A portable plantar pressure system: Specifications, design, and preliminary results

2020 ◽  
Vol 28 (5) ◽  
pp. 553-560
Author(s):  
Michal Ostaszewski ◽  
Jolanta Pauk ◽  
Kacper Lesniewski
Keyword(s):  
Healthy Volunteer ◽  
Pressure Distribution ◽  
Plantar Pressure ◽  
Pressure Sensors ◽  
Pressure System ◽  
Distribution Measurement ◽  
Plantar Pressure Distribution ◽  
Portable System ◽  
Linearity Error ◽  
The Difference

BACKGROUND: In recent years, there has been an increasing interest in developing in-shoe foot plantar pressure systems. Although such devices are not novel, devising insole devices for gait analysis is still an important issue. OBJECTIVE: The goal of this study is to develop a new portable system for plantar pressure distribution measurement based on a three-axis accelerometer. METHODS: The portable system includes: PJRC Teensy 3.6 microcontroller with 32-bit ARM Cortex-M4 microprocessor with a clock speed of 180 MHz; HC-11 radio modules (transmitter and receiver); a battery; a fixing band; pressure sensors; MPU-9150 inertial navigation module; and FFC tape. The pressure insole is leather-based and consists of seven layers. It is divided into 16 areas and the outcome of the system is data concerning plantar pressure distribution under foot during gait. The system was tested on 22 healthy volunteer subjects, and the data was compared with a commercially available system: Medilogic. RESULT: The SNR value for the proposed sensor is 28.27 dB. For a range of pressure of 30–100 N, the sensitivity is 0.0066 V/N while the linearity error is 0.05. The difference in plantar pressure from both the portable plantar pressure system and Medilogic is not statistically significant. CONCLUSION: The proposed system could be recommended for research applications both inside and outside of a typical gait laboratory.

scholarly journals Foot Modeling and Smart Plantar Pressure Reconstruction from Three Sensors

2014 ◽  
Vol 8 (1) ◽  
pp. 84-92 ◽  
Author(s):  
Hussein Abou Ghaida ◽  
Serge Mottet ◽  
Jean-Marc Goujon
Keyword(s):  
Pressure Distribution ◽  
Plantar Pressure ◽  
Soft Tissues ◽  
Pressure Sensors ◽  
Biomechanical Model ◽  
Standing Position ◽  
Average Error ◽  
Pressure Measurements ◽  
Human Foot ◽  
Plantar Pressure Distribution

In order to monitor pressure under feet, this study presents a biomechanical model of the human foot. The main elements of the foot that induce the plantar pressure distribution are described. Then the link between the forces applied at the ankle and the distribution of the plantar pressure is established. Assumptions are made by defining the concepts of a 3D internal foot shape, which can be extracted from the plantar pressure measurements, and a uniform elastic medium, which describes the soft tissues behaviour. In a second part, we show that just 3 discrete pressure sensors per foot are enough to generate real time plantar pressure cartographies in the standing position or during walking. Finally, the generated cartographies are compared with pressure cartographies issued from the F-SCAN system. The results show 0.01 daN (2% of full scale) average error, in the standing position.

scholarly journals Comparison of the effects of forefoot jointpreserving arthroplasty and resectionreplacement arthroplasty on walking plantar pressure distribution and patient-based outcomes in patients with rheumatoid arthritis

2018 ◽  
Vol 3 (3) ◽  
pp. 2473011418S0021
Author(s):  
Kosuke Ebina ◽  
Hideki Tsuboi ◽  
Makoto Hirao ◽  
Takaaki Noguchi
Keyword(s):  
Rheumatoid Arthritis ◽  
Pressure Distribution ◽  
Plantar Pressure ◽  
Disease Activity Score ◽  
Metatarsal Head ◽  
Operative Duration ◽  
Plantar Pressure Distribution ◽  
Replacement Arthroplasty ◽  
Post Operation ◽  
The Difference

