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.

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.

In older adults, gait analysis may detect changes that signal early disease states, yet challenges in biomechanical screening limit widespread use in clinical or community settings. Recently, a markerless method from multi-camera video data has become accessible, making screenings less challenging. This study evaluated the test-retest reliability and measurement error of markerless gait kinematics and kinetics in healthy older adults. Twenty-nine healthy older adults performed gait analysis on two occasions, at preferred walking speed, using their everyday clothes. Lower limb angles and moments were averaged from 8 gait cycles. Integrated pointwise indices [Intraclass Correlation Coefficient (ICC_A,K) and Standard Error of Measurement (SEM)] were calculated for curve data, as well as ICC_A,K, and SEM [95 % confidence intervals] for selected peaks. Generally, kinematic ICCs were good (>0.75) and reasonably stable throughout the gait cycle, except for the hip kinematics during the swing phase in the sagittal plane and pelvis tilt and rotation. The integrated and peaks SEM were 0.75), except for the transverse hip moment and abduction peak, fluctuating more during the swing than through the stance phase. SEM were < 0.07Nm/Kg. In conclusion, these results showed good overall test-retest reliability for markerless gait kinematics and kinetics for the hip, knee, and ankle joints, moderate for the pelvis angles, and error levels of ≤5°, and SEM%≤5% for the sagittal plane. This supports this method's use in assessing gait in healthy older adults, including kinetics, for which reliability data from markerless systems is difficult to find reported.