Motion Analysis Research Articles

Efficient diagnostic classification of diverse pathologies through contextual eye movement data analysis with a novel hybrid architecture

The analysis of eye movements has proven valuable for understanding brain function and the neuropathology of various disorders. This research aims to utilize eye movement data analysis as a screening tool for differentiation between eight different groups of pathologies, including scholar, neurologic, and postural disorders. Leveraging a dataset from 20 clinical centers, all employing AIDEAL and REMOBI eye movement technologies this study extends prior research by considering a multi-annotation setting, incorporating information from recordings from saccade and vergence eye movement tests, and using contextual information (e.g. target signals and latency of the eye movement relative to the target and confidence level of the quality of eye movement recording) to improve accuracy while reducing noise interference. Additionally, we introduce a novel hybrid architecture that combines the weight-sharing feature of convolution layers with the long-range capabilities of the transformer architecture to improve model efficiency and reduce the computation cost by a factor of 3.36, while still being competitive in terms of macro F1 score. Evaluated on two diverse datasets, our method demonstrates promising results, the most powerful discrimination being Attention & Neurologic; with a macro F1 score of up to 78.8%; disorder. The results indicate the effectiveness of our approach in classifying eye movement data from different pathologies and different clinical centers accurately, thus enabling the creation of an assistant tool in the future.

Few-Human-Interaction Reinforcement Learning for Autonomous Transbronchial Intervention.

The transbronchial interventional surgery presents challenges with winding and convoluted pathways, prone to compression and friction. Current autonomous planning struggles to reach deeper bronchial positions, and hard to consider multiple conflicting goals simultaneously. This article introduces an innovative planning scheme with preference weights to achieve smooth, frictionless, and collision-free autonomous transbronchial intervention with continuum robot (CR). A few-human-interaction twin-delayed deep deterministic policy gradient (FHITD3) generated from surgeon preference guidance is proposed, which determines the optimal strategy for the motion of CR. Preference knowledge is generated through interaction between human and few diversity samples. An abstract actuator space description is proposed for the posture and position representation of CR during movement within bronchus. A contact motion analysis strategy is proposed to calculate real-time attitude of CR in contact with bronchus. In addition, an oscillation suppression approach to address CR's unsmooth distal end trajectory is proposed. Simulated experiments show that the CR autonomously completes intervention tasks with a smooth and stable trajectory, reducing distal end oscillation by over 45%. It achieves a target endpoint within the fourth level bronchus (approximately 5 mm diameter) with over 90% probability.

The Role of Deep Learning and Gait Analysis in Parkinson's Disease: A Systematic Review.

Parkinson's disease (PD) is the second most common movement disorder in the world. It is characterized by motor and non-motor symptoms that have a profound impact on the independence and quality of life of people affected by the disease, which increases caregivers' burdens. The use of the quantitative gait data of people with PD and deep learning (DL) approaches based on gait are emerging as increasingly promising methods to support and aid clinical decision making, with the aim of providing a quantitative and objective diagnosis, as well as an additional tool for disease monitoring. This will allow for the early detection of the disease, assessment of progression, and implementation of therapeutic interventions. In this paper, the authors provide a systematic review of emerging DL techniques recently proposed for the analysis of PD by using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The Scopus, PubMed, and Web of Science databases were searched across an interval of six years (between 2018, when the first article was published, and 2023). A total of 25 articles were included in this review, which reports studies on the movement analysis of PD patients using both wearable and non-wearable sensors. Additionally, these studies employed DL networks for classification, diagnosis, and monitoring purposes. The authors demonstrate that there is a wide employment in the field of PD of convolutional neural networks for analyzing signals from wearable sensors and pose estimation networks for motion analysis from videos. In addition, the authors discuss current difficulties and highlight future solutions for PD monitoring and disease progression.

