OBJECTIVE: The progression of osteoarthritis in lateral compartment has been identified as a primary complication in medial unicompartmental knee arthroplasty (UKA) revisions, irrespective of whether employing fixed bearing (FB) or mobile bearing (MB) designs. Compared to the previous contact point analyses, the tibiofemoral contacts during knee movements are comprehended by a more comprehensive understanding of joint spaces. This study aims to dynamically map the joint spaces in the lateral compartment during the single-leg lunge following FB and MB UKA procedures, and compare them with the respective contralateral native knees. It is hypothesized that the significant change in joint space for post-UKA compared to their native knees.
METHODS: Twelve patients with unilateral medial FB UKA and eleven patients with unilateral medial MB UKA were included and underwent computed tomography scans. The exclusion criteria included anterior cruciate ligament deficiency, postoperative knee pain, any postoperative complications, and musculoskeletal illnesses. A dual fluoroscopic imaging system was utilized to capture the single-leg lunge, and 2D-to-3D registration facilitated the visualization of knee motion. According to the knee motions, joint spaces on tibial and femoral surfaces in the lateral compartments of native, FB, and MB UKA knees were calculated and mapped.
RESULTS: In comparison to the native knees, FB UKA knees exhibited significant increases in medial, lateral, central, and posterior joint spaces in the lateral compartment (p < 0.05), while MB UKA knees showed significant increases only in central and posterior joint spaces (p < 0.05). Moreover, FB UKA demonstrated greater increases in medial, central, and posterior joint spaces compared to MB UKA. Tibial varus and valgus during lunges, as well as the Oxford Knee Score (OKS) and Hip-Knee-Ankle angle (HKA), correlated with joint spaces.
CONCLUSIONS: Dynamic joint space analysis provided a more comprehensive insight into contact dynamics. FB UKA led to an enlargement of joint spaces, whereas MB UKA resulted in joint spaces closer to native knees. These findings contribute to understanding potential postoperative complication in UKAs.

The limited sample size in gait studies has hampered progress in the field. This challenge could be addressed through multicenter studies, thereby leveraging data sets from different laboratories. This study compared 3-dimensional lower-extremity running kinematics between the Biomechanics and Motor Control Laboratory, Federal University of ABC (Brazil), and the Running Injury Clinic, University of Calgary (Canada). Three-dimensional lower-extremity kinematics from 23 male runners were collected from each laboratory using comparable instrumentation and experimental procedures. The 3-dimensional hip, knee, and ankle angles were compared within and between centers using root-mean-square deviation. Two-sample t tests Statistical Parametric Mapping tested the hypothesis that the data from both laboratories were not different. The sagittal plane hip, knee, and ankle angles were similar between laboratories, while notable differences were observed for frontal (hip and ankle) and transverse (hip and knee) plane angles. The average interlaboratory root-mean-square deviation (2.6°) was lower than the intralaboratory root-mean-square deviation (Biomechanics and Motor Control = 4.8°, Running Injury Clinic = 5.6°), with the ankle transverse angle displaying the smallest, and the knee transverse angle displaying the largest variability. This study demonstrates the potential of combining gait kinematics data from different laboratories to increase sample size, but frontal and transverse plane data should be considered with caution.

BACKGROUND: Markerless motion capture (MMC) uses video cameras or depth sensors for full body tracking and presents a promising approach for objectively and unobtrusively monitoring functional performance within community settings, to aid clinical decision-making in neurodegenerative diseases such as dementia.
OBJECTIVE: The primary objective of this systematic review was to investigate the application of MMC using full-body tracking, to quantify functional performance in people with dementia, mild cognitive impairment, and Parkinson disease.
METHODS: A systematic search of the Embase, MEDLINE, CINAHL, and Scopus databases was conducted between November 2022 and February 2023, which yielded a total of 1595 results. The inclusion criteria were MMC and full-body tracking. A total of 157 studies were included for full-text screening, out of which 26 eligible studies that met the selection criteria were included in the review. .
RESULTS: Primarily, the selected studies focused on gait analysis (n=24), while other functional tasks, such as sit to stand (n=5) and stepping in place (n=1), were also explored. However, activities of daily living were not evaluated in any of the included studies. MMC models varied across the studies, encompassing depth cameras (n=18) versus standard video cameras (n=5) or mobile phone cameras (n=2) with postprocessing using deep learning models. However, only 6 studies conducted rigorous comparisons with established gold-standard motion capture models.
CONCLUSIONS: Despite its potential as an effective tool for analyzing movement and posture in individuals with dementia, mild cognitive impairment, and Parkinson disease, further research is required to establish the clinical usefulness of MMC in quantifying mobility and functional performance in the real world.

