Evaluation of Flexible Force Sensors for Pressure Monitoring in Treatment of Chronic Venous Disorders

Parmar, Suresh; Khodasevych, Iryna; Troynikov, Olga

doi:10.3390/s17081923

Cited by 57 publications

(85 citation statements)

References 36 publications

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“…This study was performed using two low-cost commercially available thin and flexible piezoresistive force sensors: the Peratech quantum tunnelling composite (QTC) TM SP 200-10 sensor and the Sensitronics ® Half Inch ThruMode FSR ( Figure 1 ). These sensors were selected on the basis of a previous study [ 25 ] that determined the most suitable and the best-performing of five pressure sensors suitable for compression therapy applications.…”

Section: Methodsmentioning

confidence: 99%

“…We investigated the effect of changes in underlying surfaces on the pressure sensing performance of two commercially available sensors on a human-leg-like test setup that is representative of human soft tissue surfaces. The sensors were selected from a wide range of commercial sensors based on our previous research [ 25 ], which demonstrated that they were the most suitable for compression therapy. The pressure measurements were conducted in a low pressure range (20–70 mmHg) appropriate for compression therapy treatment.…”

Section: Introductionmentioning

confidence: 99%

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Flexible Sensors for Pressure Therapy: Effect of Substrate Curvature and Stiffness on Sensor Performance

Khodasevych

Parmar

Troynikov

2017

Sensors

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Flexible pressure sensors are increasingly being used in medical and non-medical applications, and particularly in innovative health monitoring. Their efficacy in medical applications such as compression therapy depends on the accuracy and repeatability of their output, which in turn depend on factors such as sensor type, shape, pressure range, and conformability of the sensor to the body surface. Numerous researchers have examined the effects of sensor type and shape, but little information is available on the effect of human body parameters such as support surfaces’ curvature and the stiffness of soft tissues on pressure sensing performance. We investigated the effects of body parameters on the performance of pressure sensors using a custom-made human-leg-like test setup. Pressure sensing parameters such as accuracy, drift and repeatability were determined in both static (eight hours continuous pressure) and dynamic (10 cycles of pressure application of 30 s duration) testing conditions. The testing was performed with a focus on compression therapy application for venous leg ulcer treatments, and was conducted in a low-pressure range of 20–70 mmHg. Commercially available sensors manufactured by Peratech and Sensitronics were used under various loading conditions to determine the influence of stiffness and curvature. Flat rigid, flat soft silicone and three cylindrical silicone surfaces of radii of curvature of 3.5 cm, 5.5 cm and 6.5 cm were used as substrates under the sensors. The Peratech sensor averaged 94% accuracy for both static and dynamic measurements on all substrates; the Sensitronics sensor averaged 88% accuracy. The Peratech sensor displayed moderate variations and the Sensitronics sensor large variations in output pressure readings depending on the underlying test surface, both of which were reduced markedly by individual pressure calibration for surface type. Sensor choice and need for calibration to surface type are important considerations for their application in healthcare monitoring.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Flexible Sensors for Pressure Therapy: Effect of Substrate Curvature and Stiffness on Sensor Performance

Khodasevych

Parmar

Troynikov

2017

Sensors

Self Cite

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“…Marco et al [5] performed the sensor calibrations using robotic platform that can precisely apply controllable loads to the desired positions. Parmar et al [37] evaluated the performance of 5 different commercial FSRs during static and dynamic loading with reliable test setups that can mimic realistic conditions when applying pressure on human limbs. The sensors were evaluated quantitatively based on their accuracy, drift, and repeatability behaviors.…”

Section: Literature Reviewmentioning

confidence: 99%

A Systematic Approach to the Design and Characterization of a Smart Insole for Detecting Vertical Ground Reaction Force (vGRF) in Gait Analysis

