Association of Arch Stiffness with Plantar Impulse Distribution during Walking, Running, and Gait Termination

The purpose of this study was to determine relationships between arch stiffness and relative regional impulse during walking, running, and stopping. A total of 61 asymptomatic male subjects volunteered to participate in the study. All were classified by calculating the arch stiffness index using 3-dimensional foot morphological scanning. Plantar pressure distribution data were collected from participants using a Footscan pressure platform during gait tests that included walking, running, and gait termination. The stiff arches group (n = 19) and flexible arches group (n = 17) were included in the following data analysis. The results suggested that subjects with stiffer arches had a larger and smaller percentage of plantar impulse in the forefoot and rearfoot, respectively, than subjects with more flexible arches during walking and running. However, during gait termination, which included planned and unplanned gait stopping, the plantar impulse distribution pattern was found to be reversed. The current findings demonstrate that the distributional changes of plantar loading follow unidirectional transfer between the forefoot and the rearfoot on the plantar longitudinal axis. Moreover, the patterns of impulse distribution are also different based on different gait task mechanisms.

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

Sensors ◽

10.3390/s21062246 ◽

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.

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Effect of additional body weight on arch index and dynamic plantar pressure distribution during walking and gait termination

PeerJ ◽

10.7717/peerj.8998 ◽

2020 ◽

Vol 8 ◽

pp. e8998

Author(s):

Xuanzhen Cen ◽

Datao Xu ◽

Julien S. Baker ◽

Yaodong Gu

Keyword(s):

Body Weight ◽

Pressure Distribution ◽

Plantar Pressure ◽

Foot Injuries ◽

Gait Termination ◽

Longitudinal Arch ◽

Plantar Pressure Distribution ◽

Foot Morphology ◽

Arch Index ◽

Daily Physical Activities

The medial longitudinal arch is considered as an essential feature which distinguishes humans from other primates. The longitudinal arch plays a supporting and buffering role in human daily physical activities. However, bad movement patterns could lead to deformation of arch morphology, resulting in foot injuries. The authors aimed to investigate any alterations in static and dynamic arch index following different weight bearings. A further aim was to analyze any changes in plantar pressure distribution characteristics on gait during walking and stopping, Twelve males were required to complete foot morphology scans and three types of gait tests with 0%, 10%, 20% and 30% of additional body weight. The dynamic gait tests included walking, planned and unplanned gait termination. Foot morphology details and plantar pressure data were collected from subjects using the Easy-Foot-Scan and Footscan pressure platform. No significant differences were observed in static arch index when adding low levels of additional body weight (10%). There were no significant changes observed in dynamic arch index when loads were added in the range of 20% to 30%, except in unplanned gait termination. Significant maximal pressure increases were observed in the rearfoot during walking and in both the forefoot and rearfoot during planned gait termination. In addition, significant maximum pressure increases were shown in the lateral forefoot and midfoot during unplanned gait termination when weight was increased. Findings from the study indicated that excessive weight bearing could lead to a collapse of the arch structure and, therefore, increases in plantar loading. This may result in foot injuries, especially during unplanned gait termination.

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Self-selected running speeds do not alter plantar pressure distribution data in barefoot running

Journal of Biomechanics ◽

10.1016/s0021-9290(06)83689-4 ◽

2006 ◽

Vol 39 ◽

pp. S192 ◽

Cited By ~ 2

Author(s):

C. Maiwald ◽

S. Grau ◽

I. Krauss ◽

M. Mauch ◽

T. Horstmann

Keyword(s):

Pressure Distribution ◽

Plantar Pressure ◽

Distribution Data ◽

Barefoot Running ◽

Plantar Pressure Distribution

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A Comparative Biomechanical Analysis during Planned and Unplanned Gait Termination in Individuals with Different Arch Stiffnesses

Applied Sciences ◽

10.3390/app11041871 ◽

2021 ◽

Vol 11 (4) ◽

pp. 1871

Author(s):

Xuanzhen Cen ◽

Zhenghui Lu ◽

Julien S. Baker ◽

Bíró István ◽

Yaodong Gu

Keyword(s):

Lower Extremity ◽

Lower Limb ◽

Plantar Pressure ◽

Sagittal Plane ◽

Biomechanical Analysis ◽

Stiffness Index ◽

Maximum Pressure ◽

Pressure Data ◽

Kinetic Chain ◽

Gait Termination

Although values of arch stiffness index (ASI) have been used to evaluate arch structure and injury susceptibility, investigations are limited regarding the influence of ASI on biomechanical characteristics during gait termination, which involves a challenging balance transition from walking to standing. This study aimed to explore plantar pressure distribution and lower extremity joint kinematic differences between individuals with both a stiff and flexible arch (SA and FA, respectively) during planned and unplanned gait termination (PGT and UGT, respectively). Following the calculation of ASI, sixty-five asymptomatic male subjects were classified and participated in two types of gait termination tests to acquire kinematic and plantar pressure data. Parameters were compared between SA and FA using a two-way ANOVA during PGT and UGT, respectively. UGT was found to have a larger range of motion on the hip joint in the sagittal plane and the knee joint in the transverse plane when compared with PGT. The differences in the kinematic characteristics of the lower limb joints caused by the difference in arch stiffness are mainly concentrated in the ankle and metatarsophalangeal joints. Plantar pressure data, represented by the maximum pressure, showed significant differences in the forefoot and rearfoot areas. These results suggest that ASI could change freedom of motion of the lower limb joints, and UGT tends to conduct a compensatory adjustment for the lower extremity kinetic chain. An understanding of the biomechanical characteristics of arch structures may provide additional insights into foot function and injury prediction during gait termination.

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Effect of PSSE on Plantar Pressure Distribution and Balance in Scoliosis

Case Medical Research ◽

10.31525/ct1-nct04226209 ◽

2020 ◽

Author(s):

Keyword(s):

Pressure Distribution ◽

Plantar Pressure ◽

Plantar Pressure Distribution

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

Sensors ◽

10.3390/s21041450 ◽

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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