DOI: 10.57760/sciencedb.00357
title: CNN + LSTM model source program for continuous monitoring of exercise heart rate based on PPG signals with motion artifacts
title: CNN + LSTM model source program for continuous monitoring of exercise heart rate based on PPG signals with motion artifacts

description: This program establishes a deep learning model of CNN+LSTM, which is used for continuous monitoring of exercise heart rate with PPG signals containing motion artifacts, and has achieved good results in the PPG-DaLiA database. The description is as follows: 1. The file main_program_file is the main file, including model construction, data processing, data training, model data verification, and other processing programs for PPG signals that are not used in this article. model: build exercise heart rate monitoring model file; activity_time.xls: Collect each activity time node of each volunteer signal obtained from the PPG-DaLiA database; original_data_read.py: signal data preprocessing program (signal from the PPG-DaLiA database); ppg_filed_hr_cornet_estimate.py: training and prediction program for all volunteers’ PPG signals; ppg_filed_hr_cornet_estimate_single.py: a program to predict the PPG signal of a single volunteer; _1d_cnn, _2d_cnn, ppg_excerise_cnn_type.py, ppg_filed_hr_cnn_estimate.py: programs that use the CNN method for prediction; spc_hr_cornet_estimate.py, spc_hr_cnn_estimate.py: programs for predicting and verifying using other database PPG signals. save_model_estimate_hr.py, save_model_estimate_hr_spc.py: save the heart rate prediction model and the model program for the heart rate prediction model to be used in the SPC database. out_fig: model prediction picture output folder; 2. Data source The data comes from the PPG-DaLiA database (PPG Data For Daily Life Activity, https://archive.ics.uci.edu/ml/datasets/PPG-DaLiA): The database comes from Robert Bosch GmbH and Bosch Sensortec GmbH. The signals in this database come from 15 volunteers of different ages and different physical conditions. PPG and heart rate data are continuously collected during different exercises. The preprocessing of the downloaded data is in the program original_data_read.py. 3.other _0_basic_fun, ch3_preprocess, my_pyhht_lib: some external references of the main program, mainly the functions called by the data preprocessing part, and the main program can view their functions.This program establishes a deep learning model of CNN+LSTM, which is used for continuous monitoring of exercise heart rate with PPG signals containing motion artifacts, and has achieved good results in the PPG-DaLiA database. The description is as follows: 1. The file main_program_file is the main file, including model construction, data processing, data training, model data verification, and other processing programs for PPG signals that are not used in this article. model: build exercise heart rate monitoring model file; activity_time.xls: Collect each activity time node of each volunteer signal obtained from the PPG-DaLiA database; original_data_read.py: signal data preprocessing program (signal from the PPG-DaLiA database); ppg_filed_hr_cornet_estimate.py: training and prediction program for all volunteers’ PPG signals; ppg_filed_hr_cornet_estimate_single.py: a program to predict the PPG signal of a single volunteer; _1d_cnn, _2d_cnn, ppg_excerise_cnn_type.py, ppg_filed_hr_cnn_estimate.py: programs that use the CNN method for prediction; spc_hr_cornet_estimate.py, spc_hr_cnn_estimate.py: programs for predicting and verifying using other database PPG signals. save_model_estimate_hr.py, save_model_estimate_hr_spc.py: save the heart rate prediction model and the model program for the heart rate prediction model to be used in the SPC database. out_fig: model prediction picture output folder; 2. Data source The data comes from the PPG-DaLiA database (PPG Data For Daily Life Activity, https://archive.ics.uci.edu/ml/datasets/PPG-DaLiA): The database comes from Robert Bosch GmbH and Bosch Sensortec GmbH. The signals in this database come from 15 volunteers of different ages and different physical conditions. PPG and heart rate data are continuously collected during different exercises. The preprocessing of the downloaded data is in the program original_data_read.py. 3.other _0_basic_fun, ch3_preprocess, my_pyhht_lib: some external references of the main program, mainly the functions called by the data preprocessing part, and the main program can view their functions.
