F Leboeuf1, R Baker2, A Barré3, J Reay2, R Jones2, M Sangeux4. 1. School of Health & Society, The University of Salford, UK. Electronic address: f.leboeuf@salford.ac.uk. 2. School of Health & Society, The University of Salford, UK. 3. Moveck Solution Inc., Canada. 4. The Murdoch Children's Research Institute, Melbourne, Australia.
Abstract
BACKGROUND: The Conventional Gait Model (CGM), known by a variety of different names, is widely used in clinical gait analysis. We present pyCGM2, an open-source implementation of the CGM with two versions. The first, CGM1.0, is a clone of Vicon Plug In Gait (PiG) with all its variants. CGM1.0 provides a platform to test the effect of modifications to the CGM on data collected and processed retrospectively or to provide backward compatibility. The second version, CGM1.1, offers some practical modifications and includes three well documented improvements. RESEARCH QUESTION: How do improvements of the conventional gait model affect joint kinematics and kinetics? METHOD: The practical modifications include the possibility to use a medial knee epicondyle marker, during static calibration only, to define the medio-lateral axis of the femur in place of the knee alignment device. The three improvements correspond to the change of pelvis angle decomposition sequence, the adoption of a single tibia coordinate system, and the default decomposition of the joint moments in the joint coordinate system. We validated the outputs of version CGM1.0 against Vicon-PiG, and estimated the effect of the modifications included in version CGM1.1 using gait data collected in 16 healthy participants. RESULTS: Kinematics and kinetics of CGM1.0 were superimposed with that of Vicon-PiG, with root mean square differences less than 0.04° for kinematics and less than 0.05 N.m.kg-1 for kinetics. SIGNIFICANCE: The differences between the CGM1.1 and CGM1.0 were minimal in the healthy participant cohort but we discussed the expected difference in participants with different gait pathologies. We hope that the pyCGM2 will facilitate the systematic testing and the use of improved processing methods for the conventional gait model.
BACKGROUND: The Conventional Gait Model (CGM), known by a variety of different names, is widely used in clinical gait analysis. We present pyCGM2, an open-source implementation of the CGM with two versions. The first, CGM1.0, is a clone of Vicon Plug In Gait (PiG) with all its variants. CGM1.0 provides a platform to test the effect of modifications to the CGM on data collected and processed retrospectively or to provide backward compatibility. The second version, CGM1.1, offers some practical modifications and includes three well documented improvements. RESEARCH QUESTION: How do improvements of the conventional gait model affect joint kinematics and kinetics? METHOD: The practical modifications include the possibility to use a medial knee epicondyle marker, during static calibration only, to define the medio-lateral axis of the femur in place of the knee alignment device. The three improvements correspond to the change of pelvis angle decomposition sequence, the adoption of a single tibia coordinate system, and the default decomposition of the joint moments in the joint coordinate system. We validated the outputs of version CGM1.0 against Vicon-PiG, and estimated the effect of the modifications included in version CGM1.1 using gait data collected in 16 healthy participants. RESULTS: Kinematics and kinetics of CGM1.0 were superimposed with that of Vicon-PiG, with root mean square differences less than 0.04° for kinematics and less than 0.05 N.m.kg-1 for kinetics. SIGNIFICANCE: The differences between the CGM1.1 and CGM1.0 were minimal in the healthy participant cohort but we discussed the expected difference in participants with different gait pathologies. We hope that the pyCGM2 will facilitate the systematic testing and the use of improved processing methods for the conventional gait model.
Authors: Tadashi Ito; Hideshi Sugiura; Yuji Ito; Sho Narahara; Koji Noritake; Daiki Takahashi; Kentaro Natsume; Nobuhiko Ochi Journal: Int J Environ Res Public Health Date: 2022-09-13 Impact factor: 4.614
Authors: Yong Kuk Kim; Rosa M S Visscher; Elke Viehweger; Navrag B Singh; William R Taylor; Florian Vogl Journal: PLoS One Date: 2022-10-13 Impact factor: 3.752