3D Motion Research Articles

Development of a Tremor Detection Algorithm for Use in an Academic Movement Disorders Center.

Tremor, defined as an "involuntary, rhythmic, oscillatory movement of a body part", is a key feature of many neurological conditions including Parkinson's disease and essential tremor. Clinical assessment continues to be performed by visual observation with quantification on clinical scales. Methodologies for objectively quantifying tremor are promising but remain non-standardized across centers. Our center performs full-body behavioral testing with 3D motion capture for clinical and research purposes in patients with Parkinson's disease, essential tremor, and other conditions. The objective of this study was to assess the ability of several candidate processing pipelines to identify the presence or absence of tremor in kinematic data from patients with confirmed movement disorders and compare them to expert ratings from movement disorders specialists. We curated a database of 2272 separate kinematic data recordings from our center, each of which was contemporaneously annotated as tremor present or absent by a movement physician. We compared the ability of six separate processing pipelines to recreate clinician ratings based on F1 score, in addition to accuracy, precision, and recall. The performance across algorithms was generally comparable. The average F1 score was 0.84±0.02 (mean ± SD; range 0.81-0.87). The second highest performing algorithm (cross-validated F1=0.87) was a hybrid that used engineered features adapted from an algorithm in longstanding clinical use with a modern Support Vector Machine classifier. Taken together, our results suggest the potential to update legacy clinical decision support systems to incorporate modern machine learning classifiers to create better-performing tools.

Targeting ankle proprioception: A novel, valid, and reliable approach to measuring ankle proprioception for clinical implementation: Suggested short title: Targeting ankle proprioception

Ankle proprioception is vital to maintain balance but proprioceptive measurement is challenging in clinic. A new clinical measure of ankle proprioception, measuring joint position error (JPE), was designed and tested for validity and reliability. A total of 20 young healthy adults attended 2 visits. Subjects performed a repositioning task with a laser affixed to the foot, projected to a wall target, for all four planes of ankle motion. Laser JPE was calculated and validated against a 3D motion capture camera system. Average ankle laser JPE was 4.25° (±1.20) across all motions. Laser JPE was strongly associated with camera JPE, with over 95 % agreement. Laser JPE also showed excellent agreement within person and across days (ICC≥0.91). These strong results, combined with ease of setup and procedures, suggest our new measure of ankle proprioception is clinically feasible and appropriate.

Assessment of Hip and Lumbar Spine Range of Motion After Total Hip Arthroplasty Using a Single Camera Markerless System.

Total hip arthroplasty (THA) is one of the most cost-effective and successful procedures in orthopedics. However, assessing the post-operative range of motion (ROM) remains a challenge due to the limitations of traditional measurement methods. This study aimed to evaluate hip and spine ROMpost-operativelyand single-leg balance, using a single-camera markerless motion capture system, and compare outcomes with pre-operative ROM and with an age-matched healthy control group. An interventional study was conducted from January 2018 to December 2021. Twenty patients with hip osteoarthritisunderwent THA and were assessed using a single-camera markerless system (Kinetisense software). Measurements were taken one month pre-operatively and one year post-operatively. Significant improvements were observed in hip and lumbar spine ROM variables after THA. The most notable enhancements were in hip and spinal flexion. Compared to the control group, the THA group showed minor deficits in hip ROM, particularly in external rotation. Single-leg balance demonstrated improved stability post-operatively. The single-camera markerless motion capture system offers a promising alternative for assessing hip and lumbar spine ROM, presenting potential advantages over manual goniometry and traditional 3D motion capture systems. Using this system for the evaluation of patients after THA, it seems that THA significantly enhances hip and lumbar spine ROM. Future research should focus on validating the accuracy of markerless systems.

