| dc.description.abstract | In recent years, wearable devices are one of indispensable products. The advantages of wearable devices that help us to solve the problem and bring us a more convenient daily life, such as use of wearable devices to monitor a person walking steps, heart rate and the quality of sleep, etc. Nowadays, Sleep issue has become increasing seriousness, there are more and more people suffer from insomnia or sleep disorder. In order to assess sleep efficiency by wearable devices. Therefore, this thesis is about to proposes a sleep efficiency assessment system based on artificial neural networks.
This system which based on MLP and RBFN, including users’ basic information, heart rate, calories, step, diet and sleep-related information to predict sleep efficiency, which also uses the Pearson coefficient, principal component analysis and Fisher ratio respectively, for the test feature to collect data for each feature correlation sleep efficiency, an additional function of the atmosphere device in the system, allowing users to take advantage of this means for adjusting environment, improve sleep efficiency.
The experiment in this thesis has compared to other sleep dataset and our dataset. We also refer to deep neural network to test the datasets, and compare accuracy for artificial neural networks. | en_US |