Wang, Ching-Wei, Ahmed, Amr and Hunter, Andrew
(2006)
Vision analysis in detecting abnormal breathing activity in application to diagnosis of obstructive sleep apnoea.
In:
Proceedings of the 28th IEEE EMBS Annual International Conference.
Institute of Electrical and Electronics Engineers, Inc, New York, USA, pp. 4469-4473.
ISBN 1424400333
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| Item Type: | Book Section |
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| Item Status: | Live Archive |
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Abstract
Recognizing abnormal breathing activity from
body movement is a challenging task in machine vision. In this paper, we present a non-intrusive automatic video monitoring technique for detecting abnormal breathing activities and assisting in diagnosis of obstructive sleep apnoea. The proposed technique utilizes infrared video information and avoids imposing geometric or positional constraints on the patient. The technique also deals with fully or partially obscured patients’ body. A continuously updated breathing activity template is built
for distinguishing general body movement from breathing
behavior.
| Additional Information: | Recognizing abnormal breathing activity from
body movement is a challenging task in machine vision. In this paper, we present a non-intrusive automatic video monitoring technique for detecting abnormal breathing activities and assisting in diagnosis of obstructive sleep apnoea. The proposed technique utilizes infrared video information and avoids imposing geometric or positional constraints on the patient. The technique also deals with fully or partially obscured patients’ body. A continuously updated breathing activity template is built
for distinguishing general body movement from breathing
behavior. |
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| Keywords: | breath monitoring, behavior recognition, vision analysis, respiration monitoring |
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| Subjects: | G Mathematical and Computer Sciences > G400 Computer Science |
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| Divisions: | College of Science > School of Computer Science |
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| ID Code: | 114 |
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| Deposited On: | 27 Nov 2006 |
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