Effects of various freak waves on dynamic responses of a Spar-buoy floating offshore wind turbine

There are various kind of waves in the actual ocean. Changes on freak wave profiles affect the motion of floating offshore wind turbines (FOWTs) and threaten the operation safety. To investigate this issue, the phase modulation method is used to simulate freak waves in our work. The original freak wave is first generated, then the wave profile is modulated to obtain five other types of freak waves, such as the reversed wave, the high-crested wave, the deep-troughed wave, the fluctuated wave, the elevated wave. A coupled time-domain model of a Spar-buoy FOWT is established to simulate the motion under steady wind and various freak waves. The dynamic responses are analyzed in the time domain, including the three-degree-of-freedom (3-DOF) motions of the floating foundation, the tension of mooring line and the nacelle acceleration. The frequency-domain properties under various freak waves are investigated using wavelet analysis. The effects of various wave profiles are compared with the original freak wave. Freak waves in low sea states are also generated to research their effects on pitch motion in different sea states. Results show that the response peaks increase in the impact region under freak waves. The reversed wave increases the maximum value of the 3-DOF motions, especially in pitch motion, and causes the high-frequency energy core of wavelet plots to move. The dynamic responses under the fluctuated wave combines the properties of increased response peaks at high-crested wave and increased amplitude of low-frequency motions at deep-troughed wave. The elevated wave greatly increases the heave motion of the FOWT. The increase in sea state level causes a nonlinear amplification in the transient oscillation.

LiDARCapV2: 3D human pose estimation with human–object interaction from LiDAR point clouds

Human–object interactions in open environments are common in the real world. Estimating 3D human pose from data where objects occlude the human is a challenging task in biometrics. However, existing LiDAR-based human motion capture datasets lack occlusion scenarios between humans and objects. To overcome this limitation, we propose LiDARHuman51M, a new human–object interaction dataset captured by LiDAR in long-range outdoor scene. It includes human motion labels acquired by an IMU system and synchronous RGB images. Additionally, we present an occlusion-aware method, LiDARCapV2, for capturing human motion from LiDAR point clouds under human–object interaction settings. Our key insight is to overcome object interference in human feature extraction by introducing a module called AgNoise-Segment. A noise augmentation strategy introduced in the AgNoise-Segment module alleviates the dependency of the segmentation accuracy on the effectiveness of 3D human pose estimations. Furthermore, we propose a skeleton extraction module that integrates features learned from the AgNoise-Segment module and predicts the skeleton locations. Quantitative and qualitative experiments demonstrate that LiDARCapV2 can capture high-quality 3D human motion under human–object interaction settings. Experiments on the KITTI and Waymo datasets demonstrate that our method can be generalized to real-world open scenarios.

Tacking over-smoothing: Target-guide progressive dynamic graph learning for 3D skeleton-based human motion prediction

Graph Convolution Network-based (GCN-based) approaches show promising performance on 3D skeleton-based human motion prediction due to its natural graph representation and outstanding ability for spatial–temporal dependencies modeling for human motion. However, the existing GCN-based methods inherently have difficulties in designing deep GCN-based structures for human motion prediction due to the over-smoothing issue. Although the over-smoothing issue has been studied in many scenarios, few attempts focus on this problem in 3D human motion prediction tasks. Therefore, we propose a novel deep GCN architecture termed TPDGN (Target-guide Progressive Dynamic Graph Network) to solve this issue in a motion prediction context. The core of the proposed TPDGN is the progressive dynamic graph leaning block, which is developed to model the dynamic graph pattern. We claim that the key to solving the over-smoothing issue is to reduce the loss of motion features and boost graph representations. Accordingly, we further explore the progressive learning mode by the specific target guidance for the motion prediction task, which aims at learning rich motion dependencies to tackle over-smoothing. Empirical results show that our approach could improve the over-smoothing issue, achieving state-of-the-art results on three public datasets. The codes is available at https://github.com/1111szu/TPDGN.

