Standardization and Terminology Committee (STC)
International Society of Biomechanics (ISB):
- ISB recommendation on deﬁnitions of joint coordinate system ISB recommendation on deﬁnitions of joint coordinate system of various joints for the reporting of human joint motion—part I:
- ISB recommendation on deﬁnitions of joint coordinate system ISB recommendation on deﬁnitions of joint coordinate systems of various joints for the reporting of human joint motion—Part II:
ankle, hip, and spine
shoulder, elbow, wrist and hand
Clinical Gait Analysis:
- Can biomechanical variables predict improvement in crouch gait? In this study, we developed a multivariable regression model to determine if biomechanical variables and other subject characteristics measured during a physical exam and gait analysis can predict which subjects with crouch gait will demonstrate improved knee kinematics on a follow-up gait analysis.
- Comprehensive non-dimensional normalization of gait data Normalizing clinical gait analysis data is required to remove variability due to physical characteristics such as leg length and weight. This is particularly important for children where both are associated with age. In most clinical centres conventional normalization (by mass only) is used whereas there is a stronger biomechanical argument for non-dimensional normalization. This study used data from 82 typically developing children to compare how the two schemes performed over a wide range of temporalspatial and kinetic parameters by calculating the coefficients of determination with leg length, weight and height. 81% of the conventionally normalized parameters had a coefficient of determination above the threshold for a statistical association (p<0.05) compared to 23% of those normalized non-dimensionally.
- Development of temporal and distance parameters of gait in normal children Temporal and distance parameters of 33 normal children were obtained from instrumented gait analysis prospectively over ﬁve consecutive years. The parameters were normalised to minimise the confounding effects of increasing height and leg length.
- Detection of gait events and intervals Assessment and validation of a simple automated method for the detection of gait events and intervals
- Fixating the pelvis in the horizontal plane affects gait characteristics In assistive devices for neuro-rehabilitation, natural human motions are partly restricted by the device. This may affect the normality of walking during training. This research determines effects on gait of fixating the pelvis translations in the horizontal plane during treadmill walking. Direct effects on the motion of the pelvis and external forces acting on the pelvis were measured. Several gait descriptors (step parameters, trunk angles and a ground reaction force parameter) were defined and measured to indicate changes.
- Human movement analysis using stereophotogrammetry Human movement analysis using stereophotogrammetry Part 4: assessment of anatomical landmark misplacement and its effects on joint kinematics
- Muscle synergies and complexity of neuromuscular control during gait in cerebral palsy AIM: Individuals with cerebral palsy (CP) have impaired movement due to a brain injury near birth. Understanding how neuromuscular control is altered in CP can provide insight into pathological movement. We sought to determine if individuals with CP demonstrate reduced complexity of neuromuscular control during gait compared with unimpaired individuals and if changes in control are related to functional ability. METHOD: Muscle synergies during gait were retrospectively analyzed for 633 individuals (age range 3.9–70y): 549 with CP (hemiplegia, n=122; diplegia, n=266; triplegia, n=73; quadriplegia, n=88) and 84 unimpaired individuals. Synergies were calculated using non-negative matrix factorization from surface electromyography collected during previous clinical gait analyses. Synergy complexity during gait was compared with diagnosis subtype, functional ability, and clinical examination measures.
A simple and rapid automatic method for detection of gait events at the foot could speed up and possibly increase the repeatability of gait analysis and evaluations of treatments fo rpathological gaits. The aim of this study was to compare and validate a kinematic-based algorithm used in the detection of four gait events, heel contact, heel rise, toe contact and toe off.
- A method to calculate the centre of the ankle joint A method to calculate the centre of the ankle joint: A comparison with the Vicon Plug-in-Gait model.
- Deﬁning the knee joint ﬂexion–extension axis Deﬁning the knee joint ﬂexion–extension axis for purposes of quantitative gait analysis: An evaluation of methods
- Kalman smoothing Kalman smoothing improves the estimation of joint kinematics and kinetics in marker-based human gait analysis
In gait analysis, calculation of the ankle joint centre is a difﬁcult task. The conventional way to calculate the ankle joint centre is using the Vicon Plug-in-Gait model. The present study proposes a new model, which calculates the joint centre from two markers positioned over the medial and lateral malleoli (i.e. Two-marker-model).
Minimising measurement variability associated with hip axial rotation and avoiding knee joint angle cross-talk are two fundamental objectives of any method used to deﬁne the knee joint ﬂexion–extension axis for purposes of quantitative gait analysis. The aim of this experiment was to compare three different methods of deﬁning this axis: the knee alignment device (KAD) method, a method based on the transepicondylar axis (TEA) and an alternative numerical method (Dynamic).
We developed a Kalman smoothing algorithm to improve estimates of joint kinematics from measured marker trajectories during motion analysis. Kalman smoothing estimates are based on complete marker trajectories. This is an improvement over other techniques, such as the global optimisation method (GOM), Kalman ﬁltering, and local marker estimation (LME), where the estimate at each time instant is only based on part of the marker trajectories.
