Continuous Monitoring of Wound Healing Using a Wearable Enzymatic Uric Acid Biosensor

RoyChoudhury, Sohini; Umasankar, Yogeswaran; Jaller, Jose A.; Herskovitz, Ingrid; Mervis, Joshua S.; Darwin, Evan; Hirt, Penelope A.; Borda, Luis J.; Lev‐Tov, Hadar; Kirsner, Robert S.

doi:10.1149/2.0231808jes

Cited by 87 publications

(62 citation statements)

References 46 publications

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“…They compare performance of classification algorithms by comparing their accuracy, sensitivity and response time and concluded that decision trees and K-nearest neighbor outperforms other algorithms. Sohini Roy Choudhury et al [21] design wearable device to monitor wound healing with help of uric acid biosensor. Their proposed device detects Uric Acid(UA) from wound.…”

Section: State Of the Artmentioning

confidence: 99%

An IoT-Based Intelligent Wound Monitoring System

et al. 2019

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Clinical research of wound assessment focused on physical appearance of wound i.e. wound width, shape, color etc. Although, wound appearance is most crucial factors to influence healing process. however, apart from wound appearance other factors also contribute in healing process. Wound internal and external environment is one such factor that may show positive or negative impact on healing. Internet of things extensively popular during last decade, due to its heavy applications in almost all domains i.e. agriculture, health, marketing, banking, home etc. Therefore, in current research we proposed IoT based intelligent wound assessment system, for assessment of wound status and apply entropy and information gain statistics of decision tree to reflect status of wound assessment by categorization of assessment results in one of three class i.e. good, satisfactory or alarming. We implemented decision tree in MATLAB, in which we select ID3 algorithm for decision tree which based on entropy and information gain for the selection of best feature to split the tree. The efficient feature split of decision tree improved training accuracy rate and performance of decision tree.

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Section: State Of the Artmentioning

confidence: 99%

An IoT-Based Intelligent Wound Monitoring System

et al. 2019

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“…Sohini Roy Choudhury et al [13] designed wearable device to monitor wound healing with the help of a uric acid biosensor. Their proposed device detected Uric Acid (UA) from the wound.…”

Section: Features and Limitations Of Previous Researchmentioning

confidence: 99%

“…The matrix value showing the total predicted "favourable" classes was 89, out of which one class was truly unfavourable and wrongly predicted to be favourable, while the total unfavourable classes were 145, out of which 116 were predicted correctly, and 29 were truly favourable, but predicted to be unfavourable. By the given confusion matrix of the accuracy and error rate for both classes, prediction could be measured with the help of the formulae given in Equations (12) and (13).…”

Section: Evaluation Of the Trained Svm Classifiermentioning

confidence: 99%

“…IoT provides more flexible and low-cost solutions for daily life problems, which ultimately improve the user's life [4,5]. Although many previous researchers proposed air quality sensing systems by using different sensor combinations [6][7][8][9][10][11][12][13], as shown in Table 1, with the detailed description of previous studies provided, we may conclude that there are three reasons for the motivation to design an efficient wound monitoring system.…”

Section: Introductionmentioning

confidence: 99%

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An Intelligent Air Quality Sensing System for Open-Skin Wound Monitoring

2019

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There are many factors that may have a significant effect on the skin wound healing process. The environment is one of them. Although different previous research woks have highlighted the role of environmental elements such as humidity, temperature, dust, etc., in the process of skin wound healing, there is no predefined method available to identify the favourable or adverse environment conditions that seriously affect (positively or negatively) the skin wound healing process. In the current research work, an IoT-based approach is used to design an AQSS (Air Quality Sensing System) using sensors for the acquisition of real-time environment data, and the SVM (Support Vector Machine) classifier is applied to classify environments into one of the two categories, i.e., "favourable", and "unfavourable". The proposed system is also supported with an Android application to provide an easy-to-use interface. The proposed system provides an easy and simple means for patients to evaluate the environmental parameters and monitor their effects in the process of open skin wound healing.

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“…Such sensor could become the basis of a POC device for chronic wound care. Several sensors to assess wound healing progression have been developed, targeting infection, pH, oxygen, uric acid, hemoglobin, and broad-spectrum proteases (Dargaville et al, 2013;Edwards et al, 2013;Sharp, 2013;Krismastuti et al, 2014Krismastuti et al, , 2015aKrismastuti et al, , 2017Ochoa et al, 2014;Salvo et al, 2015;Jankowska et al, 2017;RoyChoudhury et al, 2018). These sensors have not reached yet the stage of deployment in the clinic due to the challenges involved, such as the complex wound environment with hundreds of proteins present in a wide pH range.…”

Section: Introductionmentioning

confidence: 99%

Porous Alumina Membrane-Based Electrochemical Biosensor for Protein Biomarker Detection in Chronic Wounds

et al. 2020

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A label-free electrochemical detection platform for the sensitive and rapid detection of Flightless I (Flii) protein, a biomarker of wound chronicity, has been developed using nanoporous anodic alumina (NAA) membranes modified with Flii antibody recognition sites. The electrochemical detection is based on the nanochannel blockage experienced upon Flii capture by immobilized antibodies within the nanochannels. This capture impedes the diffusion of redox species [[Fe(CN) 6 ] 4−/3− ] toward a gold electrode attached at the backside of the modified NAA membrane. Partial blockage causes a decrease in the oxidation current of the redox species at the electrode surface which is used as an analytical signal by the reported biosensor. The resulting biosensing system allows detection of Flii at the levels found in wounds. Two types of assays were tested, sandwich and direct, showing <3 and 2 h analysis time, respectively, a significant reduction in time from the nearly 48 h required for the conventional Western blot assay. Slightly higher sensitivity values were observed for the sandwich-based strategy. With faster analysis, lack of matrix effects, robustness, ease of use and cost-effectiveness, the developed sensing platform has the potential to be translated into a point-of-care (POC) device for chronic wound management and as a simple alternative characterization tool in Flii research.

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Continuous Monitoring of Wound Healing Using a Wearable Enzymatic Uric Acid Biosensor

Cited by 87 publications

References 46 publications

An IoT-Based Intelligent Wound Monitoring System

An IoT-Based Intelligent Wound Monitoring System

An Intelligent Air Quality Sensing System for Open-Skin Wound Monitoring

Porous Alumina Membrane-Based Electrochemical Biosensor for Protein Biomarker Detection in Chronic Wounds

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