Effect of tibia marker placement on knee joint kinematic analysis

Marker location and knee joint constraint affect the reporting of overhead squat kinematics in elite youth football players

Motion capture systems are used in the analysis and interpretation of athlete movement patterns for a variety of reasons, but data integrity remains critical regardless. The extent to which marker location or constraining degrees of freedom (DOF) in the biomechanical model impacts on this integrity lacks consensus. Ten elite academy footballers performed bilateral overhead squats using a marker-based motion capture system. Kinematic data were calculated using four different marker sets with 3DOF and 6DOF configurations for the three joint rotations of the right knee. Root mean squared error differences between marker sets ranged in the sagittal plane between 1.02 and 4.19 degrees to larger values in the frontal (1.30-6.39 degrees) and transverse planes (1.33 and 7.97 degrees). The cross-correlation function of the knee kinematic time series for all eight marker-sets ranged from excellent for sagittal plane motion (>0.99) but reduced for both coronal and transverse planes (<0.9). Two-way ANOVA repeated measures calculated at peak knee flexion revealed significant differences between marker sets for frontal and transverse planes (p < 0.05). Pairwise comparisons showed significant differences between some marker sets. Marker location and constraining DOF while measuring relatively large ranges of motion in this population are important considerations for data integrity.

Human Signature Identification Using IoT Technology and Gait Recognition

This study aimed to develop an autonomous design system for recognizing the subject by gait posture. Gait posture is a type of non-verbal communication characteristic of each person, and can be considered a signature used in identification. This system can be used for diagnosis. The system helps aging or disabled subjects to identify incorrect posture to recover the gait. Gait posture gives information for subject identification using leg movements and step distance as characteristic parameters. In the current study, the inertial measurement units (IMUs) located in a mobile phone were used to provide information about the movement of the upper and lower leg parts. A resistive flex sensor (RFS) was used to obtain information about the foot contact with the ground. The data were collected from a target group comprising subjects of different age, height, and mass. A comparative study was undertaken to identify the subject after the gait posture. Statistical analysis and a machine learning algorithm were used for data processing. The errors obtained after training data are presented at the end of the paper and the obtained results are encouraging. This article proposes a method of acquiring data available to anyone by using indispensable devices purchased by all users such as mobile phones.

Quantifying Soft Tissue Artefacts and Imaging Variability in Motion Capture of the Fingers

This study assessed the accuracy of marker-based kinematic analysis of the fingers, considering soft tissue artefacts (STA) and marker imaging uncertainty. We collected CT images of the hand from healthy volunteers with fingers in full extension, mid- and full-flexion, including motion capture markers. Bones and markers were segmented and meshed. The bone meshes for each volunteer's scans were aligned using the proximal phalanx to study the proximal interphalangeal joint (PIP), and using the middle phalanx to study the distal interphalangeal joint (DIP). The angle changes between positions were extracted. The HAWK protocol was used to calculate PIP and DIP joint flexion angles in each position based on the marker centroids. Finally the marker locations were 'corrected' relative to the underlying bones, and the flexion angles recalculated. Static and dynamic marker imaging uncertainty was evaluated using a wand. A strong positive correlation was observed between marker- and CT-based joint angle changes with 0.980 and 0.892 regression slopes for PIP and DIP, respectively, and Root Mean Squared Errors below 4°. Notably for the PIP joint, correlation was worsened by STA correction. The 95% imaging uncertainty interval was < ± 1° for joints, and < ± 0.25 mm for segment lengths. In summary, the HAWK marker set's accuracy was characterised for finger joint flexion angle changes in a small group of healthy individuals and static poses, and was found to benefit from skin movements during flexion.

Technical note: sensitivity analysis of the SCoRE and SARA methods for determining rotational axes during tibiofemoral movements using optical motion capture

Purpose

The first aim was to report the sensitivity of calculated tibiofemoral movements for the choice of placement of the set of femoral markers. The second aim was to report the influence of accuracy of the motion captured positions of the markers on the calculated tibiofemoral movements.

Methods

Tibiofemoral kinematics during single leg hops for distance were calculated. For the first aim, an experiment was conducted in which four different setups of the femoral markers were used to calculated tibiofemoral movements. For the second aim, an experiment was conducted in which all raw marker positions were mathematically moved independently with the known Vicon position error with a distance and in a random direction in each frame, repeated a hundred times. Each time, the tibiofemoral movements were calculated.

Results

The first experiment yields that the standard deviation of the calculated anterior tibia translation between marker setups was 0.88 mm and the standard deviation of the external tibia rotation between marker setups was 0.76 degrees. The second experiment yields that the standard deviation was 0.76 mm for anterior tibia translation and 0.38 degrees for external tibia rotation.

Conclusion

A combined standard deviation of both experiments revealed that transients in anterior tibia translation less than 2.32 mm and external tibia rotations less than 1.70 degrees should be taken with caution. These results are 19.42% of the range of the anterior tibia translation and 13.51% of the rotation range during the jump task. The marker setup should be chosen carefully.

The sensitivity of joint kinematics and kinetics to marker placement during a change of direction task

The conventional gait model (CGM) refers to several closely related biomechanical models used in the objective analysis of human motion. Their use has become popular in the analysis of change of direction tasks to inform best practice in the prevention and rehabilitation of anterior cruciate ligament injury. As externally-placed markers define segment axes origins and orientations, kinematic and kinetic outputs from the CGM are sensitive to marker placement. The aim of this investigation was to quantify the sensitivity of lower extremity kinematics and knee moments to systematic differences in marker placement across the stance phase of a change of direction task. Systematic anterior/posterior displacements were applied to the lateral thigh, femoral epicondyle and tibia markers in software. One-dimensional statistical parametric mapping was used to determine the effect of marker placement across the entire stance phase of a 90° change of direction task. Marker placement error within previously reported inter-tester variability ranges caused significant differences in knee abduction moment, hip rotation angle, knee rotation angle, ankle abduction and rotation angle across various periods of stance. Discrete measures of these variables have been associated with increased frontal plane knee loading during change of direction, considered a key mechanism of anterior cruciate ligament injury. Systematic differences in marker placement may lead to incorrect group statistical inferences in such discrete measures.