https://www.selleckchem.com/products/ck-586.html
We introduce a video-based system for concurrent activity recognition during teamwork in a clinical setting. During system development, we preserved patient and provider privacy by pre-computing spatio-temporal features. We extended the inflated 3D ConvNet (i3D) model for concurrent activity recognition. For the model training, we tuned the weights of the final stages of i3D using back-propagated loss from the fully-connected layer. We applied filtering on the model predictions to remove noisy predictions. We evaluated the system on five
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