Data should be made as simple as possible but not simpler: The method chosen for dimensionality reduction and its parameters can affect the clustering of runners based on their kinematics.

Dimensionality reduction is a critical step for the efficacy and efficiency of clustering analysis. Despite the multiple available methods, biomechanists have often defaulted to Principal Component Analysis (PCA). We evaluated two PCA- and one autoencoder-based dimensionality reduction methods for their data compression and reconstruction capability, assessed their effect on the output of clustering runners' based on kinematics, and discussed their implications for the biomechanical assessment of running technique.

Clustering analysis across different speeds reveals two distinct running techniques with no differences in running economy.

Establishing the links between running technique and economy remains elusive due to high inter-individual variability. Clustering runners by technique may enable tailored training recommendations, yet it is unclear if different techniques are equally economical and whether clusters are speed-dependent. This study aimed to identify clusters of runners based on technique and to compare cluster kinematics and running economy.

A support vector machine algorithm can successfully classify running ability when trained with wearable sensor data from anatomical locations typical of consumer technology.

Greater understanding of differences in technique between runners may allow more beneficial feedback related to improving performance and decreasing injury risk. The purpose of this study was to develop and test a support vector machine classifier, which could automatically differentiate running technique between experienced and novice participants using only wearable sensor data. Three-dimensional linear accelerations and angular velocities were collected from six wearable sensors secured to current common smart device locations.

Development and validation of FootNet; a new kinematic algorithm to improve foot-strike and toe-off detection in treadmill running.

The accurate detection of foot-strike and toe-off is often critical in the assessment of running biomechanics. The gold standard method for step event detection requires force data which are not always available. Although kinematics-based algorithms can also be used, their accuracy and generalisability are limited, often requiring corrections for speed or foot-strike pattern.

Athletic groin pain patients and healthy athletes demonstrate consistency in their movement strategy selection when performing multiple repetitions of a change of direction test.

Objectives: To report the consistency in movement strategy selection in athletic groin pain patients and to assess whether there are differences in consistency between athletic groin pain patients and healthy athletes.

Design: Cross sectional exploratory study.

Methods: Twenty athletic groin pain patients and 21 healthy athletes performed 15 repetitions of 110° change of direction task.