Please use this identifier to cite or link to this item: doi:10.22028/D291-42443
Title: Characteristic Changes of the Stance-Phase Plantar Pressure Curve When Walking Uphill and Downhill: Cross-Sectional Study
Author(s): Wolff, Christian
Steinheimer, Patrick
Warmerdam, Elke
Dahmen, Tim
Slusallek, Philipp
Schlinkmann, Christian
Chen, Fei
Orth, Marcel
Pohlemann, Tim
Ganse, Bergita
Language: English
Title: Journal of Medical Internet Research
Volume: 26
Publisher/Platform: JMIR Publications
Year of Publication: 2024
Free key words: podiatry
podiatric medicine
movement analysis
ground reaction forces
wearables
slope
gait analysis
monitoring
gait
rehabilitation
treatment
sensor
injury
postoperative treatment
sensors
personalized medicine
movement
digital health
pedography
baropedography
DDC notations: 610 Medicine and health
Publikation type: Journal Article
Abstract: Background: Monitoring of gait patterns by insoles is popular to study behavior and activity in the daily life of people and throughout the rehabilitation process of patients. Live data analyses may improve personalized prevention and treatment regimens, as well as rehabilitation. The M-shaped plantar pressure curve during the stance phase is mainly defined by the loading and unloading slope, 2 maxima, 1 minimum, as well as the force during defined periods. When monitoring gait continuously, walking uphill or downhill could affect this curve in characteristic ways. Objective: For walking on a slope, typical changes in the stance phase curve measured by insoles were hypothesized. Methods: In total, 40 healthy participants of both sexes were fitted with individually calibrated insoles with 16 pressure sensors each and a recording frequency of 100 Hz. Participants walked on a treadmill at 4 km/h for 1 minute in each of the following slopes: −20%, −15%, −10%, −5%, 0%, 5%, 10%, 15%, and 20%. Raw data were exported for analyses. A custom-developed data platform was used for data processing and parameter calculation, including step detection, data transformation, and normalization for time by natural cubic spline interpolation and force (proportion of body weight). To identify the time-axis positions of the desired maxima and minimum among the available extremum candidates in each step, a Gaussian filter was applied (σ=3, kernel size 7). Inconclusive extremum candidates were further processed by screening for time plausibility, maximum or minimum pool filtering, and monotony. Several parameters that describe the curve trajectory were computed for each step. The normal distribution of data was tested by the Kolmogorov-Smirnov and Shapiro-Wilk tests. Results: Data were normally distributed. An analysis of variance with the gait parameters as dependent and slope as independent variables revealed significant changes related to the slope for the following parameters of the stance phase curve: the mean force during loading and unloading, the 2 maxima and the minimum, as well as the loading and unloading slope (all P<.001). A simultaneous increase in the loading slope, the first maximum and the mean loading force combined with a decrease in the mean unloading force, the second maximum, and the unloading slope is characteristic for downhill walking. The opposite represents uphill walking. The minimum had its peak at horizontal walking and values dropped when walking uphill and downhill alike. It is therefore not a suitable parameter to distinguish between uphill and downhill walking. Conclusions: While patient-related factors, such as anthropometrics, injury, or disease shape the stance phase curve on a longer-term scale, walking on slopes leads to temporary and characteristic short-term changes in the curve trajectory.
DOI of the first publication: 10.2196/44948
URL of the first publication: https://www.jmir.org/2024/1/e44948
Link to this record: urn:nbn:de:bsz:291--ds-424430
hdl:20.500.11880/38091
http://dx.doi.org/10.22028/D291-42443
ISSN: 1438-8871
Date of registration: 25-Jul-2024
Faculty: M - Medizinische Fakultät
MI - Fakultät für Mathematik und Informatik
Department: M - Chirurgie
MI - Informatik
Professorship: M - Prof. Dr. med. Bergita Ganse
M - Prof. Dr. Tim Pohlemann
MI - Prof. Dr. Philipp Slusallek
Collections:SciDok - Der Wissenschaftsserver der Universität des Saarlandes

Files for this record:
File Description SizeFormat 
jmir-2024-1-e44948.pdf525,28 kBAdobe PDFView/Open


This item is licensed under a Creative Commons License Creative Commons