Falls are a major cause of disability and even death in the elderly,and fall detection can effectively reduce the damage.Compared with cameras and wearable sensors,Wi-Fi devices can protect user privacy and are inexpe...Falls are a major cause of disability and even death in the elderly,and fall detection can effectively reduce the damage.Compared with cameras and wearable sensors,Wi-Fi devices can protect user privacy and are inexpensive and easy to deploy.Wi-Fi devices sense user activity by analyzing the channel state information(CSI)of the received signal,which makes fall detection possible.We propose a fall detection system based on commercial Wi-Fi devices which achieves good performance.In the feature extraction stage,we select the discrete wavelet transform(DWT)spectrum as the feature for activity classification,which can balance the temporal and spatial resolution.In the feature classification stage,we design a deep learning model based on convolutional neural networks,which has better performance compared with other traditional machine learning models.Experimental results show our work achieves a false alarm rate of 4.8%and a missed alarm rate of 1.9%.展开更多
The discrete element method(DEM)was used in this study to numerically simulate the mixing process and motion law of particles in brown rice germination device.And the reliability of simulation experiments was verified...The discrete element method(DEM)was used in this study to numerically simulate the mixing process and motion law of particles in brown rice germination device.And the reliability of simulation experiments was verified through physical experiments.In the discrete element simulation experiment,there were three mixing stages in the mixing process of the particles.The particle motion conditions at different rotational speeds were rolling,cascading,cataracting and centrifuging.The lower the filling degree,the higher the particle mixing efficiency.The radial trajectory of the particles was approximated as an elliptical helix that continuously shrank towards the axis.The research results indicated that under the same speed and filling conditions,the motion of brown rice particles in both the simulated and physical test environments is rolling and the drop height is the same.展开更多
文摘Falls are a major cause of disability and even death in the elderly,and fall detection can effectively reduce the damage.Compared with cameras and wearable sensors,Wi-Fi devices can protect user privacy and are inexpensive and easy to deploy.Wi-Fi devices sense user activity by analyzing the channel state information(CSI)of the received signal,which makes fall detection possible.We propose a fall detection system based on commercial Wi-Fi devices which achieves good performance.In the feature extraction stage,we select the discrete wavelet transform(DWT)spectrum as the feature for activity classification,which can balance the temporal and spatial resolution.In the feature classification stage,we design a deep learning model based on convolutional neural networks,which has better performance compared with other traditional machine learning models.Experimental results show our work achieves a false alarm rate of 4.8%and a missed alarm rate of 1.9%.
基金the National Natural Science Foundation of China(Grant No.32001423)Natural Science Foundation of Hubei Province(Grant No.2020CFB471)+2 种基金Huazhong Agricultural University College Students Science and Technology Innovation Fund Project(Grant No.2022255)Fundamental Research Funds for the Central Universities(Grant No.2662020GXPY017)First Division Alar City Science and Technology Plan Project(Grant No.2023ZB01)for financial support and all of the persons who assisted in this writing.
文摘The discrete element method(DEM)was used in this study to numerically simulate the mixing process and motion law of particles in brown rice germination device.And the reliability of simulation experiments was verified through physical experiments.In the discrete element simulation experiment,there were three mixing stages in the mixing process of the particles.The particle motion conditions at different rotational speeds were rolling,cascading,cataracting and centrifuging.The lower the filling degree,the higher the particle mixing efficiency.The radial trajectory of the particles was approximated as an elliptical helix that continuously shrank towards the axis.The research results indicated that under the same speed and filling conditions,the motion of brown rice particles in both the simulated and physical test environments is rolling and the drop height is the same.
