.png){kind=icon}
返回
A machine learning approach for non-invasive fall detection using Kinect
.png){kind=icon}
PRE
AI
DOI：10.1007/s11042-022-12113-w.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Human falls seldom occur; however, predicting falls is critical in health and safety. When aged people are alone at home, the chances of a dangerous fall are high. There is no one to help them instantly; therefore, timely notifying the concerned caregivers is crucial. Our approach has created a low computational cost algorithm, which depends on the head joint extracted using Kinect. The standard deviation formula is used to check variation in the Y-axis of head joints in every frame. The rate of change is then compared to the value of standard deviation generated from experimental results. After calculating the head trajectory's standard deviation, the next step is to classify every event into a separate fall and non-fall using the k-NN model. For assessing the k-NN model, we captured around 120 video samples of different activities tested by performance metrics to achieve an accuracy of 0.95.
Keyword:
Machine learning
Fall detection
Kinect
K-NN model
期刊
IF:
3
论文数:
2.0W
被引数:
3.2W
机构
引用论文
Error compensation based on surface reconstruction for industrial robot on two-dimensional manifold基于曲面重构的工业机器人二维流形误差补偿
PREAI
分享收藏Extending lab-based X-ray nanotomography of low Z and porous materials to larger sample volumes without compromising resolution将基于实验室的X射线纳米断层成像技术扩展至更大的样品体积，同时不牺牲分辨率，应用于低原子序数和高孔隙率材料。
OAAI
OAAI分享收藏Privacy-Preserved Behavior Analysis and Fall Detection by an Infrared Ceiling Sensor Network.png)
SENSORSOAAI
SENSORSIF3.5
OAAI分享收藏
查看更多内容