Feasibility of continuous switching 3D printing on surimi

Continuous switching 3D printing that allows multiple materials printed in a single nozzle was introduced to verify its applicability to the food system, which aims to improve the switching ability and manufacturing precision of multi-material 3D printed food. By integrating a self-developed 3D printing platform with logical adjustments of extrusion pressure and 3D motion, the feasible continuous switching of surimi was achieved. Moreover, employing a dual-channel continuous switching extrusion nozzle, the study elucidated the influence of surimi rheological properties on the interface behavior between pastes during extrusion. Through the analysis of fluid dynamics behavior of channel flow and utilization of simulation technology, a channel extension solution was formulated. Extending the length of the non-extrusion channel to 15 mm effectively prevented intrusion over 4 mm at an extrusion speed of 40 mm/s. This study managed to repetitively switch between two kinds of surimi pastes within a 2 mm unit length. Meanwhile, three types of 3D printing modes were established based on the control of Gcode, and the corresponding model offset correction enabled precise deposition of 2 mm × 1 mm units. The research also succeeded in 3D printing complex structures, such as meat-fat structure of beef based on surimi.

The clinical utility of the prone hip extension test in the diagnosis of motor control impairments associated with low back pain: A cross-sectional study using motion capture and electromyography

BackgroundThe prone hip extension test is used as a clinical tool to diagnose specific motor control impairments that have been identified in individuals with chronic low back pain. However, conventional protocols for performing the test are subjective and lack evidence for their effectiveness. The objective of the current study was to quantify lumbopelvic motion and muscle activation during this test and identify which motor control patterns best distinguish individuals with low back pain from asymptomatic controls. Methods18 individuals with sub-acute or chronic low back pain and 32 asymptomatic controls performed the prone hip extension test while a 3D motion capture system measured lumbar and pelvic movement patterns and an electromyography system measured the muscle activation patterns of the paraspinal, gluteus maximus, and hamstring muscles. A three-stage statistical analysis was performed, the final stage being a stepwise logistic regression analysis aimed at identifying the movement and muscle activation pattern variables that best distinguished the two groups. FindingsThe final regression model included three lumbar kinematic variables and several electromyographic amplitude variables for the gluteus maximus and hamstring muscles during right-sided prone hip extension. The final model correctly classified 86.7 % of the control group and 83.3 % of the low back pain group. InterpretationThe subject of asymmetrical gluteus maximus and hamstring muscle activation appears to be a potentially interesting area for future research on the utility of the prone hip extension test as a clinical tool in diagnosing motor control impairments associated with low back pain.

Audio Matters Too! Enhancing Markerless Motion Capture with Audio Signals for String Performance Capture

In this paper, we touch on the problem of markerless multi-modal human motion capture especially for string performance capture which involves inherently subtle hand-string contacts and intricate movements. To fulfill this goal, we first collect a dataset, named String Performance Dataset (SPD), featuring cello and violin performances. The dataset includes videos captured from up to 23 different views, audio signals, and detailed 3D motion annotations of the body, hands, instrument, and bow. Moreover, to acquire the detailed motion annotations, we propose an audio-guided multi-modal motion capture framework that explicitly incorporates hand-string contacts detected from the audio signals for solving detailed hand poses. This framework serves as a baseline for string performance capture in a completely markerless manner without imposing any external devices on performers, eliminating the potential of introducing distortion in such delicate movements. We argue that the movements of performers, particularly the sound-producing gestures, contain subtle information often elusive to visual methods but can be inferred and retrieved from audio cues. Consequently, we refine the vision-based motion capture results through our innovative audio-guided approach, simultaneously clarifying the contact relationship between the performer and the instrument, as deduced from the audio. We validate the proposed framework and conduct ablation studies to demonstrate its efficacy. Our results outperform current state-of-the-art vision-based algorithms, underscoring the feasibility of augmenting visual motion capture with audio modality. To the best of our knowledge, SPD is the first dataset for musical instrument performance, covering fine-grained hand motion details in a multi-modal, large-scale collection. It holds significant implications and guidance for string instrument pedagogy, animation, and virtual concerts, as well as for both musical performance analysis and generation. Our code and SPD dataset are available at https://github.com/Yitongishere/string_performance.