description: This program establishes a deep learning model of CNN+LSTM, which is used for continuous monitoring of exercise heart rate with PPG signals containing motion artifacts, and has achieved good results in the PPG-DaLiA database. The description is as follows: 1. The file main_program_file is the main file, including model construction, data processing, data training, model data verification, and other processing programs for PPG signals that are not used in this article. model: build exercise heart rate monitoring model file; activity_time.xls: Collect each activity time node of each volunteer signal obtained from the PPG-DaLiA database; original_data_read.py: signal data preprocessing program (signal from the PPG-DaLiA database); ppg_filed_hr_cornet_estimate.py: training and prediction program for all volunteers’ PPG signals; ppg_filed_hr_cornet_estimate_single.py: a program to predict the PPG signal of a single volunteer; _1d_cnn, _2d_cnn, ppg_excerise_cnn_type.py, ppg_filed_hr_cnn_estimate.py: programs that use the CNN method for prediction; spc_hr_cornet_estimate.py, spc_hr_cnn_estimate.py: programs for predicting and verifying using other database PPG signals. save_model_estimate_hr.py, save_model_estimate_hr_spc.py: save the heart rate prediction model and the model program for the heart rate prediction model to be used in the SPC database. out_fig: model prediction picture output folder; 2. Data source The data comes from the PPG-DaLiA database (PPG Data For Daily Life Activity, https://archive.ics.uci.edu/ml/datasets/PPG-DaLiA): The database comes from Robert Bosch GmbH and Bosch Sensortec GmbH. The signals in this database come from 15 volunteers of different ages and different physical conditions. PPG and heart rate data are continuously collected during different exercises. The preprocessing of the downloaded data is in the program original_data_read.py. 3.other _0_basic_fun, ch3_preprocess, my_pyhht_lib: some external references of the main program, mainly the functions called by the data preprocessing part, and the main program can view their functions.This program establishes a deep learning model of CNN+LSTM, which is used for continuous monitoring of exercise heart rate with PPG signals containing motion artifacts, and has achieved good results in the PPG-DaLiA database. The description is as follows: 1. The file main_program_file is the main file, including model construction, data processing, data training, model data verification, and other processing programs for PPG signals that are not used in this article. model: build exercise heart rate monitoring model file; activity_time.xls: Collect each activity time node of each volunteer signal obtained from the PPG-DaLiA database; original_data_read.py: signal data preprocessing program (signal from the PPG-DaLiA database); ppg_filed_hr_cornet_estimate.py: training and prediction program for all volunteers’ PPG signals; ppg_filed_hr_cornet_estimate_single.py: a program to predict the PPG signal of a single volunteer; _1d_cnn, _2d_cnn, ppg_excerise_cnn_type.py, ppg_filed_hr_cnn_estimate.py: programs that use the CNN method for prediction; spc_hr_cornet_estimate.py, spc_hr_cnn_estimate.py: programs for predicting and verifying using other database PPG signals. save_model_estimate_hr.py, save_model_estimate_hr_spc.py: save the heart rate prediction model and the model program for the heart rate prediction model to be used in the SPC database. out_fig: model prediction picture output folder; 2. Data source The data comes from the PPG-DaLiA database (PPG Data For Daily Life Activity, https://archive.ics.uci.edu/ml/datasets/PPG-DaLiA): The database comes from Robert Bosch GmbH and Bosch Sensortec GmbH. The signals in this database come from 15 volunteers of different ages and different physical conditions. PPG and heart rate data are continuously collected during different exercises. The preprocessing of the downloaded data is in the program original_data_read.py. 3.other _0_basic_fun, ch3_preprocess, my_pyhht_lib: some external references of the main program, mainly the functions called by the data preprocessing part, and the main program can view their functions.