- A new approach to determining the hip rotation profile from clinical gait analysis data Conventional models for determining joint rotation angles from marker positions as part of three-dimensional clinical gait analysis are susceptible to errors arising from mis-placement of the thigh markers. An analysis of idealised data reveals how the measured variables are affected by different angular offsets of the thigh marker from its true position. An artefact on the varus-valgus signal arising from the projection true knee flexion onto a mal-aligned thigh segment axes is the most characteristic feature of this problem. If this is observed then the hip rotation profiles are also erroneous.
A technique is proposed to determine a correction factor which can be applied to gait data to correct for this mal-alignment. Its use is demonstrated on a single case study and a subjective assessment of its use on a cohort of 40 patients is reported. A detailed discussion of the assumptions on which the method is founded is included as well as guidelines as to when the technique is likely to be successful.
The technique used is perhaps best used as an aid to training staff in marker placement.
Kinematically Constrained Joint Parameters (KC Method):
- A kinematically constrained method for determining subject specific joint parameters
- Kinematically Constrained Joint Parameters I: Method, Repeatability and Objectivity
- Kinematically Constrained Joint Parameters part II: Accuracy and Kinematics Results
- Performance of the kinematically constrained method for joint parameter estimation
- A new method for estimating joint parameters from motion data This article describes a new method for joint parameter estimation. The new method can be summarized as follows: (i) the motions of two adjacent segments spanning a single joint are tracked, (ii) the axis of rotation between every pair of observed segment configurations is computed, (iii) the most likely intersection of all axes (effective joint center) and most likely orientation of the axes (effective joint axis) is found. Initial validation of the method was conducted on a hinged mechanical analog and a single healthy adult subject.
OLGA - Vicon
- Sensitivity of the OLGA and VCM models to erroneous marker placement: Effects on 3D-gait kinematics Gait data need to be reliable to be valuable for clinical decision-making. To reduce the impact of marker placement errors, the Optimized Lower Limb Gait Analysis (OLGA) model was developed. The purpose of this study was to assess the sensitivity of the kinematic gait data to a standard marker displacement of the OLGA model compared with the standard Vicon Clinical Manager (VCM) model and to determine whether OLGA reduces the errors due to the most critical marker displacements.
- Repeatability of an optimised lower body model - Vicon/OLGA The optimisation technique, optimised lower-limb gait analysis (OLGA), is described together with a preliminary study of repeatability compared to an implementation of the Newington–Helen Hayes gait model.
- Alternative modelling procedures for pelvic marker occlusion during motion analysis Motion analysis of participants with different body shapes under diverse conditions can be problematic when vitalmarkers are occluded. The markers located over the anterior superior iliac spines are commonly occluded in older patients and during analysis of activities with trunk and hip ﬂexion which can prevent accurate calculation of lower limb joint kinematics. Options to modify standard body models exist but have not been described in detail, and the effects on the lower limb kinematics are not known.
- Evaluation of alternative technical markers for the pelvic coordinate system In this study, we evaluated alternative technical markers for the motion analysis of the pelvic segment. Thirteen subjects walked eight times while tri-dimensional kinematics were recorded for one stride of each trial. Five marker sets were evaluated, and we compared the tilt, obliquity, and rotation angles of the pelvis segment: (1) standard: markers at the anterior and posterior superior iliac spines (ASIS and PSIS); (2) markers at the PSIS and at the hip joint centers, HJCs (estimated by a functional method and described with clusters of markers at the thighs); (3) markers at the PSIS and HJCs (estimated by a predictive method and described with clusters of markers at the thighs); (4) markers at the PSIS and HJCs (estimated by a predictive method and described with skin-mounted markers at the thighs based on the Helen-Hayes marker set); (5) markers at the PSIS and at the iliac spines.
- Validation of a method to accurately correct anterior superior iliac spine marker occlusion Anterior superior iliac spine (ASIS) marker occlusion commonly occurs during three-dimensional (3-D) motion capture of dynamic tasks with deep hip flexion. The purpose of this study was to validate a universal technique to correct ASIS occlusion.
Principal Component Analysis:
- A principal component analysis approach to correcting the knee flexion axis during gait Accurate and precise knee flexion axis identification is critical for prescribing and assessing tibial and femoral derotation osteotomies, but is highly prone to marker misplacement-induced error. The purpose of this study was to develop an efficient algorithm for post-hoc correction of the knee flexion axis and test its efficacy relative to other established algorithms.
- Principal component models of knee kinematics and kinetics: Normal vs. pathological gait patterns Gait data were collected on a group of 29 asymptomatic elderly subjects to describe knee joint kinematics and kinetics as measured by the three components of the bone-on-bone forces, net reaction moments and relative knee angles. Each of these gait measures were considered separately in the development of Principal Component Models (PCMs) to describe the variation of the normal subjects throughout the gait cycle. The statistical similarity of patients' gait curves (waveforms) to the pattern of normal subjects' gait waveforms was assessed using the PCMs. The PCMs consider data from the entire gait cycle and detect statistically significant waveform shapes using measures of distance from normal.