An agent-based method to estimate 3D cell migration trajectories from 2D measurements: Quantifying and comparing T vs CAR-T 3D cell migration

Background and objective:Immune cell migration is one of the key features that enable immune cells to find invading pathogens, control tissue damage, and eliminate primary developing tumors. Chimeric antigen receptor (CAR) T-cell therapy is a novel strategy in the battle against various cancers. It has been successful in treating hematological tumors, yet it still faces many challenges in the case of solid tumors. In this work, we evaluate the three-dimensional (3D) migration capacity of T and CAR-T cells within dense collagen-based hydrogels. Quantifying three-dimensional (3D) cell migration requires microscopy techniques that may not be readily accessible. Thus, we introduce a straightforward mathematical model designed to infer 3D trajectories of cells from two-dimensional (2D) cell trajectories. Methods:We develop a 3D agent-based model (ABM) that simulates the temporal changes in the direction of migration with an inverse transform sampling method. Then, we propose an optimization procedure to accurately orient cell migration over time to reproduce cell migration from 2D experimental cell trajectories. With this model, we simulate cell migration assays of T and CAR-T cells in microfluidic devices conducted under hydrogels with different concentrations of type I collagen and validate our 3D cell migration predictions with light-sheet microscopy. Results:Our findings indicate that CAR-T cell migration is more sensitive to collagen concentration increases than T cells, resulting in a more pronounced reduction in their invasiveness. Moreover, our computational model reveals significant differences in 3D movement patterns between T and CAR-T cells. T cells exhibit migratory behavior in 3D whereas that CAR-T cells predominantly move within the XY plane, with limited movement in the Z direction. However, upon the introduction of a CXCL12 chemical gradient, CAR-T cells present migration patterns that closely resemble those of T cells. Conclusions:This framework demonstrates that 2D projections of 3D trajectories may not accurately represent real migration patterns. Moreover, it offers a tool to estimate 3D migration patterns from 2D experimental data, which can be easily obtained with automatic quantification algorithms. This approach helps reduce the need for sophisticated and expensive microscopy equipment required in laboratories, as well as the computational burden involved in producing and analyzing 3D experimental data.

Individual and collective manipulation of multifunctional bimodal droplets in three dimensions.

Spatiotemporally controllable droplet manipulation is vital across numerous applications, particularly in miniature droplet robots known for their exceptional deformability. Despite notable advancements, current droplet control methods are predominantly limited to two-dimensional (2D) deformation and motion of an individual droplet, with minimal exploration of 3D manipulation and collective droplet behaviors. Here, we introduce a bimodal actuation strategy, merging magnetic and optical fields, for remote and programmable 3D guidance of individual ferrofluidic droplets and droplet collectives. The magnetic field induces a magnetic dipole force, prompting the formation of droplet collectives. Simultaneously, the optical field triggers isothermal changes in interfacial tension through Marangoni flows, enhancing buoyancy and facilitating 3D movements of individual and collective droplets. Moreover, these droplets can function autonomously as soft robots, capable of transporting objects. Alternatively, when combined with a hydrogel shell, they assemble into jellyfish-like robots, driven by sunlight. These findings present an efficient strategy for droplet manipulation, broadening the capabilities of droplet-based robotics.

