Exploring the role of task on kinematic variability and assessing consistency in individual responses across repetitive manual tasks.

To gain a greater understanding of motor variability (MV) as an individual trait, the effect of task type on MV and individual consistency in MV across three tasks was investigated. Twenty participants performed repetitive carrying, lifting, and simulated sawing tasks. MV was assessed using the linear measure of mean point-by-point standard deviation in three-dimensional upper body joint angles. Task type affected MV, where carrying showed higher MV compared to sawing (23-29%) and lifting (12-19%). Furthermore, MV was higher in lifting compared to sawing (12-25%). Poor to moderate individual consistency (ICC = 0.42-0.63) was found across tasks. Task type determined MV and only some support for MV as an individual trait across tasks was found. Based on this work, differences in degrees of freedom afforded by the task influence the opportunity to exploit MV, and possibly individual consistency in MV magnitude is specific to the degrees of freedom afforded by the task. Practitioner summary: In repetitive tasks, movement variability has been proposed as an individual characteristic independent of task characteristics, where repeaters show consistently low variability, while replacers show consistently high variability. In the current study, only moderate support was demonstrated for variability as a consistent individual characteristic across different manual tasks.AbbreviationMV: Motor variability; WRMSDs: Work-related musculoskeletal disorders; DOF: Degrees of freedom; meanSD: Mean standard deviation; SD: Standard deviation; H: Handle (of simulated sawing setup); T: Track (of simulated sawing setup); F: Frame (of simulated sawing setup); ICC: Intraclass correlation; UE: Upper extremity; MMH: Manual material handling; EMG: Electromyography.

Comparison of machine learning classifiers for differentiating level and sport using movement data.

The purposes of this study were to determine if 1) recurrent neural networks designed for multivariate, time-series analyses outperform traditional linear and non-linear machine learning classifiers when classifying athletes based on competition level and sport played, and 2) athletes of different sports move differently during non-sport-specific movement screens. Optical-based kinematic data from 542 athletes were used as input data for nine different machine learning algorithms to classify athletes based on competition level and sport played. For the traditional machine learning classifiers, principal component analysis and feature selection were used to reduce the data dimensionality and to determine the best principal components to retain. Across tasks, recurrent neural networks and linear machine learning classifiers tended to outperform the non-linear machine learning classifiers. For all tasks, reservoir computing took the least amount of time to train. Across tasks, reservoir computing had one of the highest classification rates and took the least amount of time to train; however, interpreting the results is more difficult compared to linear classifiers. In addition, athletes were successfully classified based on sport suggesting that athletes competing in different sports move differently during non-sport specific movements. Therefore, movement assessment screens should incorporate sport-specific scoring criteria.

Lumbar spine loads are reduced for activities of daily living when using a braced arm-to-thigh technique.

PURPOSE: To evaluate the effect of the braced arm-to-thigh technique (BATT) (versus self-selected techniques) on three-dimensional trunk kinematics and spinal loads for three common activities of daily living (ADLs) simulated in the laboratory: weeding (gardening), reaching for an object in a low cupboard, and car egress using the two-legs out technique. METHODS: Ten young healthy males performed each task using a self-selected technique, and then using the BATT. The pulling action of weeding was simulated using a magnet placed on a steel plate. Cupboard and car egress tasks were simulated using custom apparatus representing the dimensions of a kitchen cabinet and a medium-sized Australian car, respectively. Three-dimensional trunk kinematics and L4/L5 spinal loads were estimated using the Lifting Full-Body OpenSim model and compared between techniques. Paired t-tests were used to compare peak values between methods (self-selected vs BATT). RESULTS: The BATT significantly reduced peak extension moments (13-51%), and both compression (27-45%) and shear forces (31-62%) at L4/L5, compared to self-selected techniques for all three tasks (p < 0.05). Lateral bending angles increased with the BATT for weeding and cupboard tasks, but these changes were expected as the BATT inherently introduces asymmetric trunk motion. CONCLUSION: The BATT substantially reduced L4/L5 extension moments, and L4/L5 compression and shear forces, compared to self-selected methods, for three ADLs, in a small cohort of ten young healthy males without prior history of back pain. These study findings can be used to inform safe procedures for these three ADLs, as the results are considered representative of a mature population.

Feature Detection and Biomechanical Analysis to Objectively Identify High Exposure Movement Strategies When Performing the EPIC Lift Capacity test.

