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A real-time fall detection system is proposed to distinguish various falls during daily activities. Falls are detected in two steps: first a hierarchical algorithm is used to classify the motion and motionless postures such as lying, sit-tilted, sit-upright, standing and walking; it then analyzes whether the current lying or sit-tilted postures are normal or abnormal, based on posture transition and...
A robust method of fall and motionless detection is presented. The approach is able to detect falls and motionless periods (standing, sitting, and lying) using only one belt-worn kinematic sensor. The fall detection algorithm analyses the phase changes of vertical acceleration in relation to gravity and impact force using kinematic variables. A phase angle value was used as a threshold to distinguish...
A robust method of fall and motionless detection is presented. The approach is able to detect falls and motionless periods (standing, sitting, and lying) using only one belt-worn kinematic sensor. The fall detection algorithm analyses the phase changes of vertical acceleration in relation to gravity and impact force using kinematic variables. A phase angle value was used as a threshold to distinguish...
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