The biomechanical assessment of two stemless shoulder arthroplasty prostheses in uniformly poor-quality bone mineral density cadaveric specimens

BackgroundStemless shoulder arthroplasty offers several advantages, such as preserving bone stock and reducing periprosthetic fracture risk. However, implant motion can deter osteointegration and increase bone resorption, where micromotion less than 0.150 mm is crucial for bony ingrowth and vital to the success of the implant. The interaction between the implant and the metaphyseal bone and its effects on stability remains unclear. Therefore, this cadaveric study aims to assess the immediate stability of two stemless prostheses in low bone density specimens. MethodsTwenty cadaveric shoulders were used to compare the stability of two stemless shoulder implants by Zimmer-Biomet (model A) and Exactech (model B), subjected to loads of 220 N, 520 N, and 820 N to assess strain and micromotion. FindingsMicromotion at 220 N load was 0.061 ± 0.080 mm and 0.053 ± 0.050 mm, and at 520 N load, 0.279 ± 0.37 mm and 0.311 ± 0.35 mm for models A and B, respectively. The estimated mean force required to achieve a 150 μm micromotion was 356 ± 116 N and 315 ± 61 N for models A and B, respectively. Motion analysis revealed distinct movement patterns for each implant, with model B demonstrating better force distribution on the bone despite no significance. InterpretationForces over 520 N (high postoperative rehabilitation force) could hinder bone integration with prostheses due to excessive micromotion. Conversely, forces around 220 N (preconditioning loading force) are considered safe for prosthesis stability even with low bone density. These insights may caution against using stemless implants when bone density is low, and help guide clinical decisions on the duration of rehabilitation and sling use after stemless arthroplasty.

The early peak knee abduction moment waveform is a novel risk factor predicting anterior cruciate ligament injury in young athletes: A prospective study.

In this study, prospective data were used to evaluate whether the early peak knee abduction moment waveform is associated with the risk of anterior cruciate ligament (ACL) injury. Biomechanical data from 84 athletes who participated in the study as adolescents were analysed after cross-referencing national health registry data to confirm ACL reconstruction in the subsequent years. The knee abduction moment waveform shape was obtained with cluster analysis for the first 100 ms of a cutting manoeuvre (1776 trials in total) and classified as either containing an early peak knee abduction moment or not, and the odds ratio for later ACL injury was then calculated. Additionally, discrete kinematic and kinetic variables were extracted, and tested against the risk of ACL injury using mixed model logistic regression. Of 84 athletes, 8 (all female) sustained a total of 13 ACL injuries in the years after motion analysis data collection. Six clusters of knee abduction moment waveform shapes were identified. Two clusters containing 446 trials were classified as an early peak knee abduction waveform. This waveform was associated with a 7.2-fold increase in the risk of ACL injury (95% confidence interval: 2.4-24.6; p < 0.001). Of the kinematic and kinetic variables tested, only the knee abduction angle at initial contact was associated with an increased risk of ACL injury (p < 0.001). This is the first study to confirm the association between the early peak knee abduction moment waveform and the risk of ACL injury. Using waveforms, instead of discrete peak values of the knee abduction moment, may better represent risky movement patterns. Replicating these findings in a larger cohort will support the use of this method to screen athletes for risk and guide targeted preventive interventions and their efficacy. Level II.

Enhancing Myocardial Infarction Diagnosis: LSTM-based Deep Learning Approach Integrating Echocardiographic Wall Motion Analysis

