Novel dynamic peak and distribution plantar pressure measures on diabetic patients during walking

Al-Angari, Haitham M.; Khandoker, Ahsan H.; Lee, Sungmun; Almahmeed, Wael; Safar, Habiba S. Al; Jelinek, Herbert F.; Khalaf, Kinda

doi:10.1016/j.gaitpost.2016.11.006

Cited by 20 publications

(10 citation statements)

References 22 publications

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“…Yavuz et al suggest that pressure has a low predictive value given only 38% of plantar ulcers develop at peak pressure points 47,48 rather than peak shear locations. 49,50 New pressure variables continue to emerge 51,52 which remain the subject of debate 25 but, as with temperature, there is a strong argument not to rely on pressure in isolation but to analyze combined microclimate parameters when assessing DFU risk.…”

Section: Pressurementioning

confidence: 99%

Prediction of Diabetic Foot Ulceration: The Value of Using Microclimate Sensor Arrays

Jones

Bibb

Davies

et al. 2019

J Diabetes Sci Technol

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Background: Accurately predicting the risk of diabetic foot ulceration (DFU) could dramatically reduce the enormous burden of chronic wound management and amputation. Yet, the current prognostic models are unable to precisely predict DFU events. Typically, efforts have focused on individual factors like temperature, pressure, or shear rather than the overall foot microclimate. Methods: A systematic review was conducted by searching PubMed reports with no restrictions on start date covering the literature published until February 20, 2019 using relevant keywords, including temperature, pressure, shear, and relative humidity. We review the use of these variables as predictors of DFU, highlighting gaps in our current understanding and suggesting which specific features should be combined to develop a real-time microclimate prognostic model. Results: The current prognostic models rely either solely on contralateral temperature, pressure, or shear measurement; these parameters, however, rarely reach 50% specificity in relation to DFU. There is also considerable variation in methodological investigation, anatomical sensor configuration, and resting time prior to temperature measurements (5-20 minutes). Few studies have considered relative humidity and mean skin resistance. Conclusion: Very limited evidence supports the use of single clinical parameters in predicting the risk of DFU. We suggest that the microclimate as a whole should be considered to predict DFU more effectively and suggest nine specific features which appear to be implicated for further investigation. Technology supports real-time in-shoe data collection and wireless transmission, providing a potentially rich source of data to better predict the risk of DFU.

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Section: Pressurementioning

confidence: 99%

Prediction of Diabetic Foot Ulceration: The Value of Using Microclimate Sensor Arrays

Jones

Bibb

Davies

et al. 2019

J Diabetes Sci Technol

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“…Al-Angari et al used measures of shape and entropy to introduce new characteristics for capturing the variations in plantar pressure in a study of patients with DPN, retinopathy, and nephropathy compared with a diabetic control group without complications. e change in the position of the peak pressure of the plant with each step for both feet was represented as a convex polygon, asymmetry index, area of the convex polygon, second wavelet moment, and entropy of the sample [25].…”

Section: Computational and Mathematical Methods In Medicinementioning

confidence: 99%

Gait Biomarkers Classification by Combining Assembled Algorithms and Deep Learning: Results of a Local Study

Sánchez-DelaCruz

Weber

Biswal

et al. 2019

Computational and Mathematical Methods in Medicine

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Machine learning, one of the core disciplines of artificial intelligence, is an approach whose main emphasis is analytical model building. In other words, machine learning enables an automaton to make its own decisions based on a previous training process. Machine learning has revolutionized every research sector, including health care, by providing precise and accurate decisions involving minimal human interventions through pattern recognition. This is emphasized in this research, which addresses the issue of “support for diabetic neuropathy (DN) recognition.” DN is a disease that affects a large proportion of the global population. In this research, we have used gait biomarkers of subjects representing a particular sector of population located in southern Mexico to identify persons suffering from DN. To do this, we used a home-made body sensor network to capture raw data of the walking pattern of individuals with and without DN. The information was then processed using three sampling criteria and 23 assembled classifiers, in combination with a deep learning algorithm. The architecture of the best combination was chosen and reconfigured for better performance. The results revealed a highly acceptable classification with greater than 85% accuracy when using these combined approaches.

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“…As the volume of data from the variety of nowadays readily available body sensors used to quantify human gait and movement, including electroencephalography, electro-oculography, electro-cardiography, and electromyography video and force plate data, substantially increases, more sophisticated modeling is needed to quantify and interpret complex network physiology ( 157 ). In addition, today's advanced use of computer science has established novel features describing gait movement associated biomechanics, moving from discrete data to more realistic dynamic representations ( 158 , 159 ). Multivariate statistical analysis, machine learning methods including Support Vector Machines (SVM) have been recently extended to Deep Artificial Neural Networks such as Layer-wise Relevance Propagation (LRP) methods to provide numerical data on the contributions of variables included in the model ( 160 ).…”

Section: Gait Analysis and Artificial Intelligencementioning

confidence: 99%

Assessment Methods of Post-stroke Gait: A Scoping Review of Technology-Driven Approaches to Gait Characterization and Analysis

Mohan

Khandoker

Wasti³

et al. 2021

Front. Neurol.

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Background: Gait dysfunction or impairment is considered one of the most common and devastating physiological consequences of stroke, and achieving optimal gait is a key goal for stroke victims with gait disability along with their clinical teams. Many researchers have explored post stroke gait, including assessment tools and techniques, key gait parameters and significance on functional recovery, as well as data mining, modeling and analyses methods.Research Question: This study aimed to review and summarize research efforts applicable to quantification and analyses of post-stroke gait with focus on recent technology-driven gait characterization and analysis approaches, including the integration of smart low cost wearables and Artificial Intelligence (AI), as well as feasibility and potential value in clinical settings.Methods: A comprehensive literature search was conducted within Google Scholar, PubMed, and ScienceDirect using a set of keywords, including lower extremity, walking, post-stroke, and kinematics. Original articles that met the selection criteria were included.Results and Significance: This scoping review aimed to shed light on tools and technologies employed in post stroke gait assessment toward bridging the existing gap between the research and clinical communities. Conventional qualitative gait analysis, typically used in clinics is mainly based on observational gait and is hence subjective and largely impacted by the observer's experience. Quantitative gait analysis, however, provides measured parameters, with good accuracy and repeatability for the diagnosis and comparative assessment throughout rehabilitation. Rapidly emerging smart wearable technology and AI, including Machine Learning, Support Vector Machine, and Neural Network approaches, are increasingly commanding greater attention in gait research. Although their use in clinical settings are not yet well leveraged, these tools promise a paradigm shift in stroke gait quantification, as they provide means for acquiring, storing and analyzing multifactorial complex gait data, while capturing its non-linear dynamic variability and offering the invaluable benefits of predictive analytics.

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Novel dynamic peak and distribution plantar pressure measures on diabetic patients during walking

Cited by 20 publications

References 22 publications

Prediction of Diabetic Foot Ulceration: The Value of Using Microclimate Sensor Arrays

Prediction of Diabetic Foot Ulceration: The Value of Using Microclimate Sensor Arrays

Gait Biomarkers Classification by Combining Assembled Algorithms and Deep Learning: Results of a Local Study

Assessment Methods of Post-stroke Gait: A Scoping Review of Technology-Driven Approaches to Gait Characterization and Analysis

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