According to the requirement of natural human-computer interaction for Ambient Intelligence （Aml）, a Bluetoothbased authentication technique is provided. An authentication network combining advantages of Bluetooth a...According to the requirement of natural human-computer interaction for Ambient Intelligence （Aml）, a Bluetoothbased authentication technique is provided. An authentication network combining advantages of Bluetooth ad hoc network with the Ethernet is introduced first in detail. Then we propose a Bluetooth badge for storing the user＇s identification information. Finally, the authentication system based on Bluetooth badge and authentication network is introduced. It is demonstrated experimentally that the Bluetooth-based authentication technique can authenticate the user automatically.展开更多
Medical-action recognition is crucial for ensuring the quality of medical services.With advancements in deep learning,RGB camera-based human-action recognition made huge advancements.However,RGB cameras encounter issu...Medical-action recognition is crucial for ensuring the quality of medical services.With advancements in deep learning,RGB camera-based human-action recognition made huge advancements.However,RGB cameras encounter issues,such as depth ambiguity and privacy violation.In this paper,we propose a novel lidar-based action-recognition algorithm for medical quality control.Further,point-cloud data were used for recognizing hand-washing actions of doctors and recording the action’s duration.An improved anchor-to-joint(A2J)network,with pyramid vision transformer and feature pyramid network modules,was developed for estimating the human poses.In addition,we designed a graph convolution network for action classification based on the skeleton data.Then,we evaluated the performance of the improved A2J network on the open-source ITOP and our medical pose estimation datasets.Further,we tested our medical action-recognition method in actual wards to demonstrate its effectiveness and running efficiency.The results show that the proposed algorithm can effectively recognize the actions of medical staff,providing satisfactory real-time performance and 96.3% action-classification accuracy.展开更多
基金the National Natural Science Foundation of China (No. 60773186)the Science and Technology Research Foundation of the Beijing Municipal Education Commission of China (No. KM200710005018)
文摘According to the requirement of natural human-computer interaction for Ambient Intelligence （Aml）, a Bluetoothbased authentication technique is provided. An authentication network combining advantages of Bluetooth ad hoc network with the Ethernet is introduced first in detail. Then we propose a Bluetooth badge for storing the user＇s identification information. Finally, the authentication system based on Bluetooth badge and authentication network is introduced. It is demonstrated experimentally that the Bluetooth-based authentication technique can authenticate the user automatically.
文摘Medical-action recognition is crucial for ensuring the quality of medical services.With advancements in deep learning,RGB camera-based human-action recognition made huge advancements.However,RGB cameras encounter issues,such as depth ambiguity and privacy violation.In this paper,we propose a novel lidar-based action-recognition algorithm for medical quality control.Further,point-cloud data were used for recognizing hand-washing actions of doctors and recording the action’s duration.An improved anchor-to-joint(A2J)network,with pyramid vision transformer and feature pyramid network modules,was developed for estimating the human poses.In addition,we designed a graph convolution network for action classification based on the skeleton data.Then,we evaluated the performance of the improved A2J network on the open-source ITOP and our medical pose estimation datasets.Further,we tested our medical action-recognition method in actual wards to demonstrate its effectiveness and running efficiency.The results show that the proposed algorithm can effectively recognize the actions of medical staff,providing satisfactory real-time performance and 96.3% action-classification accuracy.
