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• W3117607589 abstract "Assistive care related products are increasingly in demand with the recentdevelopments in health sector associated technologies. There are several studiesconcerned in improving and eliminating barriers in providing quality health careservices to all people, especially elderly who live alone and those who cannot movefrom their home for various reasons such as disable, overweight. Among them, humanfall detection systems play an important role in our daily life, because fall is the mainobstacle for elderly people to live independently and it is also a major health concerndue to aging population. The three basic approaches used to develop human falldetection systems include some sort of wearable devices, ambient based devices ornon-invasive vision based devices using live cameras. Most of such systems are eitherbased on wearable or ambient sensor which is very often rejected by users due to thehigh false alarm and difficulties in carrying them during their daily life activities. Thus,this study proposes a non-invasive human fall detection system based on the height,velocity, statistical analysis, fall risk factors and position of the subject using depthinformation from Microsoft Kinect sensor. Classification of human fall from otheractivities of daily life is accomplished using height and velocity of the subjectextracted from the depth information after considering the fall risk level of the user.Acceleration and activity detection are also employed if velocity and height fail toclassify the activity. Finally position of the subject is identified for fall confirmationor statistical analysis is conducted to verify the fall event. From the experimentalresults, the proposed system was able to achieve an average accuracy of 98.3% withsensitivity of 100% and specificity of 97.7%. The proposed system accuratelydistinguished all the fall events from other activities of daily life." @default.
• W3117607589 created "2021-01-05" @default.
• W3117607589 creator A5024341550 @default.
• W3117607589 date "2018-05-01" @default.
• W3117607589 modified "2023-09-26" @default.
• W3117607589 title "Development of a human fall detection system based on depth maps" @default.
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