Purpose The Epic Lift Capacity (ELC) test is used to determine a worker's maximum lifting capacity. In the ELC test, maximum lifting capacity is often determined as the maximum weight lifted without exhibiting a visually appraised "high-risk workstyle." However, the criteria for evaluating lifting mechanics have limited justification. This study applies feature detection and biomechanical analysis to motion capture data obtained while participants performed the ELC test to objectively identify aspects of movement that may help define "high-risk workstyle". Method In this cross-sectional study, 24 participants completed the ELC test. We applied Principal Component Analysis, as a feature detection approach, and biomechanical analysis to motion capture data to objectively identify movement features related to biomechanical exposure on the low back and shoulders. Principal component scores were compared between high and low exposure trials (relative to median exposure) to determine if features of movement differed. Features were interpreted using single component reconstructions of principal components. Results Statistical testing showed that low exposure lifts and lowers maintained the body closer to the load, exhibited squat-like movement (greater knee flexion, wider base of support), and remained closer to neutral posture at the low back (less forward flexion and axial twist) and shoulder (less flexion and abduction). Conclusions Use of feature detection and biomechanical analyses revealed movement features related to biomechanical exposure at the low back and shoulders. The objectively identified criteria could augment the existing scoring criteria for ELC test technique assessment. In the future, such features can inform the design of classifiers to objectively identify "high-risk workstyle" in real-time.

Validity and Sensitivity of an Inertial Measurement Unit-Driven Biomechanical Model of Motor Variability for Gait.

Motor variability in gait is frequently linked to fall risk, yet field-based biomechanical joint evaluations are scarce. We evaluated the validity and sensitivity of an inertial measurement unit (IMU)-driven biomechanical model of joint angle variability for gait. Fourteen healthy young adults completed seven-minute trials of treadmill gait at several speeds and arm swing amplitudes. Trunk, pelvis, and lower-limb joint kinematics were estimated by IMU- and optoelectronic-based models using OpenSim. We calculated range of motion (ROM), magnitude of variability (meanSD), local dynamic stability (λmax), persistence of ROM fluctuations (DFAα), and regularity (SaEn) of each angle over 200 continuous strides, and evaluated model accuracy (RMSD: root mean square difference), consistency (ICC2,1: intraclass correlation), biases, limits of agreement, and sensitivity to within-participant gait responses (effects of speed and swing). RMSDs of joint angles were 1.7-9.2° (pooled mean of 4.8°), excluding ankle inversion. ICCs were mostly good to excellent in the primary plane of motion for ROM and in all planes for meanSD and λmax, but were poor to moderate for DFAα and SaEn. Modelled speed and swing responses for ROM, meanSD, and λmax were similar. Results suggest that the IMU-driven model is valid and sensitive for field-based assessments of joint angle time series, ROM in the primary plane of motion, magnitude of variability, and local dynamic stability.

Validation of an IMU Suit for Military-Based Tasks.

Investigating the effects of load carriage on military soldiers using optical motion capture is challenging. However, inertial measurement units (IMUs) provide a promising alternative. Our purpose was to compare optical motion capture with an Xsens IMU system in terms of movement reconstruction using principal component analysis (PCA) using correlation coefficients and joint kinematics using root mean squared error (RMSE). Eighteen civilians performed military-type movements while their motion was recorded using both optical and IMU-based systems. Tasks included walking, running, and transitioning between running, kneeling, and prone positions. PCA was applied to both the optical and virtual IMU markers, and the correlations between the principal component (PC) scores were assessed. Full-body joint angles were calculated and compared using RMSE between optical markers, IMU data, and virtual markers generated from IMU data with and without coordinate system alignment. There was good agreement in movement reconstruction using PCA; the average correlation coefficient was 0.81 ± 0.14. RMSE values between the optical markers and IMU data for flexion-extension were less than 9°, and 15° for the lower and upper limbs, respectively, across all tasks. The underlying biomechanical model and associated coordinate systems appear to influence RMSE values the most. The IMU system appears appropriate for capturing and reconstructing full-body motion variability for military-based movements.

A Subject-Specific Approach to Detect Fatigue-Related Changes in Spine Motion Using Wearable Sensors.