PurposeOwing to the increased mortality of heart diseases worldwide, especially myocardial infarction (MI), early detection is essential for improved diagnosis and treatment. The main purpose of this study is to develop a myocardial infarction detection method that combines deep learning and image processing, focusing on abnormalities in left ventricular (LV) wall motion.MethodsThe proposed method primarily uses the LV wall motion movement as a feature to train an LSTM network for MI detection. LV wall motion annotated by expert cardiologists was used as the ground truth. Accuracy, sensitivity, specificity, and area under the curve (AUC) were used to evaluate model performance. The proposed method primarily uses LV wall motion as a feature, combined with LV size and image pixels, to improve diagnostic accuracy over existing computer-aided design (CAD) systems.ResultsThe LSTM model achieved the highest diagnostic performance when trained on a combination of LV wall motion, LV size, and image pixel features with an accuracy of 95%, sensitivity of 96%, specificity of 94%, and an AUC value of 0.98. The LSTM model significantly outperformed models trained on individual feature sets or conventional machine learning algorithms. The inclusion of LV wall motion analysis improved accuracy by 10% compared to using only LV size and pixel data.ConclusionOur MI diagnosis system uses echocardiographic image analysis and LSTM-based deep learning to accurately detect LV wall motion issues related to MI. Compared with current CAD systems, the inclusion of LV wall motion analysis significantly improves diagnosis accuracy. The proposed system can help physicians detect MI early, thereby accelerating treatment and improving patient outcomes.

Understanding the Energy-Saving mechanism of ceramic balls in tumbling mills

This study investigates why ceramic balls achieve superior grinding performance compared to steel balls at lower densities and lower energy consumption. Particle motion analysis shows that increasing the filling level significantly improves the velocity distribution of the grinding media. The energy input from the mill is mainly converted into the kinetic and potential energy of the media, with potential energy being dominant. As the filling level increases, the efficiency of kinetic energy conversion improves. Collisions between media and mineral particles dominate energy transfer, and lower media density enhances collision energy distribution uniformity. Reducing media density decreases grinding energy consumption while maintaining the same grinding effect.

Effect of Screw Length in Volar Plating for Intra-articular Distal Radius Fractures: A Biomechanical Study

PurposeThe purpose of this study was to compare the effect of varying screw lengths on load to failure and retention of the dorsal ulnar corner fragment after fixation of comminuted intra-articular distal radius fractures in a cadaveric model. MethodsTwenty-four fresh frozen cadaveric forearms were subjected to a standardized distal radius osteotomy to mimic an intra-articular fracture pattern. Dual X-ray absorptiometry scans were performed to ensure minimal variability in bone density. All fractures were fixed with a volar locking plate and distal locking screws. Three different lengths of distal locking screws were used in each group of eight specimens to simulate the clinical decision of different distal screw lengths. The screw lengths tested were bicortical, 100% of the width of the bone but unicortical, and 75% of the width of the bone and unicortical. All specimens were preconditioned with cyclic axial loading and then axially loaded using matching acrylic resin molds to clinical failure and fragment displacement as detected by a motion analysis system. Retention or loss of the dorsal ulnar corner fragment during loading was recorded as a binary variable. ResultsBetween the three groups, there were no statistically significant differences in precycling stiffness, postcycling stiffness, load at 2 mm displacement of the dorsal ulnar corner, or force at failure. The group with 75% length screws had a significantly higher loss of reduction of the dorsal ulnar corner (86%) compared with the other groups (0%). ConclusionsVarying screw lengths did not affect the stiffness or overall loads to failure of axially loaded specimens. However, the 75% length screws did not reliably secure the dorsal ulnar corner fragments. Although this did not significantly affect the overall load to failure of the construct, displacement of this fragment may have implications for rotation of the forearm through the distal radioulnar joint. Clinical relevanceSurgeons should consider the utilization of full-length unicortical locking screws to ensure adequate fixation of the dorsal ulnar corner. Type of study/level of evidenceBiomechanical study V.

Radial velocity estimation method for a single hydrophone based on passive target line spectrum characteristics

Passive radial velocity estimation based on a single hydrophone has a wide range of applications in engineering, and can be used in small underwater platforms such as submerged buoys and underwater gliders. The conventional passive radial velocity estimation method based on a single hydrophone has poor performance due to the influence of noise. In this paper, the time delay coherence coefficient (TDCC) of passive target line spectrum is derived based on a single hydrophone. Further, the radial velocity can be obtained by the time delay required by the phase change of TDCC for one period. Based on the characteristics that the line spectrum phase is stable and the noise phase is random, coherent averaging (CA) is applied to suppress noise. The use of CA greatly reduces the influence of noise on TDCC and efficiently improves the accuracy of radial velocity estimation. The performance and system error of the proposed CA-TDCC method are analyzed through a series of simulations. Finally, the CA-TDCC method is used to process SwellEx-96 data, and the relative error of radial velocity estimation is less than 10%, which verifies the effectiveness of this method in practical applications.Abbreviation: CA, Coherent averaging; SNRs, Signal-to-noise ratios; TDCC, Time delay coherence coefficient; TMA, Target motion analysis.

