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.