Rapid Detection of Foot-and-Mouth Disease Virus with Optical Microchip Sensors

Bhatta, D.; Villalba, Montalvo; Johnson, Christian L.; Emmerson, G.D.; Ferris, N.P.; King, Donald P.; Lowe, Christopher R.

doi:10.1016/j.proche.2012.10.124

Cited by 17 publications

(8 citation statements)

References 18 publications

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“…[3]. By collecting the dynamic pressure distribution of the sole, the biomechanical properties of shoes can be evaluated, such as cushioning performance, support strength, stability and so on [4] [5]. By previous works, the results indicated that foot zoning, pressure, press, risk assessment of diabetic foot ulcer, load change rate, foot length and width, foot contact area, foot angle, foot axis, and time track are used for stability analysis [6] [7].…”

Section: Introductionmentioning

confidence: 99%

An efficient local binary pattern based plantar pressure optical sensor image classification using convolutional neural networks

Wang

et al. 2019

Optik

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The objective of this study was to design and produce highly comfortable shoe products guided by a plantar pressure imaging data-set. Previous studies have focused on the geometric measurement on the size of the plantar, while in this research a plantar pressure optical imaging data-set based classification technology has been developed. In this paper, an improved local binary pattern (LBP) algorithm is used to extract texture-based features and recognize patterns from the data-set. A calculating model of plantar pressure imaging feature area is established subsequently. The data-set is classified by a neural network to guide the generation of various shoe-last surfaces. Firstly, the local binary mode is improved to adapt to the pressure imaging data-set, and the texture-based feature calculation is fully used to accurately generate the feature point set; hereafter, the plantar pressure imaging feature point set is then used to guide the design of last free surface forming. In the presented experiments of plantar imaging, multi-dimensional texture-based features and improved LBP features have been found by a convolution neural network (CNN), and compared with a 21-input-3-output two-layer perceptual neural network. Three feet types are investigated in the experiment, being flatfoot (F) referring to the lack of a normal arch, or arch collapse, Talipes Equinovarus (TE), being the front part of the foot is adduction, calcaneus varus, plantar flexion, or Achilles tendon contracture and Normal (N). This research has achieved an 82% accuracy rate with 10 hidden-layers CNN of rotation invariance LBP (RI-LBP) algorithm using 21 texture-based features by comparing other deep learning methods presented in the literature.

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Section: Introductionmentioning

confidence: 99%

An efficient local binary pattern based plantar pressure optical sensor image classification using convolutional neural networks

Wang

et al. 2019

Optik

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“…Biosensors work on various principles viz detecting the changes in the pH, the ion concentrations, mass by specific hybridization, enzymatic reaction, loss of functionality, change in the electrical potential, change in color, and temperature. Based on these principles, many biosensors have been devised for the detection of animal pathogens; for example, an extended-gate field-effect transistor for the direct potentiometric serological diagnosis of the BHV-1 (Tarasov et al, 2016), nanowire-based immunosensor for bovine viral diarrhea virus (BVDV) (Montrose et al, 2015), luminescence resonance energy transferÀbased biosensors for the ultrasensitive detection of the H7 strain (Ye et al, 2014b), quartz crystal microbalance (QCM)Àbased immunosensors to detect H5N1 (Li et al, 2011), and SpectroSensTM optical microchip sensors for foot-and-mouth disease virus (FMDV) (Bhatta et al, 2012).…”

Section: Biosensorsmentioning

confidence: 99%

Biotechnological innovations in farm and pet animal disease diagnosis

Malik

Verma

Kumar

et al. 2020

Genomics and Biotechnological Advances in Veterinary, Poultry, and Fisheries

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“…Recently, rapid identification of FMDV has been reported using SpectroSens™ optical microchip sensors. Selective identification of FMDV is conducted in minutes and displayed as a yes/no readout using a hand-held device (Bhatta et al, 2012). Infrared thermography (IRT), a quantitative method for the assessment of body surface temperature, can be useful for the early detection of FMDV in the field.…”

Section: Biosensors For Animal Diseasesmentioning

confidence: 99%