Recommendations of the GAMMA association for the standardization of clinical movement analysis laboratories.

Clinical gait analysis involves objective, valid, and reliable techniques for assessing gait function and is crucial for assessing walking patterns and identifying gait abnormalities in various patient populations. By analyzing joint angles, muscle activity, and other biomechanical factors during walking, clinicians can diagnose gait disorders, plan interventions, and improve patient outcomes. The GAMMA association aims to provide recommendations to support the standardization and quality assurance for clinical-instrumented 3D motion analysis services within the German-speaking region in central Europe.

Patient-specific gait pattern in individuals with patellofemoral instability reduces knee joint loads.

Patellofemoral instability is influenced by morphological factors and associated with compensational alterations in gait pattern. Recent simulation studies investigated the impact of knee morphology on the stability and loading of the patellofemoral joint but neglected the patient-specific gait pattern. The aim of this study was to investigate the impact of patient-specific gait pattern on muscle forces and joint loading in individuals with patellofemoral instability.

Mediolateral Margin of Stability highlights motor strategies for maintaining dynamic balance in older adults.

The dynamical nature of gait increases fall risk for older adults as the Center of Mass (COM) is constantly displaced inside and outside the Base of Support (BOS). Foot placement and leg joint moments are the primary mechanisms controlling dynamic balance. The Margin of Stability (MOS) quantifies the distance between the COM dynamical state and the BOS.

GaitRec-VR: 3D Gait Analysis for Walking Overground with and without a Head-Mounted-Display in Virtual Reality.

This data descriptor introduces GaitRec-VR, a 3D gait analysis dataset consisting of 20 healthy participants (9 males, 11 females, age range 21-56) walking at self-selected speeds in a real-world laboratory and the virtual reality (VR) replicas of this laboratory. Utilizing a head-mounted display and a 12-camera motion capture system alongside a synchronized force plate, the dataset encapsulates real and virtual walking experiences. A direct kinematic model and an inverse dynamic approach were employed for kinematics and computation of joint moments respectively, with an average of 23 ± 6 steps for kinematics and five clean force plate strikes per participant for kinetic analysis.

Repeatability and minimal detectable change including clothing effects for smartphone-based 3D markerless motion capture.

OpenCap, a smartphone- and web-based markerless system, has shown acceptable accuracy compared to marker-based systems, but lacks information on repeatability. This study fills this gap by evaluating the intersession repeatability of OpenCap and investigating the effects of clothing on gait kinematics. Twenty healthy volunteers participated in a test-retest study, performing walking and sit-to-stand tasks with minimal clothing and regular street wear.

Which method should we use to determine the hip joint center location in individuals with a high amount of soft tissue?

Background: This study investigated the most accurate method for estimating the hip joint center position in clinical 3D gait analysis for young individuals with high amounts of soft tissue. We compared position estimates of five regression-based and two functional methods to the hip joint center position obtained through 3D free-hand ultrasound.

Methods: For this purpose, the data of 14 overweight or obese individuals with a mean age of 13.

Inter-trial variability is higher in 3D markerless compared to marker-based motion capture: Implications for data post-processing and analysis.

Markerless motion capture has recently attracted significant interest in clinical gait analysis and human movement science. Its ease of use and potential to streamline motion capture recordings bear great potential for out-of-the-laboratory measurements in large cohorts. While previous studies have shown that markerless systems can achieve acceptable accuracy and reliability for kinematic parameters of gait, they also noted higher inter-trial variability of markerless data.

Concurrent validity of smartphone-based markerless motion capturing to quantify lower-limb joint kinematics in healthy and pathological gait.

Markerless motion capturing has the potential to provide a low-cost and accessible alternative to traditional marker-based systems for real-world biomechanical assessment. However, before these systems can be put into practice, we need to rigorously evaluate their accuracy in estimating joint kinematics for various gait patterns. This study evaluated the accuracy of a low-cost, open-source, and smartphone-based markerless motion capture system, namely OpenCap, for measuring 3D joint kinematics in healthy and pathological gait compared to a marker-based system.

Robust deep learning-based gait event detection across various pathologies.

The correct estimation of gait events is essential for the interpretation and calculation of 3D gait analysis (3DGA) data. Depending on the severity of the underlying pathology and the availability of force plates, gait events can be set either manually by trained clinicians or detected by automated event detection algorithms. The downside of manually estimated events is the tedious and time-intensive work which leads to subjective assessments.

Modeling biological individuality using machine learning: A study on human gait.

Human gait is a complex and unique biological process that can offer valuable insights into an individual's health and well-being. In this work, we leverage a machine learning-based approach to model individual gait signatures and identify factors contributing to inter-individual variability in gait patterns. We provide a comprehensive analysis of gait individuality by (1) demonstrating the uniqueness of gait signatures in a large-scale dataset and (2) highlighting the gait characteristics that are most distinctive to each individual.

Overground Walking in a Fully Immersive Virtual Reality: A Comprehensive Study on the Effects on Full-Body Walking Biomechanics.

