scholarly journals Pilot study: Assessing repeatability of the EcoWalk platform resistive pressure sensors to measure plantar pressure during barefoot standing

2013 ◽  
Vol 450 ◽  
pp. 012029
Author(s):  
Martha Zequera ◽  
Oscar Perdomo ◽  
Carlos Wilches ◽  
Pedro Vizcaya
Keyword(s):  
Pilot Study ◽  
Plantar Pressure ◽  
Pressure Sensors

scholarly journals Early Detection of Freezing of Gait during Walking Using Inertial Measurement Unit and Plantar Pressure Distribution Data

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2246
Author(s):  
Scott Pardoel ◽  
Gaurav Shalin ◽  
Julie Nantel ◽  
Edward D. Lemaire ◽  
Jonathan Kofman
Keyword(s):  
Early Detection ◽  
Plantar Pressure ◽  
Inertial Measurement Unit ◽  
Binary Classification ◽  
Freezing Of Gait ◽  
Pressure Sensors ◽  
Measurement Unit ◽  
Distribution Data ◽  
Inertial Measurement ◽  
Plantar Pressure Distribution

Freezing of gait (FOG) is a sudden and highly disruptive gait dysfunction that appears in mid to late-stage Parkinson’s disease (PD) and can lead to falling and injury. A system that predicts freezing before it occurs or detects freezing immediately after onset would generate an opportunity for FOG prevention or mitigation and thus enhance safe mobility and quality of life. This research used accelerometer, gyroscope, and plantar pressure sensors to extract 861 features from walking data collected from 11 people with FOG. Minimum-redundancy maximum-relevance and Relief-F feature selection were performed prior to training boosted ensembles of decision trees. The binary classification models identified Total-FOG or No FOG states, wherein the Total-FOG class included data windows from 2 s before the FOG onset until the end of the FOG episode. Three feature sets were compared: plantar pressure, inertial measurement unit (IMU), and both plantar pressure and IMU features. The plantar-pressure-only model had the greatest sensitivity and the IMU-only model had the greatest specificity. The best overall model used the combination of plantar pressure and IMU features, achieving 76.4% sensitivity and 86.2% specificity. Next, the Total-FOG class components were evaluated individually (i.e., Pre-FOG windows, Freeze windows, transition windows between Pre-FOG and Freeze). The best model detected windows that contained both Pre-FOG and FOG data with 85.2% sensitivity, which is equivalent to detecting FOG less than 1 s after the freeze began. Windows of FOG data were detected with 93.4% sensitivity. The IMU and plantar pressure feature-based model slightly outperformed models that used data from a single sensor type. The model achieved early detection by identifying the transition from Pre-FOG to FOG while maintaining excellent FOG detection performance (93.4% sensitivity). Therefore, if used as part of an intelligent, real-time FOG identification and cueing system, even if the Pre-FOG state were missed, the model would perform well as a freeze detection and cueing system that could improve the mobility and independence of people with PD during their daily activities.

scholarly journals Development of smart insole for cycle time measurement in sewing process

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Eung Tae Kim ◽  
Sungmin Kim
Keyword(s):  
Cycle Time ◽  
Plantar Pressure ◽  
Capacity Planning ◽  
Pressure Sensors ◽  
Good Alternative ◽  
Labor Cost ◽  
Cost Calculation ◽  
Plantar Pressure Distribution ◽  
Operation Cycle ◽  
Smart Insole

AbstractA smart insole system consisting of pressure sensors, wireless communication modules, and pressure monitoring software has been developed to measure plantar pressure distribution that appears in sewing process. This system calculates the cycle time of each operation by analyzing the real-time plantar pressure data. The operation cycle time was divided into the time done by machine and by manual and calculated by adding the two types of time. By analyzing the cycle time, it is possible to estimate the type of operation a worker is performing. The ability to calculate accurate cycle time and to manage a large volume of data is the advantage of this system. Establishing an accurate cycle time of all operations would be of great help in improving the production process, capacity planning, line efficiency, and labor cost calculation. The system is expected to be a good alternative to the conventional manual measurement process. It will also be able to meet the high demand from garment manufacturers for automated monitoring systems.

scholarly journals A Development of Plantar Pressure Sensor for Foot Ulcer Detection in Diabetic Neuropathy Individuals – A Pilot Study

2020 ◽  
Vol 1535 ◽  
pp. 012019
Author(s):  
Nor Salwa Damanhuri ◽  
Nor Azlan Othman ◽  
Wan Fatimah Azzahra Wan Zaidi ◽  
Samihah Abdullah
Keyword(s):  
Pilot Study ◽  
Diabetic Neuropathy ◽  
Pressure Sensor ◽  
Plantar Pressure ◽  
Foot Ulcer ◽  
Ulcer Detection

Predicting Complete Ground Reaction Forces and Moments During Gait With Insole Plantar Pressure Information Using a Wavelet Neural Network