An objective method to detect muscle fatigue-related kinematic changes may reduce workplace injuries. However, heterogeneous responses to muscle fatigue suggest that subject-specific analyses are necessary. The objectives of this study were to: (1) determine if wearable inertial measurement units (IMUs) could be used in conjunction with a spine motion composite index (SMCI) to quantify subject-specific changes in spine kinematics during a repetitive spine flexion-extension (FE) task; and (2) determine if the SMCI was correlated with measures of global trunk muscle fatigue. Spine kinematics were measured using wearable IMUs in 10 healthy adults during a baseline set followed by 10 sets of 50 spine FE repetitions. After each set, two fatigue measures were collected: perceived level of fatigue using a visual analogue scale (VAS), and maximal lift strength. SMCIs incorporating 10 kinematic variables from 2 IMUs (pelvis and T8 vertebrae) were calculated and used to quantify subject-specific changes in movement. A main effect of set was observed (F (1.7, 15.32) = 10.42, p = 0.002), where the SMCI became significantly greater than set 1 starting at set 4. Significant correlations were observed between the SMCI and both fatigue VAS and maximal lift strength at the individual and study level. These findings support the use of wearable IMUs to detect subject-specific changes in spine motion associated with muscle fatigue.

Biomechanical Analysis of Running Foot Strike in Shoes of Different Mass.

Different shoes and strike patterns produce different biomechanical characteristics that can affect injury risk. Running shoes are mainly designed as lightweight, minimal, or traditional cushioned types. Previous research on different shoes utilized shoes of not only different mass but also different shoe structures. However, it is unclear whether biomechanical changes during running in different shoe types with differing mass are the result of the structural design or the mass of the shoe. Thus, the purpose of this study was to investigate the effect of shoes of different mass on running gait biomechanics. Twenty male runners participated in this study. The experimental shoe masses used in this study were 175, 255, 335 and 415 g. The peak vertical ground reaction force increased with shoe mass (p < 0.05), but the strike index, ankle plantarflexion at initial contact, peak moment of the ankle during the stance phase, and initial contact angles of the lower extremity joints did not change. During the pre-activation phase, the integrated EMG data showed that the tibialis anterior muscle was the most activated with the 175 g and 415 g shoes (p < 0.05). During the push-off phase, the semitendinosus, lateral gastrocnemius and soleus muscles displayed higher activation with the heavier shoes (p < 0.05). The center of pressure also moves forward; resulting in mid foot striking. The lightest shoes might increase gastrocnemius muscle fatigue during the braking phase. The heaviest shoes could cause semitendinosus and triceps surae muscle fatigue during the push-off phase. Therefore, runners should consider their lower extremity joints, muscle adaptation and cushioning to remain in their preferred movement path.

The effects of weighted skates on ice-skating kinematics, kinetics and muscular activity.

Sport-specific resistance training, through limb loading, can be a complimentary training method to traditional resistance training by loading the working muscles during all phases of a specific movement. The purpose of this study was to examine the acute effects of skating with an additional load on the skate, using a skate weight prototype, on kinematics, kinetics, and muscle activation during the acceleration phase while skating on a synthetic ice surface. 10 male hockey skaters accelerated from rest (standing erect with knees slightly bent) under four non-randomized load conditions: baseline 1 (no weight), light (0.9 kg per skate), heavy (1.8 kg per skate), and baseline 2 (no weight). Skating with additional weight caused athletes to skate slower (p < 0.001; η2 = 0.551), and led to few changes in kinematics: hip sagittal range of motion (ROM) decreased (2.2°; p = 0.032; η2 = 0.274), hip transverse ROM decreased (3.4°; p < 0.001; η2 = 0.494), ankle sagittal ROM decreased (2.3°; p = 0.022; η2 = 0.295), and knee sagittal ROM increased (7.8°; p < 0.001, η2 = 0.761). Overall, weighted skates decreased skating velocity, but athletes maintained similar muscle activation profiles (magnitude and trends) with minor changes to their skating kinematics.

The acute effects of targeted abdominal muscle activation training on spine stability and neuromuscular control.

