Details
Poster
Presenter(s)
![Christoph Richter Headshot](https://confcats-catavault.s3.amazonaws.com/CATAVault/ieeecass/master/files/styles/cc_user_photo/s3/user-pictures/61072.jpg?h=8f391919&itok=-Ecf_06p)
Display Name
Christoph Richter
- Affiliation
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AffiliationDSI Aerospace Technology
- Country
Abstract
In this paper a novel hardware architecture for highaccuracy and near-sensor BCG complex recognition is presented. Main contribution of our work is the implementation of an adaptive method for J-wave peak detection on FPGA enabling reliable online waveform monitoring. Also, to further increase the overall signal quality, Chebychev-based filtering is installed, leading to a smoother signal progression. The evaluation results highlight our approach as a well suited solution for online BCG complex recognition in resource constraint environments, as detection rates between 95.31% and 100% can be achieved considering human bodies in a resting position.