Virtual reality (VR) is an emerging technology offering tremendous opportunities to aid gait rehabilitation. To this date, real walking with users immersed in virtual environments with head-mounted displays (HMDs) is either possible with treadmills or room-scale (overground) VR setups. Especially for the latter, there is a growing interest in applications for interactive gait training as they could allow for more self-paced and natural walking.

Comparison of Bioelectrical Impedance-Based Methods on Body Composition in Young Patients with Obesity.

(1) Background: The determination of body composition is an important method to investigate patients with obesity and to evaluate the efficacy of individualized medical interventions. Bioelectrical impedance-based methods are non-invasive and widely applied but need to be validated for their use in young patients with obesity. (2) Methods: We compiled data from three independent studies on children and adolescents with obesity, measuring body composition with two bioelectrical impedance-based devices (TANITA and BIACORPUS).

Reliability of walking and stair climbing kinematics in a young obese population using a standard kinematic and the CGM2 model.

Background: Recently, the successor of the Conventional Gait Model, the CGM2 was introduced. Even though achievable reliability of gait kinematics is a well-assessed topic in gait analysis for several models, information about reliability in difficult study samples with high amount of subcutaneous fat is scarce and to date, not available for the CGM2. Therefore, this study evaluated the test-retest reliability of the CGM2 model for difficult data with high amount of soft tissue artifacts.

GaiTRec, a large-scale ground reaction force dataset of healthy and impaired gait.

The quantification of ground reaction forces (GRF) is a standard tool for clinicians to quantify and analyze human locomotion. Such recordings produce a vast amount of complex data and variables which are difficult to comprehend. This makes data interpretation challenging.

Input representations and classification strategies for automated human gait analysis.

Background: Quantitative gait analysis produces a vast amount of data, which can be difficult to analyze. Automated gait classification based on machine learning techniques bear the potential to support clinicians in comprehending these complex data. Even though these techniques are already frequently used in the scientific community, there is no clear consensus on how the data need to be preprocessed and arranged to assure optimal classification accuracy outcomes.

Effects of a lower extremity exercise program on gait biomechanics and clinical outcomes in children and adolescents with obesity: A randomized controlled trial.

Background: Research highlights the detrimental effects of obesity on gait biomechanics and the accompanied risk of lower-extremity skeletal malalignments, increased joint stress, pain and discomfort. Individuals with obesity typically show increased knee valgus angles combined with an increased step width. Accompanying muscular dysfunctions impede their ability to compensate for these alterations, especially in the frontal plane.

Reliability of joint kinematic calculations based on direct kinematic and inverse kinematic models in obese children.

Background: In recent years, the reliability of inverse (IK) and direct kinematic (DK) models in gait analysis have been assessed intensively, but mainly for lean populations. However, obesity is a growing issue. So far, the sparse results available for the reliability of clinical gait analysis in obese populations are limited to direct kinematic models.

KAVAGait: Knowledge-Assisted Visual Analytics for Clinical Gait Analysis.

In 2014, more than 10 million people in the US were affected by an ambulatory disability. Thus, gait rehabilitation is a crucial part of health care systems. The quantification of human locomotion enables clinicians to describe and analyze a patient's gait performance in detail and allows them to base clinical decisions on objective data.

Automatic Classification of Functional Gait Disorders.

This paper proposes a comprehensive investigation of the automatic classification of functional gait disorders (GDs) based solely on ground reaction force (GRF) measurements. The aim of this study is twofold: first, to investigate the suitability of the state-of-the-art GRF parameterization techniques (representations) for the discrimination of functional GDs; and second, to provide a first performance baseline for the automated classification of functional GDs for a large-scale dataset. The utilized database comprises GRF measurements from 279 patients with GDs and data from 161 healthy controls (N).

Is the reliability of 3D kinematics of young obese participants dependent on the hip joint center localization method used?

The aim of this study was to investigate if the test-retest reliability for three-dimensional (3D) gait kinematics in a young obese population is affected by using either a predictive (Davis) or a functional (SCoRE) hip joint center (HJC) localization approach. A secondary goal was to analyze how consistent both methods perform in estimating the HJC position. A convenience sample of ten participants, two females and eight males with an age-based body mass index (BMI) above the 97th percentile (mean±SD: 34.

Trunk muscle activation levels during eight stabilization exercises used in the functional kinetics concept: A controlled laboratory study.

Background: To ensure accurate implementation of stabilization exercises in rehabilitation, physical therapists need to understand the muscle activation patterns of prescribed exercise.

Objective: Compare muscle activity during eight trunk and lumbar spine stabilization exercises of the Functional Kinetics concept by Klein-Vogelbach.

Methods: A controlled laboratory study with a single-group repeated-measures design was utilized to analyze surface electromyographic intensities of 14 female and 6 male young healthy participants performing eight exercises.

Within-assessor reliability and minimal detectable change of gait kinematics in a young obese demographic.

Introduction: Three-dimensional gait analysis (3DGA) in obese populations is a difficult task due to a great amount of subcutaneous fat. This makes it more challenging to identify anatomical landmarks, thus leading to inconsistent marker placement. Therefore, the purpose of this study was to investigate the test-retest reliability for kinematic measurements of obese children and adolescents.