{"title":"mmSkin: An Over-Gauze Wound Assessment System Using Radio Frequency Technologies","authors":"Xiaoyu Zhang;Zhengxiong Li;Yanda Cheng;Chenhan Xu;Chuqin Huang;Emma Zhang;Ye Zhan;Wei Bo;Jun Xia;Wenyao Xu","doi":"10.1109/JIOT.2025.3553057","DOIUrl":null,"url":null,"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.","PeriodicalId":54347,"journal":{"name":"IEEE Internet of Things Journal","volume":"12 13","pages":"23773-23787"},"PeriodicalIF":8.9000,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Internet of Things Journal","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10934061/","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
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.
期刊介绍:
The EEE Internet of Things (IoT) Journal publishes articles and review articles covering various aspects of IoT, including IoT system architecture, IoT enabling technologies, IoT communication and networking protocols such as network coding, and IoT services and applications. Topics encompass IoT's impacts on sensor technologies, big data management, and future internet design for applications like smart cities and smart homes. Fields of interest include IoT architecture such as things-centric, data-centric, service-oriented IoT architecture; IoT enabling technologies and systematic integration such as sensor technologies, big sensor data management, and future Internet design for IoT; IoT services, applications, and test-beds such as IoT service middleware, IoT application programming interface (API), IoT application design, and IoT trials/experiments; IoT standardization activities and technology development in different standard development organizations (SDO) such as IEEE, IETF, ITU, 3GPP, ETSI, etc.