Validity and Reliability of OpenPose-Based Motion Analysis in Measuring Knee Valgus during Drop Vertical Jump Test.

OpenPose-based motion analysis (OpenPose-MA), utilizing deep learning methods, has emerged as a compelling technique for estimating human motion. It addresses the drawbacks associated with conventional three-dimensional motion analysis (3D-MA) and human visual detection-based motion analysis (Human-MA), including costly equipment, time-consuming analysis, and restricted experimental settings. This study aims to assess the precision of OpenPose-MA in comparison to Human-MA, using 3D-MA as the reference standard. The study involved a cohort of 21 young and healthy adults. OpenPose-MA employed the OpenPose algorithm, a deep learning-based open-source two-dimensional (2D) pose estimation method. Human-MA was conducted by a skilled physiotherapist. The knee valgus angle during a drop vertical jump task was computed by OpenPose-MA and Human-MA using the same frontal-plane video image, with 3D-MA serving as the reference standard. Various metrics were utilized to assess the reproducibility, accuracy and similarity of the knee valgus angle between the different methods, including the intraclass correlation coefficient (ICC) (1, 3), mean absolute error (MAE), coefficient of multiple correlation (CMC) for waveform pattern similarity, and Pearson's correlation coefficients (OpenPose-MA vs. 3D-MA, Human-MA vs. 3D-MA). Unpaired t-tests were conducted to compare MAEs and CMCs between OpenPose-MA and Human-MA. The ICCs (1,3) for OpenPose-MA, Human-MA, and 3D-MA demonstrated excellent reproducibility in the DVJ trial. No significant difference between OpenPose-MA and Human-MA was observed in terms of the MAEs (OpenPose: 2.4° [95%CI: 1.9-3.0°], Human: 3.2° [95%CI: 2.1-4.4°]) or CMCs (OpenPose: 0.83 [range: 0.99-0.53], Human: 0.87 [range: 0.24-0.98]) of knee valgus angles. The Pearson's correlation coefficients of OpenPose-MA and Human-MA relative to that of 3D-MA were 0.97 and 0.98, respectively. This study demonstrated that OpenPose-MA achieved satisfactory reproducibility, accuracy and exhibited waveform similarity comparable to 3D-MA, similar to Human-MA. Both OpenPose-MA and Human-MA showed a strong correlation with 3D-MA in terms of knee valgus angle excursion.

Variable angle tow-steered fibres based rotating composite beam with rotary oscillations – Parametric excitation/instability analysis

This study is concerned with the stability analysis for various vibrational motions of tow-steered variable angle fibres based composite rotating beam with time varying speed. The formulation combines a higher order theory introducing sine function along with finite element methodology. It satisfies the plane stress condition in width direction of beam. The displacement kinematics are generic in the sense it includes various possible vibrational motions like chord and flap wise motions, torsional and axial vibration behaviours. The equilibrium equations for the proposed problem evolved adopting virtual dynamic work are then transformed into the equations of Mathieu-Hill type considering time varying harmonic rotational speed. Using Bolotin’s procedure coupled with C1 continuity-based beam element, the parametric resonance zones along with boundary limits are numerically evaluated. The eigenvalue type of solution methodology applied for the detailed study is validated for accuracy and computational efficiency against the time domain dynamic response analysis. Based on in-depth analysis, dynamic instability characteristics in terms of resonance range and origin of instability of composite beam subjected to the chord and flap wise, torsional and axial motions are studied considering curvilinear fibre path angles, beam cross section, hub radius, thickness ratio and mean rotary speed.

