mmSkin: An Over-Gauze Wound Assessment System Using Radio Frequency Technologies

Skin wounds are often covered with gauze to protect the injury and support the healing process. Accurate wound assessment is essential for monitoring healing progress and guiding treatment decisions. However, existing assessment methods typically require direct exposure of the wound, necessitating the removal of gauze when present. This process disrupts the healing environment and increases the risk of secondary infections. In this paper, we introduce mmSkin, an innovative over-gauze wound assessment system that utilizes millimeter-wave (mmWave) radar technology to evaluate wound characteristics without the need to remove the gauze. Central to this system is the principle that variations in skin moisture, a critical indicator of wound health, significantly influence mmWave signal strength. By analyzing these variations, mmSkin accurately identifies skin moisture levels, thereby enabling precise assessment of wound conditions. To achieve reliable sensing, mmSkin incorporates a denoised mmWave imaging algorithm designed to reduce motion noise and effectively distinguish between signals reflected from the target skin and those from surrounding environmental interference. Additionally, the system integrates a physics-based model to guide the training of its moisture derivation model. This integration ensures that mmSkin can accurately estimate moisture distribution across the wound area, making it a powerful tool for noninvasive wound assessment. Extensive experiments validate the system’s high accuracy in over-gauze wound moisture distribution estimation, achieving a mean moisture error of approximately 0.5% in both wound phantom and invivo tests. Additionally, the system demonstrates a structural similarity index measure (SSIM) of about 0.9 compared to groundtruth moisture distributions in both test scenarios. These results highlight mmSkin’s potential to revolutionize noninvasive wound assessment and improve patient outcomes.

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Publication Details

Journal
IEEE Internet of Things Journal
Published
2025-03-19
DOI
https://doi.org/10.1109/jiot.2025.3553057
Primary Topic
Gait Recognition and Analysis
Type
article
Field-Weighted Citation Impact
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article

mmSkin: An Over-Gauze Wound Assessment System Using Radio Frequency Technologies

Zhengxiong Li, Chenhan Xu, Wei Bo, Wenyao Xu et al.
IEEE Internet of Things Journal
Gait Recognition and Analysis
article

mmSkin: An Over-Gauze Wound Assessment System Using Radio Frequency Technologies

Zhengxiong Li, Chenhan Xu, Wei Bo, Wenyao Xu, Yanda Cheng, Chuqin Huang, Emma Zhang, Jun Xia, Xiaoyu Zhang, Ye Zhan
article en

Abstract

Skin wounds are often covered with gauze to protect the injury and support the healing process. Accurate wound assessment is essential for monitoring healing progress and guiding treatment decisions. However, existing assessment methods typically require direct exposure of the wound, necessitating the removal of gauze when present. This process disrupts the healing environment and increases the risk of secondary infections. In this paper, we introduce mmSkin, an innovative over-gauze wound assessment system that utilizes millimeter-wave (mmWave) radar technology to evaluate wound characteristics without the need to remove the gauze. Central to this system is the principle that variations in skin moisture, a critical indicator of wound health, significantly influence mmWave signal strength. By analyzing these variations, mmSkin accurately identifies skin moisture levels, thereby enabling precise assessment of wound conditions. To achieve reliable sensing, mmSkin incorporates a denoised mmWave imaging algorithm designed to reduce motion noise and effectively distinguish between signals reflected from the target skin and those from surrounding environmental interference. Additionally, the system integrates a physics-based model to guide the training of its moisture derivation model. This integration ensures that mmSkin can accurately estimate moisture distribution across the wound area, making it a powerful tool for noninvasive wound assessment. Extensive experiments validate the system’s high accuracy in over-gauze wound moisture distribution estimation, achieving a mean moisture error of approximately 0.5% in both wound phantom and invivo tests. Additionally, the system demonstrates a structural similarity index measure (SSIM) of about 0.9 compared to groundtruth moisture distributions in both test scenarios. These results highlight mmSkin’s potential to revolutionize noninvasive wound assessment and improve patient outcomes.

IEEE Internet of Things JournalVol. 12(13)
Linde (United States) (US), North Carolina State University (US), University at Buffalo, State University of New York (US), University of Colorado Denver (US)
Openalex Percentile: Top 95%
Gait Recognition and Analysis
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