High levels of gait asymmetry are associated with many pathologies. Our long-term goal is to improve gait symmetry through real-time biofeedback of a symmetry index. Symmetry is often reported as a single metric or a collective signature of multiple discrete measures. While this is useful for assessment, incorporating multiple feedback metrics presents too much information for most subjects to use as visual feedback for gait retraining. The aim of this article was to develop a global gait asymmetry (GGA) score that could be used as a biofeedback metric for gait retraining and to test the effectiveness of the GGA for classifying artificially-induced asymmetry. Eighteen participants (11 males; age 26.9 y [SD = 7.7]; height 1.8 m [SD = 0.1]; body mass 72.7 kg [SD = 8.9]) walked on a treadmill in 3 symmetry conditions, induced by wearing custom-made sandals: a symmetric condition (identical sandals) and 2 asymmetric conditions (different sandals). The GGA score was calculated, based on several joint angles, and compared between conditions. Significant differences were found among all conditions (P < .001), meaning that the GGA score is sensitive to different levels of asymmetry, and may be useful for rehabilitation and assessment.

This study aimed to quantify and compare the upper limb angular kinematics and its contributions to the racket head speed between the cross-court (CC) and inside-out (IO) attacking tennis forehand of elite tennis players in a competitive environment. A new approach was used to study the forehand drive with mini-inertial sensors of motion capture to record the kinematic data. Six strokes in each direction per participant (72 shots in total) were chosen for analysis. Upper limb kinematics were calculated in the Visual 3D platform (Visual 3D Professional V5.01.21, C-motion, Germantown, MD, USA). The method used to calculate the upper limb’s contributions was performed with MATLAB software and used the segment’s (upper arm, forearm and hand) angular velocities and their respective displacement vectors obtained through the inertial sensors. Upper limb kinematics demonstrated a higher shoulder rotation in the IO direction with significant differences at the end of the backswing, which could be a key factor in distinguishing the two directions of the shot. Results also demonstrated that the horizontal flexion of the upper arm (around the shoulder joint) was primarily responsible for the racket velocity in the anteroposterior direction (48.1% CC and 45.2% IO), followed by the extension of the forearm (around the elbow joint) (17.3% CC and 20.9% IO) and the internal rotation of the upper arm (around the shoulder joint) (15.6% CC and 14.2% IO). No significant differences were shown in the contributions of upper limbs to the racket head velocity between the two directions of the shot. Tennis coaches and players should develop a specific training programme to perform higher angular velocities in these specific joint rotations.

Gait changes due to aging can result in functional limitations and a higher risk of falls, with older adults showing alterations in joint angles and moments. Marker-based gait analysis is not widely used in clinical settings due to its complexity and discomfort, especially in older adults. Recent advances in markerless motion capture, such as Theia3D, offer a promising alternative. This study aims to assess the construct validity of a markerless motion capture system for gait analysis in healthy older adults. A cross-sectional study included 30 healthy community-dwelling older adults. Gait data was collected using marker-based and markerless motion capture systems in randomized order, with participants wearing tight-fitting minimal clothes plus 46 reflective markers attached, or their usual clothes, respectively. Joint kinematics (including range of motion) and kinetics were analyzed, and correlations between methods (Rxy) were assessed. Bland&Altman analysis was used to measure agreement. Root-mean-square differences (RMSD) were computed. Acceptable thresholds were set at ≤ 5º for kinematic and at ≤ 10 % of signal amplitude for kinetics. Strong correlations (Rxy≥0.7) were found between the systems for sagittal plane kinematics (except for the pelvis), particularly for knee and ankle joints. A low agreement was detected in sagittal plane hip and pelvis kinematics, along with RMSD exceeding 5º. Weaker correlations and poor agreement were observed for transverse and frontal plane motions. Overall strong correlations were found for kinetics, except for the joint ankle inversion-eversion moment, and poor agreement for the frontal and transverse planes. Overall markerless motion capture demonstrated good construct validity for measuring sagittal plane gait lower-limb gait kinematics (excluding pelvis) and kinetics in healthy older. However, considering the agreement between methods and the results for the other movement planes, further validation is required before markerless and marker-based systems can be used interchangeably in gait assessments.

The purpose of this research was to study the sensitivity of lower limb joint kinematics and kinetics, calculated during different functional tasks (walking, stair descent and stair ascent) in a sample of older adults, to different pose estimation algorithms and models’ joint constraints. Three models were developed and optimized differently: in one model, each segment had 6 degrees of freedom (segment optimization, SO), while in the other two, global optimization (GO) was used, with different joint constraints: (1) GO, allowing all joint rotations; (2) GOR, allowing three rotations at the hip, one at the knee (flexion/extension) and two at the ankle (dorsi/plantar flexion and eversion/inversion). The results showed that joint angles are more sensitive to the model’s constraints than joint moments and, the more restrictive the model, the higher the differences between models, especially for the frontal and transverse planes (max. RMS difference during gait: 11.7 degrees (64%) vs 0.12 N·m/kg (35.4%). In addition, except for knee abduction/adduction angle, differences between SO and GO models were relatively low. Since GO avoids the nonanatomical dislocations sometimes observed in SO, choosing this model seems to be reasonable for future studies with a similar sample and study design.