5 research outputs found
Remote Bio-Sensing: Open Source Benchmark Framework for Fair Evaluation of rPPG
Remote Photoplethysmography (rPPG) is a technology that utilizes the light
absorption properties of hemoglobin, captured via camera, to analyze and
measure blood volume pulse (BVP). By analyzing the measured BVP, various
physiological signals such as heart rate, stress levels, and blood pressure can
be derived, enabling applications such as the early prediction of
cardiovascular diseases. rPPG is a rapidly evolving field as it allows the
measurement of vital signals using camera-equipped devices without the need for
additional devices such as blood pressure monitors or pulse oximeters, and
without the assistance of medical experts. Despite extensive efforts and
advances in this field, serious challenges remain, including issues related to
skin color, camera characteristics, ambient lighting, and other sources of
noise, which degrade performance accuracy. We argue that fair and evaluable
benchmarking is urgently required to overcome these challenges and make any
meaningful progress from both academic and commercial perspectives. In most
existing work, models are trained, tested, and validated only on limited
datasets. Worse still, some studies lack available code or reproducibility,
making it difficult to fairly evaluate and compare performance. Therefore, the
purpose of this study is to provide a benchmarking framework to evaluate
various rPPG techniques across a wide range of datasets for fair evaluation and
comparison, including both conventional non-deep neural network (non-DNN) and
deep neural network (DNN) methods. GitHub URL:
https://github.com/remotebiosensing/rppg.Comment: 19 pages, 10 figure
Academic competitions
Academic challenges comprise effective means for (i) advancing the state of
the art, (ii) putting in the spotlight of a scientific community specific
topics and problems, as well as (iii) closing the gap for under represented
communities in terms of accessing and participating in the shaping of research
fields. Competitions can be traced back for centuries and their achievements
have had great influence in our modern world. Recently, they (re)gained
popularity, with the overwhelming amounts of data that is being generated in
different domains, as well as the need of pushing the barriers of existing
methods, and available tools to handle such data. This chapter provides a
survey of academic challenges in the context of machine learning and related
fields. We review the most influential competitions in the last few years and
analyze challenges per area of knowledge. The aims of scientific challenges,
their goals, major achievements and expectations for the next few years are
reviewed
The 1st challenge on remote physiological signal sensing (RePSS)
Abstract
Remote measurement of physiological signals from videos is an emerging topic. The topic draws great interests, but the lack of publicly available benchmark databases and a fair validation platform are hindering its further development. For this concern, we organize the first challenge on Remote Physiological Signal Sensing (RePSS), in which two databases of VIPL and OBF are provided as the benchmark for kin researchers to evaluate their approaches. The 1st challenge of RePSS focuses on measuring the average heart rate from facial videos, which is the basic problem of remote physiological measurement. This paper presents an overview of the challenge, including data, protocol, analysis of results and discussion. The top ranked solutions are highlighted to provide insights for researchers, and future directions are outlined for this topic and this challenge