Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

6-2022

Abstract

The primary contribution of this paper is designing and prototyping a real-time edge computing system, RhythmEdge, that is capable of detecting changes in blood volume from facial videos (Remote Photoplethysmography; rPPG), enabling cardio-vascular health assessment instantly. The benefits of RhythmEdge include non-invasive measurement of cardiovascular activity, real-time system operation, inexpensive sensing components, and computing. RhythmEdge captures a short video of the skin using a camera and extracts rPPG features to estimate the Photoplethysmography (PPG) signal using a multi-task learning framework while offloading the edge computation. In addition, we intelligently apply a transfer learning approach to the multi-task learning framework to mitigate sensor heterogeneities to scale the RhythmEdge prototype to work with a range of commercially available sensing and computing devices. Besides, to further adapt the software stack for resource-constrained devices, we postulate novel pruning and quantization techniques (Quantization: FP32, FP16; Pruned-Quantized: FP32, FP16) that efficiently optimize the deep feature learning while minimizing the runtime, latency, memory, and power usage. We benchmark RhythmEdge prototype for three different cameras and edge computing platforms while evaluating it on three publicly available datasets and an in-house dataset collected under challenging environmental circumstances. Our analysis indicates that RhythmEdge performs on par with the existing contactless heart rate monitoring systems while utilizing only half of its available resources. Furthermore, we perform an ablation study with and without pruning and quantization to report the model size (87%) vs. inference time (70%) reduction. We attested the efficacy of RhythmEdge prototype with a maximum power of 8W and a memory usage of 290MB, with a minimal latency of 0.0625 seconds and a runtime of 0.64 seconds per 30 frames.

Keywords

rPPG, Edge Computing, System Prototyping

Discipline

Databases and Information Systems | Data Science | Health Information Technology

Research Areas

Data Science and Engineering

Publication

Proceedings of the 2022 IEEE International Conference on Smart Computing, Helsinki, Finland, June 20-24

First Page

92

Last Page

99

ISBN

9781665481533

Identifier

10.1109/SMARTCOMP55677.2022.00028

Publisher

IEEE Computer Society

City or Country

Helsinki, Finland

Embargo Period

6-25-2023

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/SMARTCOMP55677.2022.00028

Download

Share

COinS