Advancing healthcare practice and education via data sharing: demonstrating the utility of open data by training an artificial intelligence model to assess cardiopulmonary resuscitation skills.

Health professional education stands to gain substantially from collective efforts toward building video databases of skill performances in both real and simulated settings. An accessible resource of videos that demonstrate an array of performances - both good and bad-provides an opportunity for interdisciplinary research collaborations that can advance our understanding of movement that reflects technical expertise, support educational tool development, and facilitate assessment practices. In this paper we raise important ethical and legal considerations when building and sharing health professions education data. Collective data sharing may produce new knowledge and tools to support healthcare professional education. We demonstrate the utility of a data-sharing culture by providing and leveraging a database of cardio-pulmonary resuscitation (CPR) performances that vary in quality. The CPR skills performance database (collected for the purpose of this research, hosted at UK Data Service's ReShare Repository) contains videos from 40 participants recorded from 6 different angles, allowing for 3D reconstruction for movement analysis. The video footage is accompanied by quality ratings from 2 experts, participants' self-reported confidence and frequency of performing CPR, and the demographics of the participants. From this data, we present an Automatic Clinical Assessment tool for Basic Life Support that uses pose estimation to determine the spatial location of the participant's movements during CPR and a deep learning network that assesses the performance quality.

Is a cork a legal shortcut? – A comparison of the measured and assumed amount of rotation in freestyle tricks

ABSTRACT In snowboard freestyle disciplines, the amount of rotation is commonly determined as the sum of rotations around all board axes and is the most important indicator of the trick difficulty across all snowboard freestyle disciplines. Based on the type of rotation, tricks can be classified as flatspins, corks and flips. It is not yet known whether the type of rotation of a trick can influence the actual amount of rotation. Therefore, the aim of this study was to determine the amount of deviation, defined as difference between measured and assumed amount of rotation as a function of trick classification, using kinematic motion analysis. The amount of deviation was positive for flatspins (median: 21°; min: −4°; max: 49°) and negative for corks (median: −25°; min: −89°; max: 12°) and flips (median: −28°; min: −94°; max: 13°). Our results demonstrate that there are ways of execution where riders perform corks and flips with a shortcut and flatspins with a detour. This should be taken into account by judges, coaches and riders. Further research is needed to investigate how the shortcut can be influenced.

Evaluating Joint Angle Data for Clinical Assessment Using Multidimensional Inverse Kinematics with Average Segment Morphometry.

Movement analysis is a critical tool in understanding and addressing various disabilities associated with movement deficits. By analyzing movement patterns, healthcare professionals can identify the root causes of these alterations, which is essential for preventing, diagnosing, and rehabilitating a broad spectrum of medical conditions, disabilities, and injuries. With the advent of affordable motion capture technologies, quantitative data on patient movement is more accessible to clinicians, enhancing the quality of care. Nonetheless, it is crucial that these technologies undergo rigorous validation to ensure their accuracy in collecting and monitoring patient movements, particularly for remote healthcare services where direct patient observation is not possible. In this study, motion capture technology was used to track upper extremity movements during a reaching task presented in virtual reality. Kinematic data was then calculated for each participant using a scaled dynamic inertial model. The goal was to evaluate the accuracy of joint angle calculations using inverse kinematics from motion capture relative to the typical movement redundancy. Shoulder, elbow, radioulnar, and wrist joint angles were calculated with models scaled using either direct measurements of each individual's arm segment lengths or those lengths were calculated from individual height using published average proportions. The errors in joint angle trajectories calculated using the two methods of model scaling were compared to the inter-trial variability of those trajectories. The variance of this error was primarily within the normal range of variability between repetitions of the same movements. This suggests that arm joint angles can be inferred with good enough accuracy from motion capture data and individual height to be useful for the clinical assessment of motor deficits.