UNASSIGNED: As motion capture technology becomes more popular for athlete monitoring and return-to-play evaluation, it is imperative that trunk mechanics are modeled similarly across participants. The purpose of this study was to determine how adjusting marker placement at the sternum or removing potentially occluded markers for purposes of tracking the trunk segment influences trunk kinematics during gait and a drop vertical jump (DVJ).
UNASSIGNED: Sagittal plane trunk angles of 18 participants were computed for a Definition Model and three trunk model variations. Model variations were specifically chosen to avoid difficulties with placement of the sternum and/or thorax markers in female participants due to sports bra coverage and/or occlusion. Intraclass correlation coefficients were computed per trunk model variation to determine agreement with the Definition Model.
UNASSIGNED: The Mid-Sternum model, in which the xiphoid process marker was adjusted to the midpoint of the xiphoid process and jugular notch, exhibited the least discrepancies and excellent agreement with the Definition Model across both tasks. Alternatively, the No-Thorax model, in which the thorax marker was removed, exhibited the greatest kinematic differences during the DVJ and moderate to excellent agreement across both tasks.
UNASSIGNED: The marker set chosen to track trunk motion during dynamic tasks must include locations that can be placed similarly on all participants. Based on these findings, the xiphoid process marker may be adjusted superiorly prior to the collection of dynamic trials. The recommended model for tracking the trunk segment includes marker placements on the jugular notch, mid-sternum, and 1st and 10th thoracic spinous processes.

BACKGROUND: Systems that capture motion under laboratory conditions limit validity in real-world environments. Mobile motion capture solutions such as Inertial Measurement Units (IMUs) can progress our understanding of \"real\" human movement. IMU data must be validated in each application to interpret with clinical applicability; this is particularly true for diverse populations. Our IMU analysis method builds on the OpenSim IMU Inverse Kinematics toolkit integrating the Versatile Quaternion-based Filter and incorporates realistic constraints to the underlying biomechanical model. We validate our processing method against the reference standard optical motion capture in a case report with participants with transfemoral amputation fitted with a Percutaneous Osseointegrated Implant (POI) and without amputation walking over level ground. We hypothesis that by using this novel pipeline, we can validate IMU motion capture data, to a clinically acceptable degree.
RESULTS: Average RMSE (across all joints) between the two systems from the participant with a unilateral transfemoral amputation (TFA) on the amputated and the intact sides were 2.35° (IQR = 1.45°) and 3.59° (IQR = 2.00°) respectively. Equivalent results in the non-amputated participant were 2.26° (IQR = 1.08°). Joint level average RMSE between the two systems from the TFA ranged from 1.66° to 3.82° and from 1.21° to 5.46° in the non-amputated participant. In plane average RMSE between the two systems from the TFA ranged from 2.17° (coronal) to 3.91° (sagittal) and from 1.96° (transverse) to 2.32° (sagittal) in the non-amputated participant. Coefficients of Multiple Correlation (CMC) results between the two systems in the TFA ranged from 0.74 to > 0.99 and from 0.72 to > 0.99 in the non-amputated participant and resulted in \'excellent\' similarity in each data set average, in every plane and at all joint levels. Normalized RMSE between the two systems from the TFA ranged from 3.40% (knee level) to 54.54% (pelvis level) and from 2.18% to 36.01% in the non-amputated participant.
CONCLUSIONS: We offer a modular processing pipeline that enables the addition of extra layers, facilitates changes to the underlying biomechanical model, and can accept raw IMU data from any vendor. We successfully validate the pipeline using data, for the first time, from a TFA participant using a POI and have proved our hypothesis.

BACKGROUND: Patients with hallux valgus are known to alter lower limb joint kinematics during gait. However, little information is available about gait changes following hallux valgus surgery. We aimed to longitudinally investigate lower limb kinematic changes at the mid and terminal stances of gait after hallux valgus surgery.
METHODS: This prospective observational study included 11 female patients (17 feet), who underwent first metatarsal osteotomy. Gait analyses were performed preoperatively and 1- and 2-year postoperatively using a three-dimensional motion capture system. Toe-out angle, ankle, knee, and hip joint angles during gait were calculated from the recorded data. The spatiotemporal parameters and these angles at the mid and terminal stances of gait were statistically compared between preoperative and postoperative periods.
RESULTS: All spatiotemporal parameters remained unchanged postoperatively. The toe-out angle was significantly greater at 1- and 2-year postoperatively. The ankle pronation angle, the knee abduction angle, and the hip adduction angle at the mid and terminal stances of gait were smaller postoperatively compared to the preoperative. These angular changes showed a similar trend at 1 and 2 years postoperatively. However, the postoperative changes of the sagittal joint angles were relatively small.
CONCLUSIONS: Hallux valgus surgery can affect the toe-out angle and the lower limb coronal kinematics at the mid and terminal stances of gait in patients with hallux valgus. However, surgical correction of hallux valgus deformity did not directly improve the gait characteristics in patients with hallux valgus.

