Non-contact video-based pulse rate measurement on a mobile service robot

By Ronny Stricker, Steffen Müller, Horst-Michael Groß. IEEE International Symposium on Robot and Human Interactive Communication, 2014

459 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/67f21d5cd69bad82315666d9999663689cb2b414

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Radim Spetlik
2. Hu Han
3. Shiguang Shan
4. Jiri Matas
5. Vojtech Franc
6. Jan Cech
7. Xuesong Niu
8. Xilin Chen

In Electrical and Electronic Engineering

1. Shiguang Shan
2. Jiri Matas

Citations in the corpus

• Visual Heart Rate Estimation with Convolutional Neural Network (2018, methodology): “The PURE dataset [15] and COHFACE dataset [6] contain the ground truth from pulse oximeters.”
• RhythmNet: End-to-End Heart Rate Estimation From Face via Spatial-Temporal Representation (2019, methodology): “Stricker et al. [10] released the PURE database consisting of 60 videos from 10 subjects, in which all the subjects are asked to perform 6 kinds of movements such as talking or head rotation.”
• Photoplethysmography revisited: from contact to noncontact, from point to imaging (2015, unknown): “The main part of this topic review was based on 69 IPPG-related studies [9], [21], [37], [59], [60], [62]–[125].”
• Remote Photoplethysmograph Signal Measurement from Facial Videos Using Spatio-Temporal Networks (2019, unknown): “, [12](Table 4) trained on MAHNOBHCI, [18] trained on a lot more (2302) videos from both the whole MAHNOB and another dataset PURE [19], and [3] trained on other self-collected data), so the results are compared on a general level.”