MultiPatient Elderly Respiration dataset in Digital Twin Technology

Published: 7 December 2023| Version 1 | DOI: 10.17632/vm8j5dvrxy.1


The research focus for this study is to generate a larger respiration dataset for the creation of elderly respiration Digital Twin (DT) model. Initial experimental data is collected with an unobtrusive Wi-Fi sensor with Channel State Information (CSI) characteristics to collect the subject's respiration rate. The generation of a DT model requires extensive and diverse data. Due to limited resources and the need for extensive experimentation, the data is generated by implementing a novel statistical time series data augmentation method on single-subject respiration data. The larger synthetic respiration datasets will allow for testing the signal processing methodologies for noise removal,Breaths Per Minute (BPM) estimation, extensive Artificial Intelligence (AI) implementation. The sensor data is for BPM from 12BPM to 25BPM for a single subject. Normal respiration rate ranges from 12BPM to 16BPM and beyond this is considered abnormal BPM. A total of 14 files are present in the dataset. Each file is labeled according to the BPM. All 30 patient data are present for each BPM. Patient are numbered as "P1, P2, P3, .... untill P30" This data can be utilized by researchers and scientists toward the development of novel signal processing methodologies in the respiration DT model. These larger respiration datasets can be utilized for Machine Learning (ML) and Deep Learning (DL) in providing predictive analysis and classification of multi-patient respiration in the DT model for an elderly respiration rate.



The University of Edinburgh


Biomedical Signal Processing, Respiration, Digital Twin Technology, Data Augmentation