Tahir

Chowdhury

Khandakar

et al. 2020

Sensors

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Gait analysis is a systematic study of human locomotion, which can be utilized in various applications, such as rehabilitation, clinical diagnostics and sports activities. The various limitations such as cost, non-portability, long setup time, post-processing time etc., of the current gait analysis techniques have made them unfeasible for individual use. This led to an increase in research interest in developing smart insoles where wearable sensors can be employed to detect vertical ground reaction forces (vGRF) and other gait variables. Smart insoles are flexible, portable and comfortable for gait analysis, and can monitor plantar pressure frequently through embedded sensors that convert the applied pressure to an electrical signal that can be displayed and analyzed further. Several research teams are still working to improve the insoles’ features such as size, sensitivity of insoles sensors, durability, and the intelligence of insoles to monitor and control subjects’ gait by detecting various complications providing recommendation to enhance walking performance. Even though systematic sensor calibration approaches have been followed by different teams to calibrate insoles’ sensor, expensive calibration devices were used for calibration such as universal testing machines or infrared motion capture cameras equipped in motion analysis labs. This paper provides a systematic design and characterization procedure for three different pressure sensors: force-sensitive resistors (FSRs), ceramic piezoelectric sensors, and flexible piezoelectric sensors that can be used for detecting vGRF using a smart insole. A simple calibration method based on a load cell is presented as an alternative to the expensive calibration techniques. In addition, to evaluate the performance of the different sensors as a component for the smart insole, the acquired vGRF from different insoles were used to compare them. The results showed that the FSR is the most effective sensor among the three sensors for smart insole applications, whereas the piezoelectric sensors can be utilized in detecting the start and end of the gait cycle. This study will be useful for any research group in replicating the design of a customized smart insole for gait analysis.

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“…To calculate the sensitivity of the sensor, the estimated force values on the test experiments are considered over all folds. Flexible piezoresistive sensors often have a lower accuracy and high level of noise within the low range of the applied force [49]. Therefore, all the data points with the force value of less than 0.2 N are eliminated for the purpose of sensitivity calculation.…”

Section: Force Estimationmentioning

confidence: 99%

A Novel Dynamic-Vision-Based Approach for Tactile Sensing Applications

Naeini

Alali

Al-Husari

et al. 2020

IEEE Trans. Instrum. Meas.

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In this paper, a novel Vision-Based Measurement (VBM) approach is proposed to estimate the contact force and classify materials in a single grasp. This approach is the first event-based tactile sensor which utilizes the recent technology of neuromorphic cameras. This novel approach provides a higher sensitivity, a lower latency, and less computational and power consumption compared to other conventional visionbased techniques. Moreover, Dynamic Vision Sensor (DVS) has a higher dynamic range which increases the sensor sensitivity and performance in poor lighting conditions. Two time-series machine learning methods, namely, Time Delay Neural Network (TDNN) and Gaussian Process (GP) are developed to estimate the contact force in a grasp. A Deep Neural Network (DNN) is proposed to classify the object materials. Forty-eight experiments are conducted for four different materials to validate the proposed methods and compare them against a piezoresistive force sensor measurements. A leave-one-out cross-validation technique is implemented to evaluate and analyze the performance of the proposed machine learning methods. The contact force is successfully estimated with a mean squared error of 0.16 N and 0.17 N for TDNN and GP respectively. Four materials are classified with an average accuracy of 79.17% using unseen experimental data. The results show the applicability of eventbased sensors for grasping applications.

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Evaluation of Flexible Force Sensors for Pressure Monitoring in Treatment of Chronic Venous Disorders

Cited by 57 publications

References 36 publications

Flexible Sensors for Pressure Therapy: Effect of Substrate Curvature and Stiffness on Sensor Performance

Flexible Sensors for Pressure Therapy: Effect of Substrate Curvature and Stiffness on Sensor Performance

A Systematic Approach to the Design and Characterization of a Smart Insole for Detecting Vertical Ground Reaction Force (vGRF) in Gait Analysis

A Novel Dynamic-Vision-Based Approach for Tactile Sensing Applications

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