BACKGROUND: Targeted activation of the transversus abdominis (TrA) muscle through the abdominal drawing-in maneuver (ADIM) is a frequently prescribed exercise for the prevention and rehabilitation of low back pain. However, there is still debate over the role the ADIM plays in maintaining a stable spine during movement. Thus, a single cohort pre/post-intervention protocol was used to examine whether 5 min of ADIM training prior to a dynamic movement task alters dynamic spine stability and control. METHODS: Thirteen healthy participants performed a repetitive spine flexion task twice, once before and once after they received biofeedback training on how to correctly perform the ADIM in standing. Abdominal and back muscle activation (indwelling and surface electromyography, EMG) and 3D kinematic data were recorded during all trials. EMG activation (percent maximum) and local dynamic stability of spine movement [maximum finite-time Lyapunov exponent (λmax)] were compared before and after the training using Friedman's rank test and repeated-measures ANOVA, respectively. To assess the moderating effects of absolute changes in EMG (∆EMG) of each muscle after training on changes in stability, the ∆EMG (peak and mean) were added to the ANOVA as separate covariates (ANCOVA). RESULTS: Following ADIM training, there were greater peak and mean levels of activation in all tested abdominal muscles, including TrA, (p < 0.05), but not in the back muscles. The ANOVA showed no significant change in λmax following training (p = 0.633). However, after considering the moderating effects of the ∆EMG seen in each muscle with training, it was found that only changes in TrA EMG significantly influenced stability. The ANCOVA revealed a significant main effect of training on stability as well as a significant interaction effect between training and ∆EMG recorded from TrA (p < 0.05); those with larger increases in TrA activation demonstrated larger improvements in stability. CONCLUSION: As a group, 5 min of ADIM training did not change spine stability during dynamic movement. However, those who were most successful in improving TrA activation with a 5-min ADIM training session showed the greatest improvements in local dynamic spine stability after training. As such, dynamic spine stability in some individuals may benefit from ADIM training.

Evaluating the Relationship Between Muscle Activation and Spine Kinematics Through Wavelet Coherence.

Advances in time-frequency analysis can provide new insights into the important, yet complex relationship between muscle activation (ie, electromyography [EMG]) and motion during dynamic tasks. We use wavelet coherence to compare a fundamental cyclical movement (lumbar spine flexion and extension) to the surface EMG linear envelope of 2 trunk muscles (lumbar erector spinae and internal oblique). Both muscles cohere to the spine kinematics at the main cyclic frequency, but lumbar erector spinae exhibits significantly greater coherence than internal oblique to kinematics at 0.25, 0.5, and 1.0 Hz. Coherence phase plots of the 2 muscles exhibit different characteristics. The lumbar erector spinae precedes trunk extension at 0.25 Hz, whereas internal oblique is in phase with spine kinematics. These differences may be due to their proposed contrasting functions as a primary spine mover (lumbar erector spinae) versus a spine stabilizer (internal oblique). We believe that this method will be useful in evaluating how a variety of factors (eg, pain, dysfunction, pathology, fatigue) affect the relationship between muscles' motor inputs (ie, activation measured using EMG) and outputs (ie, the resulting joint motion patterns).

A dynamical systems analysis of assisted and unassisted anterior and posterior hand-held load carriage.

Load carriage is recognised as a primary occupational factor leading to slip and fall injuries, and therefore assessing balance maintenance during such tasks is critical in assessing injury risk. Ten males completed 55 strides under five carriage conditions: (1) unassisted anterior, (2) unassisted posterior, (3) assisted anterior, (4) assisted posterior and (5) unloaded gait (UG). Kinematic data were recorded from markers affixed to landmarks on the right side of each participant, in order to calculate segment angles for the foot, shank, thigh and pelvis. Continuous relative phase (CRP) variability was calculated for each segment pair and local dynamic stability was calculated for each segment in all three movement planes. In general, irrespective of the assistive device or movement plane, anterior load carriage was most stable (lower CRP variability and maximum finite-time Lyapunov exponents). Moreover, load carriage was less dynamically stable than UG, displaying the importance of objectively investigating safe load carriage practices. PRACTITIONER SUMMARY: Dynamical systems analyses were used to comprehensively evaluate the stability of various handheld load carriage methods. In general, anterior load carriage was significantly more stable than posterior load carriage,Mover's assistive device had small but beneficial effects on stability, and load carriage was less stable than UG.

Local dynamic stability of spine muscle activation and stiffness patterns during repetitive lifting.

To facilitate stable trunk kinematics, humans must generate appropriate motor patterns to effectively control muscle force and stiffness and respond to biomechanical perturbations and/or neuromuscular control errors. Thus, it is important to understand physiological variables such as muscle force and stiffness, and how these relate to the downstream production of stable spine and trunk movements. This study was designed to assess the local dynamic stability of spine muscle activation and rotational stiffness patterns using Lyapunov analyses, and relationships to the local dynamic stability of resulting spine kinematics, during repetitive lifting and lowering at varying combinations of lifting load and rate. With an increase in the load lifted at a constant rate there was a trend for decreased local dynamic stability of spine muscle activations and the muscular contributions to spine rotational stiffness; although the only significant change was for the full state space muscle activation stability (p < 0.05). With an increase in lifting rate with a constant load there was a significant decrease in the local dynamic stability of spine muscle activations and the muscular contributions to spine rotational stiffness (p ≤ 0.001 for all measures). These novel findings suggest that the stability of motor inputs and the muscular contributions to spine rotational stiffness can be altered by external task demands (load and lifting rate), and therefore are important variables to consider when assessing the stability of the resulting kinematics.

