A Method of Filter Implementation Using Heterogeneous Computing System for Driver Health Monitoring Oakland University

Format:

Book

Conference/Event

Author/Creator:

sinnapolu, Giribabu, author.

Contributor:

Alawneh, Shadi

Conference Name:

SAE WCX Digital Summit (2021-04-13 : Live Online, Pennsylvania, United States)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2021

Summary:

Research in any field of study requires analysis and comparisons or real-time prediction to extract useful information. To prove that the results have practical potential, outstanding filtering techniques and methodologies should be designed and implemented. Filters being a class of signal processing helps innovate new technologies with various kinds of outcomes, using filters there are always various ways to solve a problem. Considering the current COVID-19 situation, researchers are working on sequencing the novel coronavirus and the genomes of people afflicted with COVID-19 using CPU's and GPU's along with various filtering techniques. In this paper we are using a method of filter implementation to collect raw heart rate data samples from fingertip and ear lobe and process those results on CPU's and GPU's. Our method of implementation to collect raw heart rate data is using a photoplethysmography method. We all know that the moving average filtering technique is the most commonly used for averaging an array of sampled data but here in this paper we reconstructed the entire moving average filter with a slightly different averaging method where we will prove how our filter technique is better than the traditional moving average filter. This filtering technique is implemented and compared on both GPU's and CPU's. However, the filters on GPU's are slightly altered as per the GPU framework and CUDA programming techniques to optimize and output tremendous results. We will also conduct Human trials for this concept and talk about how the heart rate changes while driving and also considering environmental conditions and scenarios. The findings of this work are also compared with apple watch heart rate data as it is the most accurate heart rate sensing device in the market with less than 2% error rate

Notes:

Vendor supplied data

Publisher Number:

2021-01-0103

Access Restriction:

Restricted for use by site license