R.C.Kokhila 1, K.Ramamoorthy 2
1 PG Scholar, Department of Electronics and Communication Engineering, PSNA college of Engineering and Technology, Dindigul -624619, India.
2 Assistant Professor, Department of Electronics and Communication Engineering, PSNA college of Engineering and Technology, Dindigul -624619, India.
1 PG Scholar, Department of Electronics and Communication Engineering, PSNA college of Engineering and Technology, Dindigul -624619, India.
2 Assistant Professor, Department of Electronics and Communication Engineering, PSNA college of Engineering and Technology, Dindigul -624619, India.
This paper presents a new
real-time automated infrared video monitoring technique for detection of
breathing anomalies, and its application in the diagnosis of obstructive sleep
apnea. We introduce a novel motion model to detect subtle, cyclical breathing
signals from video, a new unsupervised self-adaptive breathing template to
learn individuals' normal breathing patterns online, and a robust action
classification method to recognize abnormal breathing activities and limb
movements. We have presented a novel approach to detect breathing signals and
to recognize abnormal breathing activity from IR video, and have analyzed the
method in identification of episodes of Obstructive Sleep Apnea. The technique
runs in real time, is robust to occlusion by a standard hospital bed cover or
sheet, variances in patterns of breathing and subject appearance, and
substantial changes of camera view relative to the subject. This preliminary
study indicates that it has good performance on both the simulated and clinical
data. The algorithm uses a novel persistence luminance model that helps to
reinforce subtle breathing movements, an activity level to segment the video,
and a novel activity template to classify motion events 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 behaviour.
