tracked postures without error for 90% of the subjects using 2.4 GHz data, and 80% using 5.8 GHz data. As accuracy limitations were complementary, a dual-frequency algorithm was developed which recognized postures without error for 100% of the subjects. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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