BACKGROUND: Movement is a defining aspect of animals, but it is rarely studied using quantitative methods in microscopic invertebrates. Bdelloid rotifers are a cosmopolitan class of aquatic invertebrates of great scientific interest because of their ability to survive in very harsh environment and also because they represent a rare example of an ancient lineage that only includes asexually reproducing species. In this class, Adineta ricciae has become a model species as it is unusually easy to culture. Yet, relatively little is known of its ethology and almost nothing on how it behaves during feeding.
METHODS: To explore feeding behaviour in A. ricciae, as well as to provide an example of application of computational ethology in a microscopic invertebrate, we apply Procrustes motion analysis in combination with ordination and clustering methods to a laboratory bred sample of individuals recorded during feeding.
RESULTS: We demonstrate that movement during feeding can be accurately described in a simple two-dimensional shape space with three main \'modes\' of motion. Foot telescoping, with the body kept straight, is the most frequent \'mode\', but it is accompanied by periodic rotations of the foot together with bending while the foot is mostly retracted.
CONCLUSIONS: Procrustes motion analysis is a relatively simple but effective tool for describing motion during feeding in A. ricciae. The application of this method generates quantitative data that could be analysed in relation to genetic and ecological differences in a variety of experimental settings. The study provides an example that is easy to replicate in other invertebrates, including other microscopic animals whose behavioural ecology is often poorly known.

In recent years, preoperative planning has undergone significant advancements, with a dual focus: improving the accuracy of implant placement and enhancing the prediction of functional outcomes. These breakthroughs have been made possible through the development of advanced processing methods for 3D preoperative images. These methods not only offer novel visualization techniques but can also be seamlessly integrated into computer-aided design models. Additionally, the refinement of motion capture systems has played a pivotal role in this progress. These \"markerless\" systems are more straightforward to implement and facilitate easier data analysis. Simultaneously, the emergence of machine learning algorithms, utilizing artificial intelligence, has enabled the amalgamation of anatomical and functional data, leading to highly personalized preoperative plans for patients. The shift in preoperative planning from 2D towards 3D, from static to dynamic, is closely linked to technological advances, which will be described in this instructional review. Finally, the concept of 4D planning, encompassing periarticular soft tissues, will be introduced as a forward-looking development in the field of orthopedic surgery.

Researchers face the challenge of defining subject selection criteria when training algorithms for human activity recognition tasks. The ongoing uncertainty revolves around which characteristics should be considered to ensure algorithmic robustness across diverse populations. This study aims to address this challenge by conducting an analysis of heterogeneity in the training data to assess the impact of physical characteristics and soft-biometric attributes on activity recognition performance. The performance of various state-of-the-art deep neural network architectures (tCNN, hybrid-LSTM, Transformer model) processing time-series data using the IntelliRehab (IRDS) dataset was evaluated. By intentionally introducing bias into the training data based on human characteristics, the objective is to identify the characteristics that influence algorithms in motion analysis. Experimental findings reveal that the CNN-LSTM model achieved the highest accuracy, reaching 88%. Moreover, models trained on heterogeneous distributions of disability attributes exhibited notably higher accuracy, reaching 51%, compared to those not considering such factors, which scored an average of 33%. These evaluations underscore the significant influence of subjects\' characteristics on activity recognition performance, providing valuable insights into the algorithm\'s robustness across diverse populations. This study represents a significant step forward in promoting fairness and trustworthiness in artificial intelligence by quantifying representation bias in multi-channel time-series activity recognition data within the healthcare domain.

BACKGROUND: A mobile polyethylene liner enables the dual mobility cup (DMC) to contribute to restoring hip joint range-of-motion, decreasing wear and increasing implant stability. However, more data is required on how liner orientation changes with hip joint movement. As a first step towards better understanding liner orientation change in vivo, this cadaver study focuses on quantifying DMC liner orientation change after different hip passive movements, using ultrasound imaging and motion analysis.
OBJECTIVE: The liner does not always go back to its initial orientation and its final orientation depends mainly on hip movement amplitude.
METHODS: 3D ultrasound imaging and motion analysis were used to define liner and hip movements for four fresh post-mortem human subjects with six implanted DMC. Abduction and anteversion angles of the liner plane relative to the pelvis were measured before and after hip flexion, internal rotation, external rotation, abduction, adduction.
RESULTS: Liner orientation changes were generally defined by angle variation smaller than 5°, with the liner nearly going back to its initial orientation. However, hip flexion caused liner abduction and anteversion angle variations greater than 15°. Except for hip adduction, only weak or no correlation was found between the final angle of the liner and the maximal hip joint movement amplitude.
CONCLUSIONS: This study is the first attempt to quantify liner orientation change for implanted DMC via ultrasound imaging and constitutes a step forward in the understanding of liner orientation change and its relationship with hip joint movement. The hypothesis that the final liner abduction and anteversion angles depend mainly on hip movement amplitude was not confirmed, even if hip flexion was the movement generating the most liner orientation changes over 15°. This approach should be extended to in vivo clinical investigations, as measured liner angle variation could provide important support for the wear and stability claims made for DMC.
METHODS: IV; cadaveric study.