Beyond Inverse Dynamics: Methods for Assessment of Individual Muscle Function during Gait.

Three-dimensional motion analysis performed in the modern gait analysis laboratory provides a wealth of information about the kinematics and kinetics of human locomotion, but standard gait analysis is largely restricted to joint-level measures. Three-dimensional joint rotations, joint moments, and joint powers tell us a great deal about gait mechanics, but it is often of interest to know about the roles that muscles play. This narrative review surveys work that has been done, largely over the past four decades, to augment standard gait analysis with muscle-level assessments of function. Often, these assessments have incorporated additional technology such as ultrasound imaging, or complex modeling and simulation techniques. The review discusses measurements of muscle moment arm during walking along with assessment of muscle mechanical advantage, muscle-tendon lengths, and the use of induced acceleration analysis to determine muscle roles. In each section of the review, examples are provided of how the auxiliary analyses have been used to gain potentially useful information about normal and pathological human walking. While this work highlights the potential benefits of adding various measures to gait analysis, it is acknowledged that challenges to implementation remain, such as the need for specialized knowledge and the potential for bias introduced by model choices.

Automatic motion estimation with applications to hiPSC-CMs

Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) are an effective tool for studying cardiac function and disease, and hold promise for screening drug effects on human tissue. Understanding alterations in motion patterns within these cells is crucial for comprehending how the administration of a drug or the onset of a disease can impact the rhythm of the human heart. However, quantifying motion accurately and efficiently from optical measurements using microscopy is currently time consuming. In this work, we present a unified framework for performing motion analysis on a sequence of microscopically obtained images of tissues consisting of hiPSC-CMs. We provide validation of our developed software using a synthetic test case and show how it can be used to extract displacements and velocities in hiPSC-CM microtissues. Finally, we show how to apply the framework to quantify the effect of an inotropic compound. The described software system is distributed as a python package that is easy to install, well tested and can be integrated into any python workflow.

Synthetic ground motions in heterogeneous geologies from various sources: the HEMEWS-3D database

Abstract. The ever-improving performances of physics-based simulations and the rapid developments of deep learning are offering new perspectives to study earthquake-induced ground motion. Due to the large amount of data required to train deep neural networks, applications have so far been limited to recorded data or two-dimensional (2D) simulations. To bridge the gap between deep learning and high-fidelity numerical simulations, this work introduces a new database of physics-based earthquake simulations. The HEterogeneous Materials and Elastic Waves with Source variability in 3D (HEMEWS-3D) database comprises 30 000 simulations of elastic wave propagation in 3D geological domains. Each domain is parametrized by a different geological model built from a random arrangement of layers augmented by random fields that represent heterogeneities. Elastic waves originate from a randomly located pointwise source parametrized by a random moment tensor. For each simulation, ground motion is synthesized at the surface by a grid of virtual sensors. The high frequency of waveforms (fmax⁡=5 Hz) allows for extensive analyses of surface ground motion. Existing and foreseen applications range from statistical analyses of the ground motion variability and machine learning methods on geological models to deep-learning-based predictions of ground motion that depend on 3D heterogeneous geologies and source properties. Data are available at https://doi.org/10.57745/LAI6YU (Lehmann, 2023).

Automated Analysis of Stereotypical Movements in Videos of Children With Autism Spectrum Disorder