Category: Midfoot/Forefoot Introduction/Purpose: The purpose of this retrospective study is to clarify the difference in plantar pressure distribution during walking and related patient-based outcomes between forefoot joint-preserving arthroplasty and resection-replacement arthroplasty in patients with rheumatoid arthritis (RA). Methods: Four groups of patients were recruited. Group1 included 22 feet of 11 healthy controls (age 48.6 years), Group2 included 36 feet of 28 RA patients with deformed non-operated feet (age 64.8 years, Disease activity score assessing 28 joints with CRP [DAS28-CRP] 2.3), Group3 included 27 feet of 20 RA patients with metatarsal head resection-replacement arthroplasty (age 60.7 years, post-operative duration 5.6 years, DAS28-CRP 2.4), and Group4 included 34 feet of 29 RA patients with metatarsophalangeal (MTP) joint-preserving arthroplasty (age 64.6 years, post-operative duration 3.2 years, DAS28-CRP 2.3). Patients were cross-sectionally examined by F-SCAN II® to evaluate walking plantar pressure, and the self-administered foot evaluation questionnaire (SAFE-Q). Twenty joint-preserving arthroplasty feet were longitudinally examined at both pre- and post-operation. Results: In the 1st MTP joint, Group4 showed higher pressure distribution (13.7%) than Group2 (8.0%) and Group3 (6.7%) (P<0.001). In the 2nd-3 rd MTP joint, Group4 showed lower pressure distribution (9.0%) than Group2 (14.5%) (P<0.001) and Group3 (11.5%) (P<0.05). On longitudinal analysis, Group4 showed increased 1st MTP joint pressure (8.5% vs. 14.7%; P<0.001) and decreased 2nd-3 rd MTP joint pressure (15.2% vs. 10.7%; P<0.01) distribution. In the SAFE-Q subscale scores, Group4 showed higher scores than Group3 in pain and pain-related scores (84.1 vs. 71.7; P<0.01) and in shoe-related scores (62.5 vs. 43.1; P<0.01). Conclusion: Joint-preserving arthroplasty resulted in higher 1st MTP joint and lower 2nd-3 rd MTP joint pressures than resection-replacement arthroplasty, which were associated with better patient-based outcomes.

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 The Design and Simulation of a 16-Sensors Plantar Pressure Insole Layout for Different Applications: From Sports to Clinics, a Pilot Study

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1450
Author(s):  
Alfredo Ciniglio ◽  
Annamaria Guiotto ◽  
Fabiola Spolaor ◽  
Zimi Sawacha
Keyword(s):  
Pressure Distribution ◽  
Plantar Pressure ◽  
Center Of Pressure ◽  
Reaction Force ◽  
Weight Lifting ◽  
Lower Limbs ◽  
Plantar Pressure Distribution ◽  
Drop Landing ◽  
Mean Pressure ◽  
Footwear Design

The quantification of plantar pressure distribution is widely done in the diagnosis of lower limbs deformities, gait analysis, footwear design, and sport applications. To date, a number of pressure insole layouts have been proposed, with different configurations according to their applications. The goal of this study is to assess the validity of a 16-sensors (1.5 × 1.5 cm) pressure insole to detect plantar pressure distribution during different tasks in the clinic and sport domains. The data of 39 healthy adults, acquired with a Pedar-X® system (Novel GmbH, Munich, Germany) during walking, weight lifting, and drop landing, were used to simulate the insole. The sensors were distributed by considering the location of the peak pressure on all trials: 4 on the hindfoot, 3 on the midfoot, and 9 on the forefoot. The following variables were computed with both systems and compared by estimating the Root Mean Square Error (RMSE): Peak/Mean Pressure, Ground Reaction Force (GRF), Center of Pressure (COP), the distance between COP and the origin, the Contact Area. The lowest (0.61%) and highest (82.4%) RMSE values were detected during gait on the medial-lateral COP and the GRF, respectively. This approach could be used for testing different layouts on various applications prior to production.

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