Dataset of lower extremity joint angles, moments and forces in distance running
Qichang Mei,
Justin Fernandez,
Liangliang Xiang,
Zixiang Gao,
Peimin Yu,
Julien S. Baker,
Yaodong Gu
Affiliations
Qichang Mei
Faculty of Sports Science, Ningbo University, Ningbo, China; Research Academy of Grand Health, Ningbo University, Ningbo, China; Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand; Corresponding author.
Justin Fernandez
Faculty of Sports Science, Ningbo University, Ningbo, China; Research Academy of Grand Health, Ningbo University, Ningbo, China; Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand; Department of Engineering Science, The University of Auckland, Auckland, New Zealand
Liangliang Xiang
Faculty of Sports Science, Ningbo University, Ningbo, China; Research Academy of Grand Health, Ningbo University, Ningbo, China; Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
Zixiang Gao
Faculty of Sports Science, Ningbo University, Ningbo, China; Research Academy of Grand Health, Ningbo University, Ningbo, China; Faculty of Engineering, University of Pannonia, Veszprém, Hungary
Peimin Yu
Faculty of Sports Science, Ningbo University, Ningbo, China; Research Academy of Grand Health, Ningbo University, Ningbo, China; Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
Julien S. Baker
Department of Sport and Physical Education, Hong Kong Baptist University, Hong Kong, China
Yaodong Gu
Faculty of Sports Science, Ningbo University, Ningbo, China; Research Academy of Grand Health, Ningbo University, Ningbo, China; Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand; Corresponding author.
This study presents a database of joint angles, moments, and forces of the lower extremity from distance running at a submaximal speed in recreational runners. Twenty recreational runners participated in two experimental sessions, specifically pre and post a 5k treadmill run, with a synchronous collection of markers trajectories and ground reaction forces for both limbs in walking and running trials. The raw data in C3D files could be used for musculoskeletal modelling. Extra datasets of joint angles, moments, and forces are presented ready-for-use in MAT files, which could be as reference for study of biomechanical alterations from distance running. Applying advanced data processing techniques (Machine Learning algorithms) to these datasets (C3D & MAT), such as Principal Component Analysis, could extract key features of variation, thus potentially being applied for correlation with accelerometric and gyroscope parameters from wearable sensors during field running. Dataset of multi-segmental foot could be another contribution for the investigation of foot complex biomechanics from distance running. The dataset from Asian males may also be used for population-based studies of running biomechanics.
