We are developing deep learning algorithm for automated sleep stage scoring and diagnosis sleep-apnea of syndromes.
Using single-channel EEG signal, our deep learning model called IITNet automates the sleep stage scoring by extracting and analyzing the features at sub-epoch level with the state-of-the-art performance.
We are developing a non-contact respiratory measuring algorithm which predicts the respiratory signals from UWB signals in real-time via a convolutional neural network.
A sleep management system that automatically detects sleep apnea by integrating the deep learning models for sleep staging and respiratory monitoring.
Because of increasing the number of elders, the elderly care labor force becomes a more important issue. Considering this problem, the robot with AI will help this.
We introduce four cases to complete this project.
Using Deep Visual Tracker, the robot will track the elder. Then, using deep CNN and LSTM, it will classify the user and judge behavior of taking medicine.
By database of user’s normal behavior, we can get the network which can have the same output with the input learned before using Deep Auto-encoder. Using this, we can classify between normal behavior and abnormal behavior.
We can judge the diseases(dementia, stroke, etc.) by the elder’s walking motion. Therefore, we have to collect the skeleton data using the Depth Camera, and then develop an Anomaly Judgement Algorithm based on Deep Auto-encoder.
We have to develop the server software which can collect and manage the database in real time. This software can make dataset to be used to Deep learning.