The Role of the Combination of 3D Simulation Sequence Diagram and Video Motion Recognition Technology in Evaluating and Correcting Dancers' Dance Moves

The Role of the Combination of 3D Simulation Sequence Diagram and Video Motion Recognition Technology in Evaluating and Correcting Dancers' Dance Moves

Supporter-Type Anterior Cruciate Ligament Prevention System Based on Estimation of Knee Joint Valgus Angle Using Stretch Sensors

Anterior cruciate ligament (ACL) injuries are common in sports involving jumping and rapid direction changes, often occurring in non-contact situations. The risk of ACL injury is evaluated by knee flexion and valgus angles; a small knee flexion angle combined with a large valgus angle increases the risk. Monitoring these angles during activities can help athletes recognize their ACL injury risk and adjust their movements. Traditional 3D motion analysis, used for measuring knee angles, is costly and impractical for daily practice. This study proposes a knee supporter with stretch sensors to estimate knee flexion and valgus angles in practice settings, evaluating ACL injury risk and notifying athletes of high-risk movements. The proposed device wirelessly transmits data from three stretch sensors placed on the device to a PC and uses machine learning to estimate the knee angles. The results of the evaluation experiments, conducted with data from five healthy male and female participants in their twenties, indicate that the estimation accuracy for the knee flexion angle, achieved by a model trained using a Random Forest Regressor (RFR) with data from individuals other than the target user, resulted in a Mean Absolute Error (MAE) of 8.86 degrees. For the knee valgus angle, a model trained with the user’s own data using the RFR achieved a MAE of 0.81 degrees.

Critical Examination of Methods to Determine Tibiofemoral Kinematics and Tibial Contact Kinematics Based on Analysis of Fluoroscopic Images.

Goals of knee replacement surgery are to restore function and maximize implant longevity. To determine how well these goals are satisfied, tibial femoral kinematics and tibial contact kinematics are of interest. Tibiofemoral kinematics, which characterize function, is movement between the tibia and femur whereas tibial contact kinematics, which is relevant to implant wear, is movement of the location of contact by the femoral implant on the tibial articular surface. The purposes of this review article are to describe and critique relevant methods to guide correct implementation. For tibiofemoral kinematics, methods are categorized as those which determine (1) relative planar motions and (2) relative three-dimensional (3D) motions. Planar motions are determined by first finding anterior-posterior (A-P) positions of each femoral condyle relative to the tibia and tracking these positions during flexion. Of the lowest point (LP) and flexion facet center (FFC) methods, which are common, the lowest point method is preferred and the reasoning is explained. 3D motions are determined using the joint coordinate system (JCS) of Grood and Suntay. Previous applications of this JCS have resulted in motions which are largely in error due to "kinematic crosstalk." Requirements for minimizing kinematic crosstalk are outlined followed by an example, which demonstrates the method for identifying a JCS that minimizes kinematic crosstalk. Although kinematic crosstalk can be minimized, the need for a JCS to determine 3D motions is questionable based on anatomical constraints, which limit varus-valgus rotation and compression-distraction translation. Methods for analyzing tibial contact kinematics are summarized and validation of methods discussed.

3D Hand Motion Generation for VR Interactions Using a Haptic Data Glove

Recently, VR-based training applications have become popular and promising, as they can simulate real-world situations in a safe, repeatable, and cost-effective way. For immersive simulations, various input devices have been designed and proposed to increase the effectiveness of training. In this study, we developed a novel device that generates 3D hand motion data and provides haptic force feedback for VR interactions. The proposed device can track 3D hand positions using a combination of the global position estimation of ultrasonic sensors and the hand pose estimation of inertial sensors in real time. For haptic feedback, shape–memory alloy (SMA) actuators were designed to provide kinesthetic forces and an efficient power control without an overheat problem. Our device improves upon the shortcomings of existing commercial devices in tracking and haptic capabilities such that it can track global 3D positions and estimate hand poses in a VR space without using an external suit or tracker. For better flexibility in handling and feeling physical objects compared to exoskeleton-based devices, we introduced an SMA-based actuator to control haptic forces. Overall, our device was designed and implemented as a lighter and less bulky glove which provides comparable accuracy and performance in generating 3D hand motion data for a VR training application (i.e., the use of a fire extinguisher), as demonstrated in the experimental results.

