We appreciate the raised ethical concerns and would like to first address them in the context of our work on RETR. We will then respond to your concerns regarding privacy and security related to HIBER and MMVR to the best of our ability, as these are separate publications.
**RETR**
- In training RETR with the MMVR and HIBER datasets, we only utilized radar heatmaps (without any RGB images) and annotation labels such as bounding boxes (BBox) and segmentation masks. Fig.11 provides an illustration of BBoxes and masks in HIBER. From the heatmaps and labels, it is difficult to directly identify individuals or infer personal attributes like gender and weight. While the mask and BBox might indicate height, the data used is limited in revealing sensitive information. We included de-faced RGB images from MMVR only for illustration purposes by requesting selected frames from the MMVR authors. We did not have any RGB images from the HIBER dataset for illustration.
- Although this RETR paper did not involve creating a new radar perception dataset, we emphasized in the "Broader Impacts" section at the end of the paper that while indoor radar perception models may open up applications for good uses such as elderly care and fall detection, it is crucial that the resulting perception results are kept secure and private, ensuring they are not misused to infer attributes like gender, size, or height without permission. Alongside the subsequent discussions on the MMVR and HIBER datasets, we plan to expand the "Broader Impacts" section to further raise awareness of the potential harms and misuse of radar perception models.
**Questions regarding HIBER and MMVR**
- *How was the data de-identified...?*
- The HIBER dataset and the MMVR dataset do NOT contain RGB images. Only radar heatmaps and annotation labels such as BBoxes, keypoints, and segmentation masks, are included. Due to the nature of radar heatmaps and these annotations, it is difficult to directly identify individuals, ensuring a level of de-identification in the data.
- *What measures...?*
- Although we cannot speak on behalf of the authors of these datasets, based on the information provided in their papers, we have learned the following:
- **No RGB Images Released:** No RGB images are included in the publicly available versions of the datasets. A small number of RGB images were used in the HIBER and MMVR papers, but in the MMVR paper, these images were all de-faced to protect participant identities.
- **Informed Consent and Data De-identification:** The MMVR paper includes a statement in Sec.6 that outlines their ethical procedures. Participants were informed at the beginning of each data session about the experiment's goals, procedures, potential exposure to high-frequency radio waves, and the use of cameras. They were also notified that de-identified data would be made publicly accessible for research purposes, and all participants signed a consent form.
- We did not find specific statements regarding data collection protocols in the HIBER paper, but it is noteworthy that on the MMVR dataset webpage, participant faces are masked.
- *Do the datasets remove...?*
- The major measure is to NOT include RGB images in both datasets. This significantly reduces the risk of visual identification. Also, it appears to us that the subjects are anonymized in the dataset.
- *Are there any methods or...?*
- There are safeguards in place for both the HIBER and MMVR datasets, according to their papers and dataset websites:
- **HIBER**: Any user of the HIBER dataset is required to sign an agreement that includes clauses limiting the use of the dataset only for scientific purposes and pointing out potential misuse and harm of using their dataset. This obligation serves as a deterrent against inappropriate uses, including unauthorized indoor surveillance.
- **MMVR**: The authors of the MMVR paper have explicitly addressed the ethical implications of using radar perception technologies for purposes such as classifying and estimating personal attributes. In Sec.7 of their paper, they raise awareness of the potential misuse of the dataset, noting: "Although radar perception has fewer privacy concerns than cameras, MMVR can potentially be utilized to classify and estimate attributes of subjects such as gender, size, height, and gait. It may also be used to advance technologies for indoor surveillance without acknowledgment or permission." This acknowledgment helps to inform users of the ethical considerations and potential risks associated with the dataset, encouraging responsible use.
Since our paper focuses on training a radar perception model, not the dataset itself, we’re considering legal clauses similar to or stronger than those in the HIBER dataset for users who are interested in using our code and model weights. We’ll also expand the "Broader Impacts" section to highlight potential harms and misuse of these models.