Development of a User-Adaptable Human Fall Detection Based on Fall Risk Levels Using Depth Sensor.
作者： Yoosuf Nizam , Mohd Norzali Haji Mohd , M Mahadi Abdul Jamil
DOI: 10.3390/S18072260
关键词: Identification (information) 、 Wearable computer 、 Artificial intelligence 、 Machine learning 、 Fall detection 、 Computer science 、 Fall risk level 、 Fall risk
摘要: Unintentional falls are a major public health concern for many communities, especially with aging populations. There various approaches used to classify human activities fall detection. Related studies have employed wearable, non-invasive sensors, video cameras and depth sensor-based develop such monitoring systems. The proposed approach in this study uses sensor employs unique procedure which identifies the risk levels adapt algorithm different people their physical strength withstand falls. inclusion of level identification, further enhanced improved accuracy experimental results showed promising performance adapting
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