Fusion-Vital: Video-RF Fusion Transformer for Advanced Remote Physiological Measurement
Authors: Jae-Ho Choi, Ki-Bong Kang, Kyung-Tae Kim
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also perform comprehensive experiments based on a newly collected and released remote vital dataset comprising synchronized video-RF sensors, showing the superiority of the fusion approach over the previous single-sensor baselines in various aspects. |
| Researcher Affiliation | Collaboration | Jae-Ho Choi1, Ki-Bong Kang2, Kyung-Tae Kim3 1Stanford University, CA, USA 2Samsung Electronics, South Korea 3Pohang University of Science and Technology, South Korea |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper states that a newly created dataset will be publicly available, but it does not provide an explicit statement or link for the open-source code of the methodology described in the paper. |
| Open Datasets | Yes | To evaluate the effectiveness of the proposed model, we performed extensive experiments on two datasets: the publicly available RRM-static dataset (Choi, Kang, and Kim 2022) and our newly collected physiological dataset, named the Multimodal Database for r PPG (MMDr PPG). |
| Dataset Splits | Yes | To ensure subject-independent crossvalidation, the datasets were divided into person-wise subfolds. More details regarding the evaluation protocols are available in the supplementary material. |
| Hardware Specification | No | The paper mentions that processing occurred "under workstation settings" but does not provide specific details such as GPU models, CPU models, or memory specifications used for the experiments. |
| Software Dependencies | No | The paper mentions the use of the ADAM optimizer but does not specify version numbers for any software components, libraries, or programming languages. |
| Experiment Setup | Yes | The proposed Fusion-Vital model was trained using the ADAM optimizer with a batch size of 64 and a learning rate of 0.0001. The inputs for the RGB branch consisted of video clips that were center-cropped and resized to 36 × 36 pixels... As for the RF branch, the received complex radio signals were transformed to the RJTF format using a STFT with a Hann window 300 ms long, hop size of 60 ms, and 256-point FFT. The resulting images were then resized to fit the temporal dimension of the RGB inputs. To ensure a fair comparison, all temporal models were configured with a window size of 10 frames. |