Designing a Subsystem for Creating a Three-dimensional Model of an Orthopedic Insole Based on Data from a Laser 3D Scanning of the Patient's Feet

Voronov, Vyacheslav I.; Dovgolevskiy, P. A.

doi:10.1109/emctech49634.2020.9261530

Cited by 6 publications

(3 citation statements)

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“…The authors stated, based on the obtained results, that the system achieved a higher measurement accuracy with lower cost and stronger environmental adaptability. Besides, a subsystem to create a three-dimensional model of an orthopedic insole, based on 3D laser scanning data, was presented in [8]. The authors used an adversarial neural network to model the orthopedic insoles.…”

Section: State Of the Artmentioning

confidence: 99%

Three-dimensional scanning system based on a low-cost infrared sensor

Braun

Lima

Pereira

et al. 2021

2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA )

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Nowadays, with the availability of 3D printers, the scanners for objects are becoming increasingly present since they allow to replicate objects by 3D printing, especially for small scale sizes. However, the majority of these technologies are expensive, due to the complexity of this task. Therefore, this work presents a prototype of a low-cost 3D scanning system for small objects using a point cloud to stereolithography approach where it was already validated in simulation in previous work. This concept has a restriction that the objects must have a uniform shape, i.e, without discontinuities. The architecture is composed of two stepper motors, due to their precision, a rotating plate to allow 360 degrees scans and another rotating structure that allows the infrared distance sensor to scan the object from bottom to top (90 degrees). The prototype was validated in the real scenario with good results.

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Section: State Of the Artmentioning

confidence: 99%

Three-dimensional scanning system based on a low-cost infrared sensor

Braun

Lima

Pereira

et al. 2021

2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA )

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“…The role of artificial intelligence (AI) approaches cannot be neglected when the task is to handle complicated data sets and to predict specific inputs over which optimum results can be achieved (Dhankhar et al, 2019; Kumar et al, 2019; Badhwar et al, 2020). A considerable impact of the hybrid statistical tools GA-RSM and GA-artificial neural network (ANN) is also evident in predicting the mechanical behavior of 3D printed parts (Deshwal et al, 2020; Kumar et al, 2022); hence, the probability of getting precise results becomes very high if 3D-SA is patched with artificial machine learning statics (Nayak and Das, 2020; Voronov and Dovgolevskiy, 2020).…”

Section: Introductionmentioning

confidence: 99%

Hybrid machine learning approach for accurate and expeditious 3D scanning to enhance rapid prototyping reliability in orthotics using RSM-RSMOGA-MOGANN

Kumar,

Chhabra

2024

AIEDAM

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This study aims to develop a multidisciplinary artificial hybrid machine learning (AHML) approach to reduce the scanning time (ST) of the human wrist and improve the accuracy of 3D scanning for anthropometric data collection. A systematic AHML approach was deployed to scan the human wrist distal end optimally using a portable SENSE 2.0 3D scanner. A central composite design (CCD) matrix was developed for three input variables; light intensity (LI = 12–20 W/m2), capture angle (CA = 10°–50°), and scanning distance (SD = 10–20 inches) for executing the experimental runs. For accuracy evaluation, the wrist perimeter on the distal end was checked using CREO Parametric software for wrist perimeter error (WPE). Various AHML tools were developed using: response surface methodology (RSM), multi-objective genetic algorithm RSM, and multi-objective genetic algorithm neural networking (MOGANN). The optimal process parameters recommended by the hybrid tools were experimentally validated for their prediction accuracy. The MOGANN approach combined with the Bayesian regularization algorithm (trainabr) provided the best mutual combination of optimal ST = 20.072 sec and WPE = 0.375 cm corresponding to LI = 12.001 W/m2, CA = 29.428°, and SD = 18.214 inch, with a significant percentage reduction of 55.83% in WPE. Executing 3D scanning of the human wrist over the optimized process parameters predicted by AHML tools will ensure the availability of precise scans for the rapid prototyping of customized orthotic devices in a reliable manner.

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“…A fully 3D printed, capacitive-sensing insole would provide a cost-effective means to create insoles that could be customized to fit the shape and size of a patient's foot while also controlling the sensor placement within the insole. Previous work by Voronov and Dovgolevskiy demonstrated a method to design a customized insole based on a 3D scan of a patient's foot and an adversarial neural network [29]. A similar approach could be used to customize the shape of a pressure-sensing insole to match a patient's footbed by scanning the patient's plantar surface into a point cloud, converting it into an STL file, inserting a 3D printed sensor, and developing wire geometry in the insole.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Characterization of a Low-Cost Fully and Continuously 3D Printed Capacitive Pressure-Sensing System for Plantar Pressure Measurements

Gothard,

Hott,

Anton

2023

Sensors

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In orthopedics, the evaluation of footbed pressure distribution maps is a valuable gait analysis technique that aids physicians in diagnosing musculoskeletal and gait disorders. Recently, the use of pressure-sensing insoles to collect pressure distributions has become more popular due to the passive collection of natural gait data during daily activities and the reduction in physical strain experienced by patients. However, current pressure-sensing insoles face the limitations of low customizability and high cost. Previous works have shown the ability to construct customizable pressure-sensing insoles with capacitive sensors using fused-deposition modeling (FDM) 3D printing. This work explores the feasibility of low-cost fully and continuously 3D printed pressure sensors for pressure-sensing insoles using three sensor designs, which use flexible thermoplastic polyurethane (TPU) as the dielectric layer and either conductive TPU or conductive polylactic acid (PLA) for the conductive plates. The sensors are paired with a commercial capacitance-to-voltage converter board to form the sensing system. Dynamic sensor performance is evaluated via sinusoidal compressive tests at frequencies of 1, 3, 5, and 7 Hz, with pressure levels varying from 14.33 to 23.88, 33.43, 52.54, and 71.65 N/cm2 at each frequency. Five sensors of each type are tested. Results show that all sensors display significant hysteresis and nonlinearity. The PLA-TPU sensor with 10% infill is the best-performing sensor with the highest average sensitivity and lowest average hysteresis and linearity errors. The range of average sensitivities, hysteresis, and linearity errors across the entire span of tested pressures and frequencies for the PLA-TPU sensor with 10% infill is 11.61–20.11·10−4 V/(N/cm2), 11.9–31.8%, and 9.0–22.3%, respectively. The significant hysteresis and linearity error are due to the viscoelastic properties of TPU, and some additional nonlinear effects may be due to buckling of the infill walls of the dielectric.

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Designing a Subsystem for Creating a Three-dimensional Model of an Orthopedic Insole Based on Data from a Laser 3D Scanning of the Patient's Feet

Cited by 6 publications

References 10 publications

Three-dimensional scanning system based on a low-cost infrared sensor

Three-dimensional scanning system based on a low-cost infrared sensor

Hybrid machine learning approach for accurate and expeditious 3D scanning to enhance rapid prototyping reliability in orthotics using RSM-RSMOGA-MOGANN

Dynamic Characterization of a Low-Cost Fully and Continuously 3D Printed Capacitive Pressure-Sensing System for Plantar Pressure Measurements

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