Stereotypical motor movements (SMMs) are a form of restricted and repetitive behavior, which is a core symptom of autism spectrum disorder (ASD). Current quantification of SMM severity is extremely limited, with studies relying on coarse and subjective caregiver reports or laborious manual annotation of short video recordings. To assess the utility of a new open-source AI algorithm that can analyze extensive video recordings of children and automatically identify segments with heterogeneous SMMs, thereby enabling their direct and objective quantification. This retrospective cohort study included 241 children (aged 1.4 to 8.0 years) with ASD. Video recordings of 319 behavioral assessments carried out at the Azrieli National Centre for Autism and Neurodevelopment Research in Israel between 2017 and 2021 were extracted. Behavioral assessments included cognitive, language, and autism diagnostic observation schedule, 2nd edition (ADOS-2) assessments. Data were analyzed from October 2020 to May 2024. Each assessment was recorded with 2 to 4 cameras, yielding 580 hours of video footage. Within these extensive video recordings, manual annotators identified 7352 video segments containing heterogeneous SMMs performed by different children (21.14 hours of video). A pose estimation algorithm was used to extract skeletal representations of all individuals in each video frame and was trained an object detection algorithm to identify the child in each video. The skeletal representation of the child was then used to train an SMM recognition algorithm using a 3 dimensional convolutional neural network. Data from 220 children were used for training and data from the remaining 21 children were used for testing. Among 319 behavioral assessment recordings from 241 children (172 [78%] male; mean [SD] age, 3.97 [1.30] years), the algorithm accurately detected 92.53% (95% CI, 81.09%-95.10%) of manually annotated SMMs in our test data with 66.82% (95% CI, 55.28%-72.05%) precision. Overall number and duration of algorithm-identified SMMs per child were highly correlated with manually annotated number and duration of SMMs (r = 0.8; 95% CI, 0.67-0.93; P < .001; and r = 0.88; 95% CI, 0.74-0.96; P < .001, respectively). This study suggests the ability of an algorithm to identify a highly diverse range of SMMs and quantify them with high accuracy, enabling objective and direct estimation of SMM severity in individual children with ASD.

Optimizing the patellofemoral compartment in total knee arthroplasty: Is it time for dynamic assessment?

Despite improvements in implant design, surgical techniques and assistive technologies for total knee arthroplasty (TKA), anterior knee pain (AKP) remains frequently reported, even by satisfied patients. This persistent problem calls for better understanding and management of the patellofemoral or anterior compartment during surgery, just as the techniques and strategies deployed to optimize the flexion and extension spaces through personalized alignment, bone cuts and ligament balancing. Assistive technologies such as navigation and robotics provide new tools to manage this 'third space' through precise pre-operative planning and dynamic intra-operative assessment. Such endeavors must start with clear definitions of the 'third space', how it should be measured, what constitutes its 'safe zone', and how it affects outcomes. There are yet no established methods to evaluate the patellofemoral compartment, and no clear thresholds to define over- or under-stuffing. Static assessment using lateral radiographs provides a limited understanding and depends considerably on flexion angle, while dynamic evaluation at multiple flexion angles or using intra-operative computer or robotic-assistance enables a broader perspective and solutions to manage patellar tracking and anterior offset. Future studies should investigate the impact of variations in anterior offset in TKA, define its safe zone, and understand the effects of of thresholds for over- or under-stuffing. Experimental methods such as in-vivo motion analysis and force sensors could elucidate the influence of anterior offset on flexion and extension biomechanics.

Analysis of Head Movement in KPSIT Dummies and the Impact of Seats and Seat Belts during Low-Speed Collisions 20 km/h.

The aim of this study was to compare the head displacement of the KPSIT C50 dummy, representing a 50th percentile male, with the KPSIT C5 dummy, representing a 5th percentile female, during low-speed collisions. Low-speed collisions, such as those occurring in urban traffic jams, are increasingly common. The research was conducted on a dedicated educational workstation designed to measure forces in seat belts. This study is part of a comprehensive research project on crash tests involving both volunteers and physical KPSIT dummies. The tests were conducted at a speed of 20 km/h to simulate real-world low-speed collision scenarios. The findings demonstrate that using a sports bucket seat with four-point or five-point harnesses significantly reduces head displacement compared with standard car seats. Such seating configurations enhance safety by minimizing the risk of head injuries, which can occur when airbags do not deploy during low-speed collisions. Moreover, the study highlights that standard three-point seat belts allow for greater forward head movement, increasing the risk of head contact with the vehicle's interior during collisions at speeds too low to trigger airbag deployment.