Needle tracking in low-resolution ultrasound volumes using deep learning

PurposeClinical needle insertion into tissue, commonly assisted by 2D ultrasound imaging for real-time navigation, faces the challenge of precise needle and probe alignment to reduce out-of-plane movement. Recent studies investigate 3D ultrasound imaging together with deep learning to overcome this problem, focusing on acquiring high-resolution images to create optimal conditions for needle tip detection. However, high-resolution also requires a lot of time for image acquisition and processing, which limits the real-time capability. Therefore, we aim to maximize the US volume rate with the trade-off of low image resolution. We propose a deep learning approach to directly extract the 3D needle tip position from sparsely sampled US volumes.MethodsWe design an experimental setup with a robot inserting a needle into water and chicken liver tissue. In contrast to manual annotation, we assess the needle tip position from the known robot pose. During insertion, we acquire a large data set of low-resolution volumes using a 16 ×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes $$\\end{document} 16 element matrix transducer with a volume rate of 4 Hz. We compare the performance of our deep learning approach with conventional needle segmentation.ResultsOur experiments in water and liver show that deep learning outperforms the conventional approach while achieving sub-millimeter accuracy. We achieve mean position errors of 0.54 mm in water and 1.54 mm in liver for deep learning.ConclusionOur study underlines the strengths of deep learning to predict the 3D needle positions from low-resolution ultrasound volumes. This is an important milestone for real-time needle navigation, simplifying the alignment of needle and ultrasound probe and enabling a 3D motion analysis.

Agent-based model predicts that layered structure and 3D movement work synergistically to reduce bacterial load in 3D in vitro models of tuberculosis granuloma.

Tuberculosis (TB) remains a global public health threat. Understanding the dynamics of host-pathogen interactions within TB granulomas will assist in identifying what leads to the successful elimination of infection. In vitro TB models provide a controllable environment to study these granuloma dynamics. Previously we developed a biomimetic 3D spheroid granuloma model that controls bacteria better than a traditional monolayer culture counterpart. We used agent-based simulations to predict the mechanistic reason for this difference. Our calibrated simulations were able to predict heterogeneous bacterial dynamics that are consistent with experimental data. In one group of simulations, spheroids are found to have higher macrophage activation than their traditional counterparts, leading to better bacterial control. This higher macrophage activation in the spheroids was not due to higher counts of activated T cells, instead fewer activated T cells were able to activate more macrophages due to the proximity of these cells to each other within the spheroid. In a second group of simulations, spheroids again have more macrophage activation but also more T cell activation, specifically CD8+ T cells. This higher level of CD8+ T cell activation is predicted to be due to the proximity of these cells to the cells that activate them. Multiple mechanisms of control were predicted. Simulations removing individual mechanisms show that one group of simulations has a CD4+ T cell dominant response, while the other has a mixed/CD8+ T cell dominant response. Lastly, we demonstrated that in spheroids the initial structure and movement rules work synergistically to reduce bacterial load. These findings provide valuable insights into how the structural complexity of in vitro models impacts immune responses. Moreover, our study has implications for engineering more physiologically relevant in vitro models and advancing our understanding of TB pathogenesis and potential therapeutic interventions.

Interference-enhanced micro-vision-based single-shot imaging of five degrees-of-freedom error motions for ultra-precision rotary axes

