Accession Number : AD1002995

Title :   Wearable Wireless Sensor for Multi-Scale Physiological Monitoring

Descriptive Note : Technical Report,25 Sep 2014,24 Sep 2015

Corporate Author : Worcester Polytechnic Institute Worcester United States

Personal Author(s) : Mendelson,Yitzhak ; Chon,Ki H

Full Text :

Report Date : 01 Oct 2015

Pagination or Media Count : 142

Abstract : One of the aims of Year 2 of the project was to complete development of a prototype multi-channel pulse oximeter that can be used to collect physiological data from multiple body locations to combat motion artifact contamination. Specifically, the aim was to investigate if a motion artifact-free signal can be obtained in at least one of the multi-channels at any given time. Towards this aim, we have developed a prototype 6-photodetector reflectance-based pulse oximeter and results to date show that good signals can be obtained in one of the multi-channels at any given time. These devices are currently in use for field testing in our labs and at UMASS. Moreover, it was found that both forehead- and ear-located pulse oximeters provide better signal quality than a finger pulse oximeter. The second major aim of the project was to develop a motion and noise detection algorithm and a separate algorithm for the reconstruction ofthe motion and noise contaminated portion of the data. For detection of motion and noise artifacts, we have successfully developed an accurate and real-time realizable algorithm. For all new data including that from UMASS, our new MNA algorithms consistently provide better accuracy than our previously-published algorithm. A manuscript describing this new algorithm has been submitted for publication. In the past year, we have published 4 journal articles and 2 journal articles have been submitted based on algorithm development. Moreover, we have filed 2 new patent disclosures on our algorithms. Finally, we have recruited 105 patients to date at UMASS. These data will be used in the year 3 to investigate the robustness of our motion and noise artifact algorithms. Both the sensor and algorithms will be thoroughly tested and further refined, if needed, using the UMASS data collected in Year 3 of the project.


Distribution Statement : APPROVED FOR PUBLIC RELEASE