The UnoViS database provides a publicly available dataset of unobtrusively measured vital signs to facilitate the development and comparison of robust signal processing algorithms.
Answers are grounded in this paper and cite their sources.
UnoViS enables standardized benchmarking of unobtrusive vital-sign algorithms; leaves open prospective clinical validation before practice impact.
While PhysioNet is a large database for standard clinical vital signs measurements, such a database does not exist for unobtrusively measured signals. This inhibits progress in the vital area of signal processing for unobtrusive medical monitoring as not everybody owns the specific measurement systems to acquire signals. Furthermore, if no common database exists, a comparison between different signal processing approaches is not possible. This gap will be closed by our UnoViS database. It contains different recordings in various scenarios ranging from a clinical study to measurements obtained while driving a car. Currently, 145 records with a total of 16.2 h of measurement data is available, which are provided as MATLAB files or in the PhysioNet WFDB file format. In its initial state, only (multichannel) capacitive ECG and unobtrusive PPG signals are, together with a reference ECG, included. All ECG signals contain annotations by a peak detector and by a medical expert. A dataset from a clinical study contains further clinical annotations. Additionally, supplementary functions are provided, which simplify the usage of the database and thus the development and evaluation of new algorithms. The development of urgently needed methods for very robust parameter extraction or robust signal fusion in view of frequent severe motion artifacts in unobtrusive monitoring is now possible with the database.
No takes yet. Share an insight, caveat, or question.
Wartzek et al. (2015) studied this question.
Synapse has enriched 10 related papers. Consider them for comparative context: