Sleeping_Pattern_Predict_Project

수정중

[Subject]

[Contents]

Developing an LSTM-CNN Model to Predicting Personalized Wake-up Time by Analysing Sleep Patterns
Integrating IoT devices on the App to Operate in Conjunction with User's Personalized Wake-up Time

[Program Languages]

[Idea]

The Sleeping Stages Cycle Order is divided into N1, N2, N3, N2, and REM Each cycle lasts around 90–110 minutes
This Project, Sleeping Cycle is Labeled into Four-Stages (Awake, REM, Core, and Deep) Detecting Awake Time is Labeled as "Core"

[Service]

[Contributors] Heejung Lim, Jiwon Park, Kunho Chu, Sujung Lee

Name		Name	Last commit message	Last commit date
Latest commit History 53 Commits
App_Xcode		App_Xcode
MySQL		MySQL
Web		Web
streamlit		streamlit
.DS_Store		.DS_Store
.gitattributes		.gitattributes
0525.ipynb		0525.ipynb
ARIMA.ipynb		ARIMA.ipynb
ARIMA_total.ipynb		ARIMA_total.ipynb
CNN_LSTM_final.ipynb		CNN_LSTM_final.ipynb
CNNsegmentation.ipynb		CNNsegmentation.ipynb
Data_preprocessing.ipynb		Data_preprocessing.ipynb
ECG_Classification_\|_CNN_LSTM_Attention_Mechanism_20230418.ipynb		ECG_Classification_\|_CNN_LSTM_Attention_Mechanism_20230418.ipynb
HACKATON.ipynb		HACKATON.ipynb
LSTM.ipynb		LSTM.ipynb
LSTM_example_with_Reuters_dataset.ipynb		LSTM_example_with_Reuters_dataset.ipynb
README.md		README.md
SJ_LSTM.ipynb		SJ_LSTM.ipynb
SJ_ML.ipynb		SJ_ML.ipynb
SJ_applewatch_Data_preprocessing.ipynb		SJ_applewatch_Data_preprocessing.ipynb
SJ_padding.ipynb		SJ_padding.ipynb
U_Net_binary.ipynb		U_Net_binary.ipynb
Untitled17.ipynb		Untitled17.ipynb
[fix]Deep_Learning_for_ECG_Segmentation.ipynb		[fix]Deep_Learning_for_ECG_Segmentation.ipynb
binary.ipynb		binary.ipynb
datapreprocessing_0424.ipynb		datapreprocessing_0424.ipynb
one_step_pred.ipynb		one_step_pred.ipynb
weekday_lstm.ipynb		weekday_lstm.ipynb
weekday_lstm_0425_hj.ipynb		weekday_lstm_0425_hj.ipynb
weekday_lstm_0425_hj_datettime.ipynb		weekday_lstm_0425_hj_datettime.ipynb
weekday_weekend_LSTM.ipynb		weekday_weekend_LSTM.ipynb
데이터전처리.ipynb		데이터전처리.ipynb
시계열.ipynb		시계열.ipynb

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