The measurement of five degrees-of-freedom (5-DOF) error motions, including radial, axial, and tilt motions, is crucial for ultra-precision rotary axes, which are key components of ultra-precision machine tools and instrumentation. In this study, we propose an interference-enhanced micro-vision technique to concurrently derive the 5-DOF error motions from a single-shot two-dimensional image, which was captured by a standard industrial camera equipped with an interference objective lens. By consolidating the essential features into a single optical path, the interference-enhanced micro-vision technique ingeniously merges machine micro-vision and modified white-light interference to detect in-plane and out-of-plane motions. Numerical simulations demonstrated, the basic principle for deriving the 5-DOF error motions, and the magnification of objective lens had inconsistent effects on the measurement accuracy for the radial and tilt motions, i.e. higher magnification led to higher radial accuracy but lower tilt accuracy. As practical application, the error motion detection capability was demonstrated by simultaneously measuring the 5-DOF synchronous and asynchronous error motions for a typical air bearing spindle at rotation speeds of 8.33, 108.33, and 308.33 rpm. The synchronous errors were nearly identical at various spindle speeds. However, because of system dynamics, increased vibrations were observed to be superimposed on the basic tilt error motions as the spindle speeds increased, which were verified by the vibration marks imprinted on the turned surfaces. For the 5-DOF motion measurements, the least-square fitting using large-volume edge and greyscale data of the captured image enabled super-high resolutions, despite using a camera with a relatively large pixel size and low bit depth. These results demonstrate that the proposed interference-enhanced micro-vision technique is a simple and effective tool for measuring spatial error motions in ultra-precision rotary axes.

Influence of Internal and External Foot Rotation on Peak Knee Adduction Moments and Ankle Moments during Gait in Individuals with Knee Osteoarthritis: A Cross-Sectional Study.

The aim of the study was to verify the effects of foot progression angle (FPA) modification during walking on the internal moments of the ankle and knee joints in individuals with knee osteoarthritis (OA). Biomechanical changes such as increased knee adduction moment (KAM) during walking are known to be involved in the development and severity of knee OA. Although various FPA modifications during gait have been applied to reduce peak KAM, few studies have investigated the effects of applying toe-in or toe-out walking modifications for knee OA on peak KAM and three-dimensional (3D) moments of the ankle joint. Kinetic moment variables were acquired from 35 individuals with medial knee compartment OA. A 3D motion analysis system and two force platforms were used to acquire KAM and 3D moments of both ankle joints during gait. Visual3D was used to obtain final moment data for statistical processing. Repeated-measures analysis of variance with Bonferroni adjustment was used to compare kinetic and kinematic values for each FPA walking condition. There was a significant decrease (p < 0.01) in first peak KAM when walking with an internal rotation foot position compared to normal foot position walking. Also, there was a significant decrease (p < 0.01) in second peak KAM when walking with an external rotation foot position compared to normal foot position walking. Compared to a normal foot position, peak ankle inversion moment of the external rotation foot position walking showed a significant decrease (p < 0.05). There were no interactive effects between FPA condition and limb sides for any KAM values (p > 0.05). The results showed no significant increase in the ankle joint moment value during gait for FPA modification conditions. Thus, the clinical implications of this study suggest that modification of the FPA in patients with OA to reduce KAM does not negatively impact the 3D ankle moments.

Protocol to record and analyze primate leaping in three dimensions in the wild.

Several studies comparing primate locomotion under lab versus field conditions have shown the importance of implementing both types of studies, as each has their advantages and disadvantages. However, three-dimensional (3D) motion capture of primates has been challenging under natural conditions. In this study, we provide a detailed protocol on how to collect 3D biomechanical data on primate leaping in their natural habitat that can be widely implemented. To record primate locomotion in the dense forest we use modified GoPro Hero Black cameras with zoom lenses that can easily be carried around and set up on tripods. We outline details on how to obtain camera calibrations at greater heights and how to process the collected data using the MATLAB camera calibration app and the motion tracking software DLTdv8a. We further developed a new MATLAB application "WildLeap3D" to generate biomechanical performance metrics from the derived x, y, z coordinates of the leaps. We provide details on how to collect data on support diameter, compliance, and orientation, and combine these with the jumps to study locomotor performance in an ecological context. We successfully reconstructed leaps of wild primates in the 3D space under natural conditions and provided data on four representative leaps. We provide exemplar data on primate velocity and acceleration during a leap and show how our protocol can be used to analyze segmental kinematics. This study will help to make motion capture of freely moving animals more accessible and help further our knowledge about animal locomotion and movement.