An Enhanced Spine Model Validated for Simulating Dynamic Lifting Tasks in OpenSim.

A fully articulated thoracolumbar spine model had been previously developed in OpenSim and had been extensively validated against experimental data during various static tasks. In the present study, we enhanced this detailed musculoskeletal model by adding the role of passive structures and adding kinematic constraints to make it suitable for dynamic tasks. We validated the spinal forces estimated by this enhanced model during nine dynamic lifting/lowering tasks. Moreover, we recently developed and evaluated five approaches in OpenSim to model the external loads applied to the hands during lifting/lowering tasks, and in the present study, we assessed which approach results in more accurate spinal forces. Regardless of the external load modeling approach, the maximum forces predicted by our enhanced spine model across all tasks, as well as the pattern of estimated spinal forces within each task, showed strong correlations (r-values and cross-correlation coefficients > 0.9) with experimental data. Given the biofidelity of our enhanced model, its accessibility via the open-source OpenSim software, and the extent to which this model has been validated, we recommend it for applications requiring estimation of spinal forces during lifting/lowering tasks using multibody-based models and inverse dynamic analyses.

Investigating concurrent validity of inertial sensors to evaluate multiplanar spine movement.

Inertial measurement units (IMUs) offer a portable and inexpensive alternative to traditional optical motion capture systems, and have potential to support clinical diagnosis and treatment of low back pain; however, due to a lack of confidence regarding the validity of IMU-derived metrics, their uptake and acceptance remain a challenge. The objective of this work was to assess the concurrent validity of the Xsens DOT IMUs for tracking multiplanar spine movement, and to evaluate concurrent validity and reliability for estimating clinically relevant metrics relative to gold-standard optical motion capture equipment. Ten healthy controls performed spine range of motion (ROM) tasks, while data were simultaneously tracked from IMUs and optical marker clusters placed over the C7, T12, and S1 vertebrae. Root mean square error (RMSE), mean absolute error (MAE), and intraclass correlation coefficients (ICC2,1) were calculated to assess validity and reliability of absolute (abs; C7, T12, and S1 sensors) and relative joint (rel; intersegmental thoracic, lumbar, and total) motion. Overall RMSEabs = 1.33°, MAEabs = 0.74° ± 0.69, and ICC2,1,abs = 0.953 across all movements, sensors, and planes. Results were slightly better for uniplanar movements when evaluating the primary rotation axis (prim) absolute ROM (MAEabs,prim = 0.56° ± 0.49; ICC2,1,abs,prim = 0.999). Similarly, when evaluating relative intersegmental motion, overall RMSErel = 2.39°, MAErel = 1.10° ± 0.96, and ICC2,1,rel = 0.950, and relative primary rotation axis achieved MAErel,prim = 0.87° ± 0.77, and ICC2,1,rel,prim = 0.994. Findings from this study suggest that these IMUs can be considered valid for tracking multiplanar spine movement, and may be used to objectively assess spine movement and neuromuscular control in clinics.

Dynamic assessment of spine movement patterns using an RGB-D camera and deep learning.

In clinical practice, functional limitations in patients with low back pain are subjectively assessed, potentially leading to misdiagnosis and prolonged pain. This paper proposes an objective deep learning (DL) markerless motion capture system that uses a red-green-blue-depth (RGB-D) camera to measure the kinematics of the spine during flexion-extension (FE) through: 1) the development and validation of a DL semantic segmentation algorithm that segments the back into four anatomical classes and 2) the development and validation of a framework that uses these segmentations to measure spine kinematics during FE. Twenty participants performed ten cycles of FE with drawn-on point markers while being recorded with an RGB-D camera. Five of these participants also performed an additional trial where they were recorded with an optical motion capture (OPT) system. The DL algorithm was trained to segment the back and pelvis into four anatomical classes: upper back, lower back, spine, and pelvis. A kinematic framework was then developed to refine these segmentations into upper spine, lower spine, and pelvis masks, which were used to measure spine kinematics after obtaining 3D global coordinates of the mask corners. The segmentation algorithm achieved high accuracy, and the root mean square error (RMSE) between ground truth and predicted lumbar kinematics was < 4°. When comparing markerless and OPT kinematics, RMSE values were < 6°. This work demonstrates the feasibility of using markerless motion capture to assess FE spine movement in clinical settings. Future work will expand the studied movement directions and test on different demographics.

