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Ama ieee takeda
 

Ama ieee takeda

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    Ama ieee takeda Ama ieee takeda Presentation Transcript

    • DETECTION OFPATIENT’S SIGN OFFALLSMaki Takeda, Nagisa Sasaki, Kayo YoshimotoTakeshi Ando, Sachiko Shimizu, Kenji Yamadaand Yuko OhnoDepartment of Health Sciences,Graduate School of Medicine, Osaka University CAUTION
    • BACKGROUND Nurse No problem. Nurse stationInpatient It is difficult to predict falls.
    • PROPOSED SYSTEM Motion detection Motion prediction Video sensor Catch Roll overthe sign or Patient’s room Wake up Bayesian approach
    • METHOD & EXPERIMENT  Center of gravity of head  Bayesian approach from original image  a x, y   Lx, y a  a  : prior distribution , : posterior distribution , :likelihood  Culculating velocity of : x-coordinate of center of center of gravity of head gravity of head : y-coordinate of center ofVelocity [pixel/frame] 10 25frame ? gravity of head 5 : regression coefficient 0 Estimate gradient by 0 20 40 60 80 Frame maximum likelihood estimate Motion of posterior distribution detection
    • EXPERIMENTAL RESULTS Subjects performed 3 situations on bed ( wake-up, right or left roll-over) 150 X-axial displacement [pixel] 120 起き上がり Wake-up 90 右 Right roll-over 60 左 Left roll-over 30 0 0 0.1 0.2 0.3 0.4 Estimated gradient It is possible to classify wake-up or roll-over by estimated gradient and x-axial displacement of center of gravity.