Foot shape-function model data
Cite this dataset
Schuster, Robert (2023). Foot shape-function model data [Dataset]. Dryad. https://doi.org/10.5061/dryad.g4f4qrfwt
Abstract
The human foot is a complex structure that plays an important role in our capacity for upright locomotion. Comparisons of our feet to those of our closest extinct and extant relatives have linked shape features (e.g., the longitudinal and transverse arches, heel size and toe length) to specific mechanical functions. However, foot shape varies widely across the human population, so it remains unclear if and how specific shape variants are related to locomotor mechanics. Here we construct a statistical shape-function model (SFM) from 100 healthy participants to directly explore the relationship between the shape and function of our feet. We also examined if we could predict the joint motion and moments occurring within a person’s foot during locomotion based purely on shape features. The SFM revealed that the longitudinal and transverse arches, relative foot proportions and toe shape along with their associated joint mechanics were the most variable. However, each of these only accounted for small proportions of the overall variation in shape, deformation, and joint mechanics, most likely due to the high structural complexity of the foot. Nevertheless, a leave-one-out analysis showed that the SFM can accurately predict joint mechanics of a novel foot, based on its shape and deformation.
README: Foot shape-function model data
https://doi.org/10.5061/dryad.g4f4qrfwt
3D foot scans (PLY files) while bearing minimal weight (mBW) and full body weight (fBW), as well as a MATLAB file containing foot and ankle joint angles and moments from level, incline and decline treadmill walking and running.
Description of the data and file structure
3D foot scans (~920 MB):
The 3D foot scans, collected while the participant's dominant foot was either bearing minimal weight (mBW) or their full body-weight (fBW), are provided as PLY files.
File names contain the participant's ID, the weight-bearing condition and which foot was scanned (e.g., 045_fBW_R.ply: a scan of the 45th participant's right foot while bearing full body-weight).
For left-foot dominant participants, the foot scan was reflected about its length axis to be comparable to the feet of right-foot dominant participants. Hence, the filenames for these scans contain Lr instead of R.
Joint Kinematics and Kinetics (~280 MB):
The file treadmill_data.mat contains MATLAB structures of the stance phase time-normalised joint
- angles (IK) and
- moments (ID).
Each of these structures is subdivided into further structures for each participant (e.g., p003), as well as a structure containing the values averaged across all stance phases for each participant (mean).
The participant and mean structures are divided into the various locomotor tasks:
- running downhill
- running level
- running uphill
- walking downhill
- walking level
- walking uphill
These, in turn, each contain a matrix of dimensions 101 x number of stance phases for each of the following joints:
- hip
- knee
- ankle
- subtalar
- midtarsal
- tarsometatarsal and
- metatarsophalangeal (mtp).
A table containing the participant information (info), including identifier (ID), sex (M/F), height (in cm), weight (in kg) and leg dominance (Leg) is also contained in this file.
Methods
3D foot scans (PLY files) while bearing minimal weight (mBW) and full body weight (fBW), as well as a MATLAB file containing foot and ankle joint angles and moments from level, incline and decline treadmill walking and running.
Funding
Australian Research Council, Award: DE200100585