Three-dimensional knee joint moments during performance of the bodyweight squat: effects of stance width and foot rotation.

The purpose of this investigation was to assess the effects of stance width and foot rotation angle on three-dimensional knee joint moments during bodyweight squat performance. Twenty-eight participants performed 8 repetitions in 4 conditions differing in stance or foot rotation positions. Knee joint moment waveforms were subjected to principal component analysis. Results indicated that increasing stance width resulted in a larger knee flexion moment magnitude, as well as larger and phase-shifted adduction moment waveforms. The knee's internal rotation moment magnitude was significantly reduced with external foot rotation only under the wide stance condition. Moreover, squat performance with a wide stance and externally rotated feet resulted in a flattening of the internal rotation moment waveform during the middle portion of the movement. However, it is speculated that the differences observed across conditions are not of clinical relevance for young, healthy participants.

Interjoint coordination and the personal lift-assist device.

It has been suggested that interjoint coordination may serve to reduce joint stress and muscular demand and to maintain balance during dynamic lifting tasks, thus having implications for safe lifting practices. Before recommending the use of an on-body ergonomic aid, the Personal Lift-Assist Device (PLAD), it is important to determine any effects this device may have on interjoint coordination. Principal component analyses were applied to relative phase angle waveforms, defining the hip-knee and lumbar spine-hip coordination of 15 males and 15 females during a repetitive lifting task. When wearing the PLAD, users lifted with more synchronous hip-knee and lumbar spine-hip coordination patterns (P < .01). Furthermore, increases in load caused less synchronized interjoint coordination at both the hip-knee and lumbar spine-hip during the up and down phases of the lift (P < .01) for all conditions. No significant main effects of sex or significant interactions were observed on any of the outcome variables.

Evaluation of spinal force normalization techniques.

Division normalization is commonly used in biomechanics studies to remove the effect of anthropometric differences (e.g., body weight) on kinetic variables, facilitating comparison across a population. In spine biomechanics, spinal forces are commonly divided by the body weight or the intervertebral load during a standing posture. However, it has been suggested that offset and power curve normalization are more appropriate than division normalization for normalizing kinetic variables such as ground reaction forces during walking and running. The present study investigated, for the first time, the effectiveness of four techniques for normalizing spinal forces to remove the effect of body weight. Spinal forces at all lumbar levels were estimated using a detailed OpenSim musculoskeletal model of the spine for 11 scaled models (50-100 kg) and during 13 trunk flexion tasks. Pearson correlations of raw and normalized forces against body weight were used to assess the effectiveness of each normalization technique. Body weight and standing division normalization could only successfully normalize L4L5 spinal forces in three tasks, and L5S1 loads in five and three tasks, respectively; however, offset and power curve normalization techniques were successful across all lumbar spine levels and all tasks. Offset normalization successfully removed the effect of body weight and maintained the influence of flexion angle on spinal forces. Thus, we recommend offset normalization to account for anthropometric differences in studies of spinal forces.

Three-Dimensional Motion Capture Data of a Movement Screen from 183 Athletes.

Movement screens are widely used to identify aberrant movement patterns in hopes of decreasing risk of injury, identifying talent, and/or improving performance. Motion capture data can provide quantitative, objective feedback regarding movement patterns. The dataset contains three-dimensional (3D) motion capture data of 183 athletes performing mobility tests (ankle, back bend, crossover adduction, crossover rotation, elbows, head, hip turn, scorpion, shoulder abduction, shoulder azimuth, shoulder rotation, side bends, side lunges and trunk rotation) and stability tests (drop jump, hop down, L-cut, lunge, rotary stability, step down and T-balance) bilaterally (where applicable), the athletes' injury history, and demographics. All data were collected at 120 Hz or 480 Hz using an 8-camera Raptor-E motion capture system with 45 passive reflective markers. A total of 5,493 trials were pre-processed and included in .c3d and .mat formats. This dataset will enable researchers and end users to explore movement patterns of athletes of varying demographics from different sports and competition levels; develop objective movement assessment tools; and gain new insights into the relationships between movement patterns and injury.