2015 ◽  
Vol 137 (9) ◽  
Author(s):  
Taeyong Sim ◽  
Hyunbin Kwon ◽  
Seung Eel Oh ◽  
Su-Bin Joo ◽  
Ahnryul Choi ◽  
...  
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Plantar Pressure ◽  
Prediction Models ◽  
Pressure Sensors ◽  
Principal Component ◽  
Wavelet Neural Network ◽  
Human Gait ◽  
Ground Reaction Forces ◽  
Reaction Forces

In general, three-dimensional ground reaction forces (GRFs) and ground reaction moments (GRMs) that occur during human gait are measured using a force plate, which are expensive and have spatial limitations. Therefore, we proposed a prediction model for GRFs and GRMs, which only uses plantar pressure information measured from insole pressure sensors with a wavelet neural network (WNN) and principal component analysis-mutual information (PCA-MI). For this, the prediction model estimated GRFs and GRMs with three different gait speeds (slow, normal, and fast groups) and healthy/pathological gait patterns (healthy and adolescent idiopathic scoliosis (AIS) groups). Model performance was validated using correlation coefficients (r) and the normalized root mean square error (NRMSE%) and was compared to the prediction accuracy of the previous methods using the same dataset. As a result, the performance of the GRF and GRM prediction model proposed in this study (slow group: r = 0.840–0.989 and NRMSE% = 10.693–15.894%; normal group: r = 0.847–0.988 and NRMSE% = 10.920–19.216%; fast group: r = 0.823–0.953 and NRMSE% = 12.009–20.182%; healthy group: r = 0.836–0.976 and NRMSE% = 12.920–18.088%; and AIS group: r = 0.917–0.993 and NRMSE% = 7.914–15.671%) was better than that of the prediction models suggested in previous studies for every group and component (p < 0.05 or 0.01). The results indicated that the proposed model has improved performance compared to previous prediction models.

scholarly journals MEMS Technology Sensors as a More Advantageous Technique for Measuring Foot Plantar Pressure and Balance in Humans

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Clara Sanz Morère ◽  
Łukasz Surażyński ◽  
Ana Rodrigo Pérez-Tabernero ◽  
Erkki Vihriälä ◽  
Teemu Myllylä
Keyword(s):  
Plantar Pressure ◽  
Microelectromechanical Systems ◽  
Human Life ◽  
Pressure Sensors ◽  
High Mobility ◽  
Pressure Measurements ◽  
Mems Sensor ◽  
Research Problems ◽  
Mems Technology ◽  
Footwear Design

Locomotor activities are part and parcel of daily human life. During walking or running, feet are subjected to high plantar pressure, leading sometimes to limb problems, pain, or foot ulceration. A current objective in foot plantar pressure measurements is developing sensors that are small in size, lightweight, and energy efficient, while enabling high mobility, particularly for wearable applications. Moreover, improvements in spatial resolution, accuracy, and sensitivity are of interest. Sensors with improved sensing techniques can be applied to a variety of research problems: diagnosing limb problems, footwear design, or injury prevention. This paper reviews commercially available sensors used in foot plantar pressure measurements and proposes the utilization of pressure sensors based on the MEMS (microelectromechanical systems) technique. Pressure sensors based on this technique have the capacity to measure pressure with high accuracy and linearity up to high pressure levels. Moreover, being small in size, they are highly suitable for this type of measurement. We present two MEMS sensor models and study their suitability for the intended purpose by performing several experiments. Preliminary results indicate that the sensors are indeed suitable for measuring foot plantar pressure. Importantly, by measuring pressure continuously, they can also be utilized for body balance measurements.

scholarly journals THE INFLUENCE OF SLOW RECOVERY INSOLE ON PLANTAR PRESSURE AND CONTACT AREA DURING WALKING

2015 ◽  
Vol 15 (02) ◽  
pp. 1540005
Author(s):  
ROOZBEH NAEMI ◽  
KIMBERLEY LINYARD-TOUGH ◽  
AOIFE HEALY ◽  
NACHIAPPAN CHOCKALINGAM
Keyword(s):  
Contact Pressure ◽  
Contact Area ◽  
Plantar Pressure ◽  
Pressure Sensors ◽  
Ethylene Vinyl Acetate ◽  
Significant Negative Correlation ◽  
Mechanical Trauma ◽  
Slow Recovery ◽  
Walking Gait ◽  
Positive Significant Correlation

Plantar pressure assessment is commonly used as a tool to assess the efficacy of insoles in reducing the risk of mechanical trauma to the plantar soft tissue during walking gait. The slow rebound (SR) Poron insole is intended to provide a custom fit to the foot and is believed to be superior in increasing the contact area and consequently reducing the contact pressure compared to a normal Poron (NP) insole. The aim of this study was to compare the effectiveness of SR or NP versus an ethylene vinyl acetate (EV) insole in increasing the contact area (CA), and in reducing the contact pressure (CP) at different regions of the foot during walking. Plantar pressure data was collected from nine healthy individuals during walking using commercially available in-shoe plantar pressure sensors. Although, the NP insole significantly increased the CA and decreased the CP on the entire foot compared to the EV, there was no significant change in CP or CA at any region of the foot in any of the tested insoles. CP showed a positive significant correlation with CA at heel, hallux and heel center in all three insoles. The expected significant negative correlation between regional CA and CP was not observed.

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