Literature DB >> 32053914

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

Anas M Tahir1, Muhammad E H Chowdhury1, Amith Khandakar1, Sara Al-Hamouz1, Merna Abdalla1, Sara Awadallah1, Mamun Bin Ibne Reaz2, Nasser Al-Emadi1.   

Abstract

Gait analysis is a systematic study of human locomotion, which can be utilized in variousapplications, such as rehabilitation, clinical diagnostics and sports activities. The various limitationssuch as cost, non-portability, long setup time, post-processing time etc., of the current gait analysistechniques have made them unfeasible for individual use. This led to an increase in research interestin developing smart insoles where wearable sensors can be employed to detect vertical groundreaction forces (vGRF) and other gait variables. Smart insoles are flexible, portable and comfortablefor gait analysis, and can monitor plantar pressure frequently through embedded sensors thatconvert 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 ofinsoles sensors, durability, and the intelligence of insoles to monitor and control subjects' gait bydetecting various complications providing recommendation to enhance walking performance. Eventhough systematic sensor calibration approaches have been followed by different teams to calibrateinsoles' sensor, expensive calibration devices were used for calibration such as universal testingmachines or infrared motion capture cameras equipped in motion analysis labs. This paper providesa systematic design and characterization procedure for three different pressure sensors: forcesensitiveresistors (FSRs), ceramic piezoelectric sensors, and flexible piezoelectric sensors that canbe used for detecting vGRF using a smart insole. A simple calibration method based on a load cellis presented as an alternative to the expensive calibration techniques. In addition, to evaluate theperformance of the different sensors as a component for the smart insole, the acquired vGRF fromdifferent insoles were used to compare them. The results showed that the FSR is the most effectivesensor among the three sensors for smart insole applications, whereas the piezoelectric sensors canbe utilized in detecting the start and end of the gait cycle. This study will be useful for any researchgroup in replicating the design of a customized smart insole for gait analysis.

Entities:  

Keywords:  characterization; force sensitive resistors; gait analysis; piezoelectric sensors; sensor calibration; smart insole; vertical ground reaction forces

Year:  2020        PMID: 32053914     DOI: 10.3390/s20040957

Source DB:  PubMed          Journal:  Sensors (Basel)        ISSN: 1424-8220            Impact factor:   3.576


  11 in total

1.  Estimating Ground Reaction Force and Center of Pressure Using Low-Cost Wearable Devices.

Authors:  Brandon Oubre; Spencer Lane; Skylar Holmes; Katherine Boyer; Sunghoon Ivan Lee
Journal:  IEEE Trans Biomed Eng       Date:  2022-03-18       Impact factor: 4.538

2.  Assessment System for Predicting Maximal Safe Range for Heel Height by Using Force-Sensing Resistor Sensors and Regression Models.

Authors:  Yi-Ting Hwang; Si-Huei Lee; Bor-Shing Lin
Journal:  Sensors (Basel)       Date:  2022-04-30       Impact factor: 3.847

3.  Machine Learning Strategies for Low-Cost Insole-Based Prediction of Center of Gravity during Gait in Healthy Males.

Authors:  Jose Moon; Dongjun Lee; Hyunwoo Jung; Ahnryul Choi; Joung Hwan Mun
Journal:  Sensors (Basel)       Date:  2022-05-04       Impact factor: 3.847

4.  Automated Detection of COVID-19 Cases on Radiographs using Shape-Dependent Fibonacci-p Patterns.

Authors:  Karen Panetta; Foram Sanghavi; Sos Agaian; Neel Madan
Journal:  IEEE J Biomed Health Inform       Date:  2021-06-03       Impact factor: 7.021

5.  Deep Learning in the Detection and Diagnosis of COVID-19 Using Radiology Modalities: A Systematic Review.

Authors:  Mustafa Ghaderzadeh; Farkhondeh Asadi
Journal:  J Healthc Eng       Date:  2021-03-15       Impact factor: 2.682

6.  Deep Residual Neural Network for COVID-19 Detection from Chest X-ray Images.

Authors:  Amirhossein Panahi; Reza Askari Moghadam; Mohammadreza Akrami; Kurosh Madani
Journal:  SN Comput Sci       Date:  2022-02-21

Review 7.  Recent Trends and Practices Toward Assessment and Rehabilitation of Neurodegenerative Disorders: Insights From Human Gait.

Authors:  Ratan Das; Sudip Paul; Gajendra Kumar Mourya; Neelesh Kumar; Masaraf Hussain
Journal:  Front Neurosci       Date:  2022-04-15       Impact factor: 5.152

Review 8.  Detection and assessment of Parkinson's disease based on gait analysis: A survey.

Authors:  Yao Guo; Jianxin Yang; Yuxuan Liu; Xun Chen; Guang-Zhong Yang
Journal:  Front Aging Neurosci       Date:  2022-08-03       Impact factor: 5.702

9.  Design of a Plantar Pressure Insole Measuring System Based on Modular Photoelectric Pressure Sensor Unit.

Authors:  Bin Ren; Jianwei Liu
Journal:  Sensors (Basel)       Date:  2021-05-29       Impact factor: 3.576

10.  Deep convolutional neural networks for COVID-19 automatic diagnosis.

Authors:  Heba M Emara; Mohamed R Shoaib; Mohamed Elwekeil; Walid El-Shafai; Taha E Taha; Adel S El-Fishawy; El-Sayed M El-Rabaie; Saleh A Alshebeili; Moawad I Dessouky; Fathi E Abd El-Samie
Journal:  Microsc Res Tech       Date:  2021-06-14       Impact factor: 2.893

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.